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100 |
Multi-agent Architecture Search via Agentic Supernet
|
https://openreview.net/forum?id=imcyVlzpXh
|
[
"Guibin Zhang",
"Luyang Niu",
"Junfeng Fang",
"Kun Wang",
"LEI BAI",
"Xiang Wang"
] |
Oral
|
Large Language Model (LLM)-empowered multi-agent systems extend the cognitive boundaries of individual agents through disciplined collaboration and interaction, while constructing these systems often requires labor-intensive manual designs. Despite the availability of methods to automate the design of agentic workflows, they typically seek to identify a static, complex, one-size-fits-all system, which, however, fails to dynamically allocate inference resources based on the difficulty and domain of each query. To address this challenge, we shift away from the pursuit of a monolithic agentic system, instead optimizing the \textbf{agentic supernet}, a probabilistic and continuous distribution of agentic architectures. We introduce \textbf{MaAS}, an automated framework that samples query-dependent agentic systems from the supernet, delivering high-quality solutions and tailored resource allocation (\textit{e.g.}, LLM calls, tool calls, token cost). Comprehensive evaluation across six benchmarks demonstrates that MaAS \textbf{(I)} requires only $6\\sim45\\%$ of the inference costs of existing handcrafted or automated multi-agent systems, \textbf{(II)} surpasses them by $0.54\\%\sim11.82\\%$, and \textbf{(III)} enjoys superior cross-dataset and cross-LLM-backbone transferability.
|
LLM-agent, Agentic Workflows, Multi-agent System
| null | 1,274 |
2502.04180
|
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] |
https://github.com/bingreeky/MaAS
|
101 |
VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models
|
https://openreview.net/forum?id=yMJcHWcb2Z
|
[
"Hila Chefer",
"Uriel Singer",
"Amit Zohar",
"Yuval Kirstain",
"Adam Polyak",
"Yaniv Taigman",
"Lior Wolf",
"Shelly Sheynin"
] |
Oral
|
Despite tremendous recent progress, generative video models still struggle to capture real-world motion, dynamics, and physics. We show that this limitation arises from the conventional pixel reconstruction objective, which biases models toward appearance fidelity at the expense of motion coherence.
To address this, we introduce **VideoJAM**, a novel framework that instills an effective motion prior to video generators, by encouraging the model to learn *a joint appearance-motion representation*. VideoJAM is composed of two complementary units. During training, we extend the objective to predict both the generated pixels and their corresponding motion from a single learned representation.
During inference, we introduce **Inner-Guidance**, a mechanism that steers the generation toward coherent motion by leveraging the model's own evolving motion prediction as a dynamic guidance signal.
Notably, our framework can be applied to any video model with minimal adaptations, requiring no modifications to the training data or scaling of the model.
VideoJAM achieves state-of-the-art performance in motion coherence, surpassing highly competitive proprietary models while also enhancing the perceived visual quality of the generations.
These findings emphasize that appearance and motion can be complementary and, when effectively integrated, enhance both the visual quality and the coherence of video generation.
|
Video generation, Motion understanding, Diffusion models
| null | 1,047 |
2502.02492
|
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] | null |
102 |
ReferSplat: Referring Segmentation in 3D Gaussian Splatting
|
https://openreview.net/forum?id=reuShgiHdg
|
[
"Shuting He",
"Guangquan Jie",
"Changshuo Wang",
"Yun Zhou",
"Shuming Hu",
"Guanbin Li",
"Henghui Ding"
] |
Oral
|
We introduce Referring 3D Gaussian Splatting Segmentation (R3DGS), a new task that aims to segment target objects in a 3D Gaussian scene based on natural language descriptions, which often contain spatial relationships or object attributes.
This task requires the model to identify newly described objects that may be occluded or not directly visible in a novel view,
posing a significant challenge for 3D multi-modal understanding. Developing this capability is crucial for advancing embodied AI.
To support research in this area, we construct the first R3DGS dataset, Ref-LERF.
Our analysis reveals that 3D multi-modal understanding and spatial relationship modeling are key challenges for R3DGS.
To address these challenges, we propose ReferSplat, a framework that explicitly models 3D Gaussian points with natural language expressions in a spatially aware paradigm.
ReferSplat achieves state-of-the-art performance on both the newly proposed R3DGS task and 3D open-vocabulary segmentation benchmarks.
Dataset and code are available at https://github.com/heshuting555/ReferSplat.
|
3D Gaussian Splatting, Referring Segmentation, ReferSplat
| null | 779 | null |
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] |
https://github.com/heshuting555/ReferSplat
|
103 |
STAIR: Improving Safety Alignment with Introspective Reasoning
|
https://openreview.net/forum?id=aHzPGyUhZa
|
[
"Yichi Zhang",
"Siyuan Zhang",
"Yao Huang",
"Zeyu Xia",
"Zhengwei Fang",
"Xiao Yang",
"Ranjie Duan",
"Dong Yan",
"Yinpeng Dong",
"Jun Zhu"
] |
Oral
|
Ensuring the safety and harmlessness of Large Language Models (LLMs) has become equally critical as their performance in applications. However, existing safety alignment methods typically suffer from safety-performance trade-offs and susceptibility to jailbreak attacks, primarily due to their reliance on direct refusals for malicious queries. In this paper, we propose **STAIR**, a novel framework that integrates **S**afe**T**y **A**lignment with **I**trospective **R**easoning. We enable LLMs to identify safety risks through step-by-step analysis by self-improving chain-of-thought (CoT) reasoning with safety awareness. STAIR first equips the model with a structured reasoning capability and then advances safety alignment via iterative preference optimization on step-level reasoning data generated using our newly proposed Safety-Informed Monte Carlo Tree Search (SI-MCTS). Specifically, we design a theoretically grounded reward for outcome evaluation to seek balance between helpfulness and safety. We further train a process reward model on this data to guide test-time searches for improved responses. Extensive experiments show that STAIR effectively mitigates harmful outputs while better preserving helpfulness, compared to instinctive alignment strategies. With test-time scaling, STAIR achieves a safety performance comparable to Claude-3.5 against popular jailbreak attacks. We have open-sourced our code, datasets and models at https://github.com/thu-ml/STAIR.
|
LLM, Safety Alignment, Reasoning
|
We propose the framework of STAIR, to improve safety alignment with introspective reasoning.
| 715 |
2502.02384
|
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] |
https://github.com/thu-ml/STAIR
|
104 |
From Weight-Based to State-Based Fine-Tuning: Further Memory Reduction on LoRA with Parallel Control
|
https://openreview.net/forum?id=x4qvBVuzzu
|
[
"Chi Zhang",
"REN Lianhai",
"Jingpu Cheng",
"Qianxiao Li"
] |
Oral
|
The LoRA method has achieved notable success in reducing GPU memory usage by applying low-rank updates to weight matrices. Yet, one simple question remains: can we push this reduction even further? Furthermore, is it possible to achieve this while improving performance and reducing computation time? Answering these questions requires moving beyond the conventional weight-centric approach. In this paper, we present a state-based fine-tuning framework that shifts the focus from weight adaptation to optimizing forward states, with LoRA acting as a special example. Specifically, state-based tuning introduces parameterized perturbations to the states within the computational graph, allowing us to control states across an entire residual block. A key advantage of this approach is the potential to avoid storing large intermediate states in models like transformers. Empirical results across multiple architectures—including ViT, RoBERTa, LLaMA2-7B, and LLaMA3-8B—show that our method further reduces memory consumption and computation time while simultaneously improving performance. Moreover, as a result of memory reduction, we explore the feasibility to train 7B/8B models on consumer-level GPUs like Nvidia 3090, without model quantization. The code is available at an anonymous GitHub repository
|
Low-Rank Adaptation, Parallel Control, Parameter-Efficient Fine-Tuning
|
We further reduce the memory cost of LoRA methods by introducing parallel control, which allows training 7B/8B models on GPUs like 3090 or 4090.
| 643 | null |
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] | null |
105 |
Mixture of Lookup Experts
|
https://openreview.net/forum?id=wUEp13rqXP
|
[
"Shibo Jie",
"Yehui Tang",
"Kai Han",
"Yitong Li",
"Duyu Tang",
"Zhi-Hong Deng",
"Yunhe Wang"
] |
Oral
|
Mixture-of-Experts (MoE) activates only a subset of experts during inference, allowing the model to maintain low inference FLOPs and latency even as the parameter count scales up. However, since MoE dynamically selects the experts, all the experts need to be loaded into VRAM. Their large parameter size still limits deployment, and offloading, which load experts into VRAM only when needed, significantly increase inference latency. To address this, we propose Mixture of Lookup Experts (MoLE), a new MoE architecture that is efficient in both communication and VRAM usage. In MoLE, the experts are Feed-Forward Networks (FFNs) during training, taking the output of the embedding layer as input. Before inference, these experts can be re-parameterized as lookup tables (LUTs) that retrieves expert outputs based on input ids, and offloaded to storage devices. Therefore, we do not need to perform expert computations during inference. Instead, we directly retrieve the expert's computation results based on input ids and load them into VRAM, and thus the resulting communication overhead is negligible. Experiments show that, with the same FLOPs and VRAM usage, MoLE achieves inference speeds comparable to dense models and significantly faster than MoE with experts offloading, while maintaining performance on par with MoE. Code: https://github.com/JieShibo/MoLE.
|
Mixture-of-Experts, Large Language Models
| null | 626 |
2503.15798
|
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] |
https://github.com/JieShibo/MoLE
|
106 |
AutoGFM: Automated Graph Foundation Model with Adaptive Architecture Customization
|
https://openreview.net/forum?id=fCPB0qRJT2
|
[
"Haibo Chen",
"Xin Wang",
"Zeyang Zhang",
"Haoyang Li",
"Ling Feng",
"Wenwu Zhu"
] |
Oral
|
Graph foundation models (GFMs) aim to share graph knowledge across diverse domains and tasks to boost graph machine learning.
However, existing GFMs rely on hand-designed and fixed graph neural network (GNN) architectures, failing to utilize optimal architectures *w.r.t.* specific domains and tasks, inevitably leading to suboptimal performance in diverse graph domains and tasks.
In this paper, we explore graph neural architecture search (GNAS) for GFMs for the first time, which suffers from the problem of *architecture inconsistency*, i.e., the optimal architectures for different tasks and domains vary. We tackle this problem by discovering an invariant graph-architecture relationship across domains and tasks, which imposes three challenges: i) how to capture invariant and variant patterns; ii) how to customize architectures to adapt to diverse domains and tasks; iii) how to mitigate the data domination phenomenon during the architecture search process.
To address these challenges, we propose **Auto**mated **G**raph **F**oundation **M**odel with Adaptive Architecture Customization (**AutoGFM**), providing a theoretical analysis to demonstrate the limitations of existing GNAS. Specifically, we first propose a disentangled contrastive graph encoder to learn invariant and variant patterns. Then, we design an invariant-guided architecture customization strategy to customize architectures for data from diverse domains and tasks. Finally, we propose a curriculum architecture customization mechanism to mitigate the phenomenon of particular data dominating the search process.
Extensive experiments demonstrate that **AutoGFM** outperforms baselines, achieving state-of-the-art performance.
|
Graph Foundation Model, Text Attributed Graph, Graph Neural Architecture Search, Graph Neural Networks
|
We explore the problem of graph neural architecture search for GNN-based GFMs, aiming to automatically search customized GNN architectures tailored to data with different domains and tasks.
| 579 | null |
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] | null |
107 |
VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Data
|
https://openreview.net/forum?id=l19DmXbwPK
|
[
"Thomas Zeng",
"Shuibai Zhang",
"Shutong Wu",
"Christian Classen",
"Daewon Chae",
"Ethan Ewer",
"Minjae Lee",
"Heeju Kim",
"Wonjun Kang",
"Jackson Kunde",
"Ying Fan",
"Jungtaek Kim",
"Hyung Il Koo",
"Kannan Ramchandran",
"Dimitris Papailiopoulos",
"Kangwook Lee"
] |
Oral
|
Process Reward Models (PRMs) have proven effective at enhancing mathematical reasoning for Large Language Models (LLMs) by leveraging increased inference-time computation. However, they are predominantly trained on mathematical data and their generalizability to non-mathematical domains has not been rigorously studied. In response, this work first shows that current PRMs have poor performance in other domains. To address this limitation, we introduce ***VersaPRM***, a multi-domain PRM trained on synthetic reasoning data generated using our novel data generation and annotation method. VersaPRM achieves consistent performance gains across diverse domains. For instance, in the MMLU-Pro category of Law, VersaPRM via weighted majority voting, achieves a 7.9% performance gain over the majority voting baseline–surpassing Qwen2.5-Math-PRM's gain of 1.3%. We further contribute to the community by open-sourcing all data, code and models for VersaPRM.
|
Proccess Reward Model
|
Math PRMs show limited generalizability beyond math. The fix: further train on a synthetically generated multi-domain CoT dataset.
| 409 |
2502.06737
|
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] | null |
108 |
UnHiPPO: Uncertainty-aware Initialization for State Space Models
|
https://openreview.net/forum?id=U8GUmxnzXn
|
[
"Marten Lienen",
"Abdullah Saydemir",
"Stephan Günnemann"
] |
Poster
|
State space models are emerging as a dominant model class for sequence problems with many relying on the HiPPO framework to initialize their dynamics. However, HiPPO fundamentally assumes data to be noise-free; an assumption often violated in practice. We extend the HiPPO theory with measurement noise and derive an uncertainty-aware initialization for state space model dynamics. In our analysis, we interpret HiPPO as a linear stochastic control problem where the data enters as a noise-free control signal. We then reformulate the problem so that the data become noisy outputs of a latent system and arrive at an alternative dynamics initialization that infers the posterior of this latent system from the data without increasing runtime. Our experiments show that our initialization improves the resistance of state-space models to noise both at training and inference time.
|
state space, uncertainty, hippo, mamba, kalman, noise, filter
|
HiPPO extension based on linear stochastic control theory and the Kalman filter making SSMs more robust against noise
| 16,431 |
2506.05065
|
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] | null |
109 |
When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series
|
https://openreview.net/forum?id=Dqp6IMI3gQ
|
[
"Min-Yeong Park",
"Won-Jeong Lee",
"Seong Tae Kim",
"Gyeong-Moon Park"
] |
Poster
|
Recently, forecasting future abnormal events has emerged as an important scenario to tackle realworld necessities. However, the solution of predicting specific future time points when anomalies will occur, known as Anomaly Prediction (AP), remains under-explored. Existing methods dealing with time series data fail in AP, focusing only on immediate anomalies or failing to provide precise predictions for future anomalies. To address AP, we propose a novel framework called Anomaly to Prompt (A2P), comprised of Anomaly-Aware Forecasting (AAF) and Synthetic Anomaly Prompting (SAP). To enable the forecasting model to forecast abnormal time points, we adopt a strategy to learn the relationships of anomalies. For the robust detection of anomalies, our proposed SAP introduces a learnable Anomaly Prompt Pool (APP) that simulates diverse anomaly patterns using signal-adaptive prompt. Comprehensive experiments on multiple real-world datasets demonstrate the superiority of A2P over state-of-the-art methods, showcasing its ability to predict future anomalies.
|
Time series forecasting, time series anomaly detection
|
A2P: See anomalies before they strike!
| 16,430 |
2506.23596
|
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] |
https://github.com/KU-VGI/AP
|
110 |
KGMark: A Diffusion Watermark for Knowledge Graphs
|
https://openreview.net/forum?id=GKZySvM2t9
|
[
"Hongrui Peng",
"Haolang Lu",
"Yuanlong Yu",
"WeiYe Fu",
"Kun Wang",
"Guoshun Nan"
] |
Poster
|
Knowledge graphs (KGs) are ubiquitous in numerous real-world applications, and watermarking facilitates protecting intellectual property and preventing potential harm from AI-generated content. Existing watermarking methods mainly focus on static plain text or image data, while they can hardly be applied to dynamic graphs due to spatial and temporal variations of structured data. This motivates us to propose KGMark, the first graph watermarking framework that aims to generate robust, detectable, and transparent diffusion fingerprints for dynamic KG data. Specifically, we propose a novel clustering-based alignment method to adapt the watermark to spatial variations. Meanwhile, we present a redundant embedding strategy to harden the diffusion watermark against various attacks, facilitating the robustness of the watermark to the temporal variations. Additionally, we introduce a novel learnable mask matrix to improve the transparency of diffusion fingerprints. By doing so, our KGMark properly tackles the variation challenges of structured data. Experiments on various public benchmarks show the effectiveness of our proposed KGMark.
|
Watermarking, Knowledge Graph, Diffusion Models, Generative Models
|
We present KGMark, the first watermarking method for knowledge graph embeddings that ensures high detectability, transparency, and robustness across various graph modifications.
| 16,409 |
2505.23873
|
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] |
https://github.com/phrara/kgmark
|
111 |
LSCD: Lomb--Scargle Conditioned Diffusion for Time series Imputation
|
https://openreview.net/forum?id=GdYg0Ohx0k
|
[
"Elizabeth Fons",
"Alejandro Sztrajman",
"Yousef El-Laham",
"Luciana Ferrer",
"Svitlana Vyetrenko",
"Manuela Veloso"
] |
Poster
|
Time series with missing or irregularly sampled data are a persistent challenge in machine learning. Many methods operate on the frequency-domain, relying on the Fast Fourier Transform (FFT) which assumes uniform sampling, therefore requiring prior interpolation that can distort the spectra. To address this limitation, we introduce a differentiable Lomb--Scargle layer that enables a reliable computation of the power spectrum of irregularly sampled data.
We integrate this layer into a novel score-based diffusion model (LSCD) for time series imputation conditioned on the entire signal spectrum.
Experiments on synthetic and real-world benchmarks demonstrate that our method recovers missing data more accurately than purely time-domain baselines, while simultaneously producing consistent frequency estimates. Crucially, our method can be easily integrated into learning frameworks, enabling broader adoption of spectral guidance in machine learning approaches involving incomplete or irregular data.
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time series, diffusion models, frequency spectrum
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We propose Lomb–Scargle Conditioned Diffusion (LSCD), a diffusion-based time series imputation method that leverages a differentiable Lomb–Scargle periodogram to handle irregular sampling and preserve spectral consistency
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2506.17039
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] | null |
112 |
Emoji Attack: Enhancing Jailbreak Attacks Against Judge LLM Detection
|
https://openreview.net/forum?id=Q0rKYiVEZq
|
[
"Zhipeng Wei",
"Yuqi Liu",
"N. Benjamin Erichson"
] |
Poster
|
Jailbreaking techniques trick Large Language Models (LLMs) into producing restricted output, posing a potential threat. One line of defense is to use another LLM as a Judge to evaluate the harmfulness of generated text. However, we reveal that these Judge LLMs are vulnerable to token segmentation bias, an issue that arises when delimiters alter the tokenization process, splitting words into smaller sub-tokens. This alters the embeddings of the entire sequence, reducing detection accuracy and allowing harmful content to be misclassified as safe. In this paper, we introduce Emoji Attack, a novel strategy that amplifies existing jailbreak prompts by exploiting token segmentation bias. Our method leverages in-context learning to systematically insert emojis into text before it is evaluated by a Judge LLM, inducing embedding distortions that significantly lower the likelihood of detecting unsafe content. Unlike traditional delimiters, emojis also introduce semantic ambiguity, making them particularly effective in this attack. Through experiments on state-of-the-art Judge LLMs, we demonstrate that Emoji Attack substantially reduces the unsafe prediction rate, bypassing existing safeguards.
|
LLM safety; Jailbreaking Attacks; Judge LLMs; Token Segmentation
|
We introduce Emoji Attack, an adversarial strategy that exploits token segmentation bias in Judge LLMs by inserting emojis to manipulate tokenization, enhancing the effectiveness of jailbreak attacks against Judge LLM detection.
| 16,371 |
2411.01077
|
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] |
https://github.com/zhipeng-wei/EmojiAttack
|
113 |
HiRemate: Hierarchical Approach for Efficient Re-materialization of Neural Networks
|
https://openreview.net/forum?id=rnx11J4hsg
|
[
"Julia Gusak",
"Xunyi Zhao",
"Théotime Le Hellard",
"Zhe LI",
"Lionel Eyraud-Dubois",
"Olivier Beaumont"
] |
Poster
|
Training deep neural networks (DNNs) on memory-limited GPUs is challenging, as storing intermediate activations often exceeds available memory. Re-materialization, a technique that preserves exact computations, addresses this by selectively recomputing activations instead of storing them. However, existing methods either fail to scale, lack generality, or introduce excessive execution overhead. We introduce ${\mbox{HiRemate}}$ a ${\textit hierarchical}$ re-materialization framework that recursively partitions large computation graphs, applies optimized solvers at multiple levels, and merges solutions into a global efficient training schedule. This enables scalability to significantly larger graphs than prior ILP-based methods while keeping runtime overhead low. Designed for single-GPU models and activation re-materialization, HiRemate extends the feasibility of training networks with thousands of graph nodes, surpassing prior methods in both efficiency and scalability. Experiments on various types of networks yield up to 50-70% memory reduction with only 10-15% overhead, closely matching optimal solutions while significantly reducing solver time. Seamlessly integrating with PyTorch Autograd, HiRemate requires almost no code change to use, enabling broad adoption in memory-constrained deep learning.
|
Rematerialization, Checkpointing, Memory-Efficient Training, Neural Networks, PyTorch, Integer Linear Programming, Training
| null | 16,364 | null |
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] | null |
114 |
ITFormer: Bridging Time Series and Natural Language for Multi-Modal QA with Large-Scale Multitask Dataset
|
https://openreview.net/forum?id=GByP03IitA
|
[
"Yilin wang",
"Peixuan Lei",
"Jie Song",
"Yuzhe Hao",
"Tao Chen",
"Yuxuan Zhang",
"LEI JIA",
"Yuanxiang Li",
"zhongyu wei"
] |
Poster
|
Time-series data are critical in diverse applications, such as industrial monitoring, medical diagnostics, and climate research. However, effectively integrating these high-dimensional temporal signals with natural language for dynamic, interactive tasks remains a significant challenge. To address this, we introduce the Time-Series Question Answering (Time-Series QA) task and release EngineMT-QA, the first large-scale, multi-task, temporal-textual QA dataset designed to capture complex interactions between time-series signals and natural language. Building on this resource, we propose the Instruct Time Transformer (ITFormer), a novel framework that bridges time-series encoders with frozen large language models (LLMs). ITFormer effectively extracts, aligns, and fuses temporal and textual features, achieving a strong improvement in QA accuracy over strong baselines with fewer than 1\% additional trainable parameters. By combining computational efficiency with robust cross-modal modeling, our work establishes a adaptable paradigm for integrating temporal data with natural language, paving the way for new research and applications in multi-modal AI. More details about the project, including datasets and code, are available at: https://pandalin98.github.io/itformer_site/.
|
Time Series Analysis, Time-Series Question Answering, Time-Series-Textual Alignment, Time-Series-Textual Fusion
|
Bridging time-series data and natural language, we propose ITFormer and introduce EngineMT-QA, enabling efficient and accurate Time-Series Question Answering for multimodal AI
| 16,325 |
2506.20093
|
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] | null |
115 |
GPEN: Global Position Encoding Network for Enhanced Subgraph Representation Learning
|
https://openreview.net/forum?id=7QFmZ7i7sr
|
[
"Nannan Wu",
"Yuming Huang",
"Yiming Zhao",
"Jie Chen",
"Wenjun Wang"
] |
Poster
|
Subgraph representation learning has attracted growing interest due to its wide applications in various domains. However, existing methods primarily focus on local neighborhood structures while overlooking the significant impact of global structural information, in particular the influence of multi-hop neighbors beyond immediate neighborhoods. This presents two key challenges: how to effectively capture the structural relationships between distant nodes, and how to prevent excessive aggregation of global structural information from weakening the discriminative ability of subgraph representations.
To address these challenges, we propose GPEN (Global Position Encoding Network). GPEN leverages a hierarchical tree structure to encode each node's global position based on its path distance to the root node, enabling a systematic way to capture relationships between distant nodes. Furthermore, we introduce a boundary-aware convolution module that selectively integrates global structural information while maintaining the unique structural patterns of each subgraph. Extensive experiments on eight public datasets identify that GPEN significantly outperforms state-of-the-art methods in subgraph representation learning.
|
Subgraph Representation Learning
| null | 16,318 | null |
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] | null |
116 |
Fast Min-$\epsilon$ Segmented Regression using Constant-Time Segment Merging
|
https://openreview.net/forum?id=w2QNIkcwWw
|
[
"Ansgar Lößer",
"Max Schlecht",
"Florian Schintke",
"Joel Witzke",
"Matthias Weidlich",
"Björn Scheuermann"
] |
Poster
|
Segmented regression is a statistical method that approximates a function $f$ by a piecewise function $\hat{f}$ using noisy data samples.
*Min-$\epsilon$* approaches aim to reduce the regression function's mean squared error (MSE) for a given number of $k$ segments.
An optimal solution for *min-$\epsilon$* segmented regression is found in $\mathcal{O}(n^2)$ time (Bai & Perron, 1998; Yamamoto & Perron, 2013) for $n$ samples. For large datasets, current heuristics improve time complexity to $\mathcal{O}(n\log{n})$ (Acharya et al., 2016) but can result in large errors, especially when exactly $k$ segments are used.
We present a method for *min-$\epsilon$* segmented regression that combines the scalability of top existing heuristic solutions with a statistical efficiency similar to the optimal solution. This is achieved by using a new method to merge an initial set of segments using precomputed matrices from samples, allowing both merging and error calculation in constant time.
Our approach, using the same samples and parameter $k$, produces segments with up to 1,000 times lower MSE compared to Acharya et al. (2016) in about 100 times less runtime on data sets over $10^4$ samples.
|
Regression, Segmented Regression, Time-Series Analysis
| null | 16,316 | null |
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] |
https://github.com/Loesgar/mvsr/tree/paper-icml-25
|
117 |
How Compositional Generalization and Creativity Improve as Diffusion Models are Trained
|
https://openreview.net/forum?id=1OUEnfusEd
|
[
"Alessandro Favero",
"Antonio Sclocchi",
"Francesco Cagnetta",
"Pascal Frossard",
"Matthieu Wyart"
] |
Poster
|
Natural data is often organized as a hierarchical composition of features. How many samples do generative models need in order to learn the composition rules, so as to produce a combinatorially large number of novel data? What signal in the data is exploited to learn those rules? We investigate these questions in the context of diffusion models both theoretically and empirically. Theoretically, we consider a simple probabilistic context-free grammar - a tree-like graphical model used to represent the hierarchical and compositional structure of data such as language and images. We demonstrate that diffusion models learn the grammar's composition rules with the sample complexity required for clustering features with statistically similar context, a process similar to the word2vec algorithm. However, this clustering emerges hierarchically: higher-level features associated with longer contexts require more data to be identified. This mechanism leads to a sample complexity that scales polynomially with the said context size. As a result, diffusion models trained on an intermediate dataset size generate data coherent up to a certain scale, but lacking global coherence. We test these predictions across different domains and find remarkable agreement: both generated texts and images achieve progressively larger coherence lengths as the training time or dataset size grows. We discuss connections between the hierarchical clustering mechanism we introduce here and the renormalization group in physics.
|
Science of deep learning, compositionality, diffusion models, probabilistic graphical models, sample complexity, generalization
| null | 16,314 |
2502.12089
|
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] | null |
118 |
Gradient-based Explanations for Deep Learning Survival Models
|
https://openreview.net/forum?id=P0wSGDoip1
|
[
"Sophie Hanna Langbein",
"Niklas Koenen",
"Marvin N. Wright"
] |
Poster
|
Deep learning survival models often outperform classical methods in time-to-event predictions, particularly in personalized medicine, but their "black box" nature hinders broader adoption. We propose a framework for gradient-based explanation methods tailored to survival neural networks, extending their use beyond regression and classification. We analyze the implications of their theoretical assumptions for time-dependent explanations in the survival setting and propose effective visualizations incorporating the temporal dimension. Experiments on synthetic data show that gradient-based methods capture the magnitude and direction of local and global feature effects, including time dependencies. We introduce GradSHAP(t), a gradient-based counterpart to SurvSHAP(t), which outperforms SurvSHAP(t) and SurvLIME in a computational speed vs. accuracy trade-off. Finally, we apply these methods to medical data with multi-modal inputs, revealing relevant tabular features and visual patterns, as well as their temporal dynamics.
|
Deep Learning, Survival Analysis, Explainable Artificial Intelligence, Interpretable Machine Learning, XAI, IML, Feature Attribution
|
We introduce gradient-based explanation methods for survival neural networks, offering improved interpretability, time-dependent insights, and faster, more accurate alternatives for analyzing medical and multi-modal data.
| 16,311 |
2502.04970
|
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] |
https://github.com/bips-hb/Survival-XAI-ICML
|
119 |
Pairwise Maximum Likelihood For Multi-Class Logistic Regression Model With Multiple Rare Classes
|
https://openreview.net/forum?id=9Kywz2fO26
|
[
"Xuetong Li",
"Danyang Huang",
"Hansheng Wang"
] |
Poster
|
We study in this work the problem of multi-class logistic regression with one major class and multiple rare classes, which is motivated by a real application in TikTok live stream data. The model is inspired by the two-class logistic regression model of Wang (2020) but with surprising theoretical findings, which in turn motivate new estimation methods with excellent statistical and computational efficiency.
Specifically, since rigorous theoretical analysis suggests that the resulting maximum likelihood estimators of different rare classes should be asymptotically independent, we consider to solve multiple pairwise two-class logistic regression problems instead of optimizing the joint log-likelihood function with computational challenge in multi-class problem, which are computationally much easier and can be conducted in a fully parallel way. To further reduce the computation cost, a subsample-based pairwise likelihood estimator is developed by down-sampling the major class. We show rigorously that the resulting estimators could be as asymptotically efficient as the global maximum likelihood estimator under appropriate regularity conditions. Extensive simulation studies are presented to support our theoretical findings and a TikTok live stream dataset is analyzed for illustration purpose.
|
Multi-class logistic regression model, Pairwise maximum likelihood estimation, Rare class analysis, Car plate recognition
|
We study multi-class logistic regression with multiple rare classes, proposing an efficient and effective parallel pairwise estimation method.
| 16,293 | null |
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] | null |
120 |
ShieldAgent: Shielding Agents via Verifiable Safety Policy Reasoning
|
https://openreview.net/forum?id=DkRYImuQA9
|
[
"Zhaorun Chen",
"Mintong Kang",
"Bo Li"
] |
Poster
|
Autonomous agents powered by foundation models have seen widespread adoption across various real-world applications. However, they remain highly vulnerable to malicious instructions and attacks, which can result in severe consequences such as privacy breaches and financial losses. More critically, existing guardrails for LLMs are not applicable due to the complex and dynamic nature of agents. To tackle these challenges, we propose ShieldAgent, the first guardrail agent designed to enforce explicit safety policy compliance for the action trajectory of other protected agents through logical reasoning. Specifically, ShieldAgent first constructs a safety policy model by extracting verifiable rules from policy documents and structuring them into a set of action-based probabilistic rule circuits. Given the action trajectory of the protected agent, ShieldAgent retrieves relevant rule circuits and generates a shielding plan, leveraging its comprehensive tool library and executable code for formal verification. In addition, given the lack of guardrail benchmarks for agents, we introduce ShieldAgent-Bench, a dataset with 3K safety-related pairs of agent instructions and action trajectories, collected via SOTA attacks across 6 web environments and 7 risk categories. Experiments show that ShieldAgent achieves SOTA on ShieldAgent-Bench and three existing benchmarks, outperforming prior methods by 11.3% on average with a high recall of 90.1%. Additionally, ShieldAgent reduces API queries by 64.7% and inference time by 58.2%, demonstrating its high precision and efficiency in safeguarding agents. Our project is available and continuously maintained here: https://shieldagent-aiguard.github.io/
|
LLM Agent Safety, LLM Guardrail Agent, Policy Compliance, Automated Logic Reasoning
|
We present ShieldAgent, which safeguards foundation model agents by enforcing policy compliance, and ShieldAgent-Bench, a dataset for evaluating guardrail performance across diverse real-world scenarios.
| 16,287 |
2503.22738
|
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] | null |
121 |
Rethinking Benign Overfitting in Two-Layer Neural Networks
|
https://openreview.net/forum?id=Uc0dTE2Wox
|
[
"Ruichen Xu",
"Kexin Chen"
] |
Poster
|
Recent theoretical studies (Kou et al., 2023; Cao et al., 2022) revealed a sharp phase transition from benign to harmful overfitting when the
noise-to-feature ratio exceeds a threshold—a situation common in long-tailed data distributions where atypical data is prevalent. However, such harmful overfitting rarely happens in overparameterized neural networks. Further experimental results suggested that memorization is necessary for achieving near-optimal generalization error in long-tailed data distributions (Feldman & Zhang, 2020). We argue that this discrepancy between theoretical predictions and empirical observations arises because previous feature-noise data models overlook the heterogeneous nature of noise across different data classes. In this paper, we refine the feature-noise data model by incorporating class-dependent heterogeneous noise and re-examine the overfitting phenomenon in neural networks. Through a comprehensive analysis of the training dynamics, we establish test loss bounds for the refined model. Our findings reveal that neural networks can leverage "data noise" to learn implicit features that improve the classification accuracy for long-tailed data. Our analysis also provides a training-free metric for evaluating data influence on test performance. Experimental validation on both synthetic and real-world datasets supports our theoretical results.
|
Benign overfitting, long-tailed data, two-layer neural networks
|
We re-examine benign overfitting in two-layer neural networks and prove that while data can be classified by explicit features, long-tailed data can also be classified base on implicit features learned from class-dependent noise.
| 16,273 |
2502.11893
|
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] | null |
122 |
Symmetry-Aware GFlowNets
|
https://openreview.net/forum?id=JD4eHocSPi
|
[
"Hohyun Kim",
"Seunggeun Lee",
"Min-hwan Oh"
] |
Poster
|
Generative Flow Networks (GFlowNets) offer a powerful framework for sampling graphs in proportion to their rewards. However, existing approaches suffer from systematic biases due to inaccuracies in state transition probability computations. These biases, rooted in the inherent symmetries of graphs, impact both atom-based and fragment-based generation schemes. To address this challenge, we introduce Symmetry-Aware GFlowNets (SA-GFN), a method that incorporates symmetry corrections into the learning process through reward scaling. By integrating bias correction directly into the reward structure, SA-GFN eliminates the need for explicit state transition computations. Empirical results show that SA-GFN enables unbiased sampling while enhancing diversity and consistently generating high-reward graphs that closely match the target distribution.
|
GFlowNet, graph generation, molecule optimization
|
This paper analyzes the bias inherent in GFlowNets and proposes a reward-scaling method to address the issue.
| 16,266 |
2506.02685
|
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] |
https://github.com/hohyun312/sagfn
|
123 |
Kinetic Langevin Diffusion for Crystalline Materials Generation
|
https://openreview.net/forum?id=7J1kwZY72h
|
[
"François R J Cornet",
"Federico Bergamin",
"Arghya Bhowmik",
"Juan Maria Garcia-Lastra",
"Jes Frellsen",
"Mikkel N. Schmidt"
] |
Poster
|
Generative modeling of crystalline materials using diffusion models presents a series of challenges: the data distribution is characterized by inherent symmetries and involves multiple modalities, with some defined on specific manifolds. Notably, the treatment of fractional coordinates representing atomic positions in the unit cell requires careful consideration, as they lie on a hypertorus. In this work, we introduce Kinetic Langevin Diffusion for Materials (KLDM), a novel diffusion model for crystalline materials generation, where the key innovation resides in the modeling of the coordinates. Instead of resorting to Riemannian diffusion on the hypertorus directly, we generalize Trivialized Diffusion Model (TDM) to account for the symmetries inherent to crystals. By coupling coordinates with auxiliary Euclidean variables representing velocities, the diffusion process is now offset to a flat space. This allows us to effectively perform diffusion on the hypertorus while providing a training objective that accounts for the periodic translation symmetry of the true data distribution. We evaluate KLDM on both Crystal Structure Prediction (CSP) and De-novo Generation (DNG) tasks, demonstrating its competitive performance with current state-of-the-art models.
|
generative models, diffusion models, crystals, materials
| null | 16,254 |
2507.03602
|
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] |
https://github.com/frcnt/kldm
|
124 |
CombiMOTS: Combinatorial Multi-Objective Tree Search for Dual-Target Molecule Generation
|
https://openreview.net/forum?id=FSlTEObdLl
|
[
"Thibaud Southiratn",
"Bonil Koo",
"Yijingxiu Lu",
"Sun Kim"
] |
Poster
|
Dual-target molecule generation, which focuses on discovering compounds capable of interacting with two target proteins, has garnered significant attention due to its potential for improving therapeutic efficiency, safety and resistance mitigation.
Existing approaches face two critical challenges.
First, by simplifying the complex dual-target optimization problem to scalarized combinations of individual objectives, they fail to capture important trade-offs between target engagement and molecular properties.
Second, they typically do not integrate synthetic planning into the generative process.
This highlights a need for more appropriate objective function design and synthesis-aware methodologies tailored to the dual-target molecule generation task.
In this work, we propose CombiMOTS, a Pareto Monte Carlo Tree Search (PMCTS) framework that generates dual-target molecules.
CombiMOTS is designed to explore a synthesizable fragment space while employing vectorized optimization constraints to encapsulate target affinity and physicochemical properties.
Extensive experiments on real-world databases demonstrate that CombiMOTS produces novel dual-target molecules with high docking scores, enhanced diversity, and balanced pharmacological characteristics, showcasing its potential as a powerful tool for dual-target drug discovery.
The code and data is accessible through \url{https://github.com/Tibogoss/CombiMOTS}.
|
Dual-target Molecule Generation, Fragment-based Drug Discovery, Monte-Carlo Tree Search, Pareto Optimization, Search Space Reduction
|
CombiMOTS is a framework that designs dual-target compounds using industrial building blocks and Pareto MCTS for favorable multi-objective trade-offs and improved synthesizability.
| 16,227 | null |
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] |
https://github.com/Tibogoss/CombiMOTS
|
125 |
Scalable Meta-Learning via Mixed-Mode Differentiation
|
https://openreview.net/forum?id=NWKjVzkDzg
|
[
"Iurii Kemaev",
"Dan A. Calian",
"Luisa M Zintgraf",
"Gregory Farquhar",
"Hado van Hasselt"
] |
Poster
|
Gradient-based bilevel optimisation is a powerful technique with applications in hyperparameter optimisation, task adaptation, algorithm discovery, meta-learning more broadly, and beyond. It often requires differentiating through the gradient-based optimisation process itself, leading to "gradient-of-a-gradient" calculations with computationally expensive second-order and mixed derivatives. While modern automatic differentiation libraries provide a convenient way to write programs for calculating these derivatives, they oftentimes cannot fully exploit the specific structure of these problems out-of-the-box, leading to suboptimal performance. In this paper, we analyse such cases and propose Mixed-Flow Meta-Gradients, or MixFlow-MG -- a practical algorithm that uses mixed-mode differentiation to construct more efficient and scalable computational graphs yielding over 10x memory and up to 25\% wall-clock time improvements over standard implementations in modern meta-learning setups.
|
meta-learning, bilevel optimization, second-order optimization, automatic differentiation, autodiff, mixed-mode differentiation, gradient-based methods, scalable algorithms
|
A practical algorithm that uses mixed-mode differentiation to construct more efficient and scalable computational graphs yielding over 10x memory and up to 25% wall-clock time improvements over standard implementations in modern meta-learning setups.
| 16,225 |
2505.00793
|
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] | null |
126 |
PINNsAgent: Automated PDE Surrogation with Large Language Models
|
https://openreview.net/forum?id=RO5OGOzs6M
|
[
"Qingpo Wuwu",
"Chonghan Gao",
"Tianyu Chen",
"Yihang Huang",
"Yuekai Zhang",
"Jianing Wang",
"Jianxin Li",
"Haoyi Zhou",
"Shanghang Zhang"
] |
Poster
|
Solving partial differential equations (PDEs) using neural methods has been a long-standing scientific and engineering research pursuit. Physics-Informed Neural Networks (PINNs) have emerged as a promising alternative to traditional numerical methods for solving PDEs. However, the gap between domain-specific knowledge and deep learning expertise often limits the practical application of PINNs. Previous works typically involve manually conducting extensive PINNs experiments and summarizing heuristic rules for hyperparameter tuning. In this work, we introduce PINNsAgent, a novel surrogation framework that leverages large language models (LLMs) to bridge the gap between domain-specific knowledge and deep learning. PINNsAgent integrates Physics-Guided Knowledge Replay (PGKR) for efficient knowledge transfer from solved PDEs to similar problems, and Memory Tree Reasoning for exploring the search space of optimal PINNs architectures. We evaluate PINNsAgent on 14 benchmark PDEs, demonstrating its effectiveness in automating the surrogation process and significantly improving the accuracy of PINNs-based solutions.
|
pinns, llm-agent
| null | 16,222 |
2501.12053
|
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] | null |
127 |
Compositional Generalization via Forced Rendering of Disentangled Latents
|
https://openreview.net/forum?id=rkHCHI5H5W
|
[
"Qiyao Liang",
"Daoyuan Qian",
"Liu Ziyin",
"Ila R Fiete"
] |
Poster
|
Composition—the ability to generate myriad variations from finite means—is believed to underlie powerful generalization. However, compositional generalization remains a key challenge for deep learning. A widely held assumption is that learning disentangled (factorized) representations naturally supports this kind of extrapolation. Yet, empirical results are mixed, with many generative models failing to recognize and compose factors to generate out-of-distribution (OOD) samples. In this work, we investigate a controlled 2D Gaussian "bump" generation task with fully disentangled $(x,y)$ inputs, demonstrating that standard generative architectures still fail in OOD regions when training with partial data, by re-entangling latent representations in subsequent layers. By examining the model's learned kernels and manifold geometry, we show that this failure reflects a "memorization" strategy for generation via data superposition rather than via composition of the true factorized features. We show that when models are forced—through architectural modifications with regularization or curated training data—to render the disentangled latents into the full-dimensional representational (pixel) space, they can be highly data-efficient and effective at composing in OOD regions. These findings underscore that disentangled latents in an abstract representation are insufficient and show that if models can represent disentangled factors directly in the output representational space, it can achieve robust compositional generalization.
|
Factorization, Compositionality, Compositional Generalization, Data Efficiency
|
Standard networks given separate x/y cues still memorize and stitch examples rather than combine them. Forcing each cue into the output space—via low-rank embeddings or simple stripe data—enables true compositional generalization.
| 16,212 |
2501.18797
|
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] |
https://github.com/qiyaoliang/DisentangledCompGen
|
128 |
Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time
|
https://openreview.net/forum?id=aqZKgwf7Cc
|
[
"Duc Anh Nguyen",
"Ernesto Araya",
"Adalbert Fono",
"Gitta Kutyniok"
] |
Poster
|
Recent years have seen significant progress in developing spiking neural networks (SNNs) as a potential solution to the energy challenges posed by conventional artificial neural networks (ANNs). However, our theoretical understanding of SNNs remains relatively limited compared to the ever-growing body of literature on ANNs. In this paper, we study a discrete-time model of SNNs based on leaky integrate-and-fire (LIF) neurons, referred to as discrete-time LIF-SNNs, a widely used framework that still lacks solid theoretical foundations. We demonstrate that discrete-time LIF-SNNs realize piecewise constant functions defined on polyhedral regions, and more importantly, we quantify the network size required to approximate continuous functions. Moreover, we investigate the impact of latency (number of time steps) and depth (number of layers) on the complexity of the input space partitioning induced by discrete-time LIF-SNNs. Our analysis highlights the importance of latency and contrasts these networks with ANNs that use piecewise linear activation functions. Finally, we present numerical experiments to support our theoretical findings.
|
Spiking neural networks, Linear regions, Approximation theory
| null | 16,203 |
2505.18023
|
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] | null |
129 |
Scalable Non-Equivariant 3D Molecule Generation via Rotational Alignment
|
https://openreview.net/forum?id=l5KpQ5MmaD
|
[
"Yuhui Ding",
"Thomas Hofmann"
] |
Poster
|
Equivariant diffusion models have achieved impressive performance in 3D molecule generation. These models incorporate Euclidean symmetries of 3D molecules by utilizing an SE(3)-equivariant denoising network. However, specialized equivariant architectures limit the scalability and efficiency of diffusion models. In this paper, we propose an approach that relaxes such equivariance constraints. Specifically, our approach learns a sample-dependent SO(3) transformation for each molecule to construct an aligned latent space. A non-equivariant diffusion model is then trained over the aligned representations. Experimental results demonstrate that our approach performs significantly better than previously reported non-equivariant models. It yields sample quality comparable to state-of-the-art equivariant diffusion models and offers improved training and sampling efficiency. Our code is available at: https://github.com/skeletondyh/RADM
|
Non-equivariant diffusion, 3D molecule generation
| null | 16,184 |
2506.10186
|
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] |
https://github.com/skeletondyh/radm
|
130 |
Learning Distances from Data with Normalizing Flows and Score Matching
|
https://openreview.net/forum?id=SOwcmZ91Sl
|
[
"Peter Sorrenson",
"Daniel Behrend-Uriarte",
"Christoph Schnoerr",
"Ullrich Koethe"
] |
Poster
|
Density-based distances (DBDs) provide a principled approach to metric learning by defining distances in terms of the underlying data distribution. By employing a Riemannian metric that increases in regions of low probability density, shortest paths naturally follow the data manifold. Fermat distances, a specific type of DBD, have attractive properties, but existing estimators based on nearest neighbor graphs suffer from poor convergence due to inaccurate density estimates. Moreover, graph-based methods scale poorly to high dimensions, as the proposed geodesics are often insufficiently smooth. We address these challenges in two key ways. First, we learn densities using normalizing flows. Second, we refine geodesics through relaxation, guided by a learned score model. Additionally, we introduce a dimension-adapted Fermat distance that scales intuitively to high dimensions and improves numerical stability. Our work paves the way for the practical use of density-based distances, especially in high-dimensional spaces.
|
density-based distance, Fermat distance, Riemannian geometry, representation learning, normalizing flows, score matching
|
Our work improves estimation of Fermat distances by combining normalizing flows, score-based models, geodesic smoothing, and a new dimension-adapted Fermat distance for better scalability.
| 16,182 |
2407.09297
|
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] |
https://github.com/vislearn/Fermat-Distance
|
131 |
The Importance of Being Lazy: Scaling Limits of Continual Learning
|
https://openreview.net/forum?id=edhBkkYS8R
|
[
"Jacopo Graldi",
"Alessandro Breccia",
"Giulia Lanzillotta",
"Thomas Hofmann",
"Lorenzo Noci"
] |
Poster
|
Despite recent efforts, neural networks still struggle to learn in non-stationary environments, and our understanding of catastrophic forgetting (CF) is far from complete.
In this work, we perform a systematic study on the impact of model scale and the degree of feature learning in continual learning. We reconcile existing contradictory observations on scale in the literature, by differentiating between *lazy* and *rich* training regimes through a variable parameterization of the architecture. We show that increasing model width is only beneficial when it reduces the amount of *feature learning*, yielding more laziness. Using the framework of dynamical mean field theory, we then study the infinite width dynamics of the model in the feature learning regime and characterize CF, extending prior theoretical results limited to the lazy regime. We study the intricate relationship between feature learning, task non-stationarity, and forgetting, finding that high feature learning is only beneficial with highly similar tasks. We identify a transition modulated by task similarity where the model exits an effectively lazy regime with low forgetting to enter a rich regime with significant forgetting. Finally, our findings reveal that neural networks achieve optimal performance at a critical level of feature learning, which depends on task non-stationarity and *transfers across model scales*. This work provides a unified perspective on the role of scale and feature learning in continual learning.
|
feature learning, continual learning, deep learning, forgetting, ntk, muP, lazy, dmft
|
We investigate how network size and feature learning regimes affect catastrophic forgetting in continual learning.
| 16,178 |
2506.16884
|
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] | null |
132 |
EcoMapper: Generative Modeling for Climate-Aware Satellite Imagery
|
https://openreview.net/forum?id=YUtJsxQjv3
|
[
"Muhammed Goktepe",
"Amir hossein Shamseddin",
"Erencan Uysal",
"Javier Muinelo Monteagudo",
"Lukas Drees",
"Aysim Toker",
"Senthold Asseng",
"Malte von Bloh"
] |
Poster
|
Satellite imagery is essential for Earth observation, enabling applications like crop yield prediction, environmental monitoring, and climate
change assessment. However, integrating satellite imagery with climate data remains a challenge, limiting its utility for forecasting and scenario analysis. We introduce a novel dataset of 2.9 million Sentinel-2 images spanning 15 land cover types with corresponding climate records, forming the foundation for two satellite image generation approaches using fine-tuned Stable Diffusion 3 models. The first is a text-to-image generation model that uses textual prompts with climate and land cover details to produce realistic synthetic imagery for specific regions. The second leverages ControlNet for multi-conditional image generation, preserving spatial structures while mapping climate data or generating time-series to simulate landscape evolution. By combining synthetic image generation with climate and land cover data, our work advances generative modeling in remote sensing, offering realistic inputs for environmental forecasting and new possibilities for climate adaptation and geospatial analysis.
|
remote sensing, generative modeling, stable diffusion, climate, satellite imagery, computer vision
|
We present a novel dataset and two generative approaches using fine-tuned Stable Diffusion 3 models to create realistic satellite imagery conditioned on climate and land cover data, enabling single-image and conditional time-series generation.
| 16,158 | null |
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] |
https://github.com/maltevb/ecomapper
|
133 |
DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts
|
https://openreview.net/forum?id=umT6rMf1Rm
|
[
"Tobias Braun",
"Mark Rothermel",
"Marcus Rohrbach",
"Anna Rohrbach"
] |
Poster
|
The proliferation of disinformation demands reliable and scalable fact-checking solutions. We present **D**ynamic **E**vidence-based **FA**ct-checking with **M**ultimodal **E**xperts (DEFAME), a modular, zero-shot MLLM pipeline for open-domain, text-image claim verification. DEFAME operates in a six-stage process, dynamically selecting the tools and search depth to extract and evaluate textual and visual evidence. Unlike prior approaches that are text-only, lack explainability, or rely solely on parametric knowledge, DEFAME performs end-to-end verification, accounting for images in claims *and* evidence while generating structured, multimodal reports. Evaluation on the popular benchmarks VERITE, AVeriTeC, and MOCHEG shows that DEFAME surpasses all previous methods, establishing itself as the new general state-of-the-art fact-checking system for uni- and multimodal fact-checking. Moreover, we introduce a new multimodal benchmark, ClaimReview2024+, featuring claims after the knowledge cutoff of GPT-4o, avoiding data leakage. Here, DEFAME drastically outperforms the GPT-4o baselines, showing temporal generalizability and the potential for real-time fact-checking.
|
fact-checking, multimodal, claim-verification, MLLM
|
DEFAME is a modular, training-free fact-checking system that verifies open-domain text-image claims end-to-end, dynamically retrieving and integrating textual and visual evidence.
| 16,152 |
2412.10510
|
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] |
https://github.com/multimodal-ai-lab/DEFAME/tree/icml
|
134 |
Generalization of noisy SGD in unbounded non-convex settings
|
https://openreview.net/forum?id=Au9rfI6Fjd
|
[
"Leello Tadesse Dadi",
"Volkan Cevher"
] |
Poster
|
We study generalization of iterative noisy gradient schemes on smooth non-convex losses. Formally, we establish time-independent information theoretic generalization bounds for Stochastic Gradient Langevin Dynamics (SGLD) that do not diverge as the iteration count increases. Our bounds are obtained through a stability argument: we analyze the difference between two SGLD sequences ran in parallel on two datasets sampled from the same distribution. Our result only requires an isoperimetric inequality to hold, which is merely a restriction on the tails of the loss. Our work relaxes the assumptions of prior work to establish that the iterates stay within a bounded KL divergence from each other. Under an additional dissipativity assumption, we show that the stronger Renyi divergence also stays bounded by establishing a uniform log-Sobolev constant of the iterates. Without dissipativity, we sidestep the need for local log-Sobolev inequalities and instead exploit the regularizing properties of Gaussian convolution. These techniques allow us to show that strong convexity is not necessary for finite stability bounds. Our work shows that noisy SGD can have finite, iteration-independent, generalization and differential privacy bounds in unbounded non-convex settings.
|
Information theoretic generalization, Langevin, SGD, differential privacy
| null | 16,148 | null |
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] | null |
135 |
Statistical Hypothesis Testing for Auditing Robustness in Language Models
|
https://openreview.net/forum?id=ECayXPDoha
|
[
"Paulius Rauba",
"Qiyao Wei",
"Mihaela van der Schaar"
] |
Poster
|
Consider the problem of testing whether the outputs of a large language model (LLM) system change under an arbitrary intervention, such as an input perturbation or changing the model variant. We cannot simply compare two LLM outputs since they might differ due to the stochastic nature of the system, nor can we compare the entire output distribution due to computational intractability. While existing methods for analyzing text-based outputs exist, they focus on fundamentally different problems, such as measuring bias or fairness. To this end, we introduce distribution-based perturbation analysis, a framework that reformulates LLM perturbation analysis as a frequentist hypothesis testing problem. We construct empirical null and alternative output distributions within a low-dimensional semantic similarity space via Monte Carlo sampling, enabling tractable inference without restrictive distributional assumptions. The framework is (i) model-agnostic, (ii) supports the evaluation of arbitrary input perturbations on any black-box LLM, (iii) yields interpretable p-values; (iv) supports multiple perturbations via controlled error rates; and (v) provides scalar effect sizes. We demonstrate the usefulness of the framework across multiple case studies, showing how we can quantify response changes, measure true/false positive rates, and evaluate alignment with reference models. Above all, we see this as a reliable frequentist hypothesis testing framework for LLM auditing.
|
language models, safety, interpretability, reliability
|
We develop a statitical hypothesis testing method to quantify the impact of a perturbation in the input prompt on the outputs of language models
| 16,143 |
2506.07947
|
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] |
https://github.com/vanderschaarlab/dbpa
|
136 |
SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations
|
https://openreview.net/forum?id=0Hd1lh52Fi
|
[
"Grigory Bartosh",
"Dmitry Vetrov",
"Christian A. Naesseth"
] |
Poster
|
The Latent Stochastic Differential Equation (SDE) is a powerful tool for time series and sequence modeling. However, training Latent SDEs typically relies on adjoint sensitivity methods, which depend on simulation and backpropagation through approximate SDE solutions, which limit scalability. In this work, we propose SDE Matching, a new simulation-free method for training Latent SDEs. Inspired by modern Score- and Flow Matching algorithms for learning generative dynamics, we extend these ideas to the domain of stochastic dynamics for time series modeling, eliminating the need for costly numerical simulations. Our results demonstrate that SDE Matching achieves performance comparable to adjoint sensitivity methods while drastically reducing computational complexity.
|
diffusion, generative models, SDE, time series, variational inference
| null | 16,133 |
2502.02472
|
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] | null |
137 |
An Empirical Study on Configuring In-Context Learning Demonstrations for Unleashing MLLMs' Sentimental Perception Capability
|
https://openreview.net/forum?id=DTdtM53iag
|
[
"Daiqing Wu",
"Dongbao Yang",
"Sicheng Zhao",
"Can Ma",
"Yu Zhou"
] |
Poster
|
The advancements in Multimodal Large Language Models (MLLMs) have enabled various multimodal tasks to be addressed under a zero-shot paradigm. This paradigm sidesteps the cost of model fine-tuning, emerging as a dominant trend in practical application. Nevertheless, Multimodal Sentiment Analysis (MSA), a pivotal challenge in the quest for general artificial intelligence, fails to accommodate this convenience. The zero-shot paradigm exhibits undesirable performance on MSA, casting doubt on whether MLLMs can perceive sentiments as competent as supervised models. By extending the zero-shot paradigm to In-Context Learning (ICL) and conducting an in-depth study on configuring demonstrations, we validate that MLLMs indeed possess such capability. Specifically, three key factors that cover demonstrations' retrieval, presentation, and distribution are comprehensively investigated and optimized. A sentimental predictive bias inherent in MLLMs is also discovered and later effectively counteracted. By complementing each other, the devised strategies for three factors result in average accuracy improvements of 15.9% on six MSA datasets against the zero-shot paradigm and 11.2% against the random ICL baseline.
|
Multimodal Sentiment Analysis, Multimodal Large Language Model, In-Context Learning
|
We conduct an in-depth investigation into three pivotal factors that influence the configuration of In-Context Learning demonstrations on Multimodal Sentiment Analysis.
| 16,131 |
2505.16193
|
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] | null |
138 |
Beyond Cropped Regions: New Benchmark and Corresponding Baseline for Chinese Scene Text Retrieval in Diverse Layouts
|
https://openreview.net/forum?id=G80YGyxzv7
|
[
"Gengluo Li",
"Huawen Shen",
"Yu Zhou"
] |
Poster
|
Chinese scene text retrieval is a practical task that aims to search for images containing visual instances of a Chinese query text. This task is extremely challenging because Chinese text often features complex and diverse layouts in real-world scenes. Current efforts tend to inherit the solution for English scene text retrieval, failing to achieve satisfactory performance. In this paper, we establish a Diversified Layout benchmark for Chinese Street View Text Retrieval (DL-CSVTR), which is specifically designed to evaluate retrieval performance across various text layouts, including vertical, cross-line, and partial alignments. To address the limitations in existing methods, we propose Chinese Scene Text Retrieval CLIP (CSTR-CLIP), a novel model that integrates global visual information with multi-granularity alignment training. CSTR-CLIP applies a two-stage training process to overcome previous limitations, such as the exclusion of visual features outside the text region and reliance on single-granularity alignment, thereby enabling the model to effectively handle diverse text layouts. Experiments on existing benchmark show that CSTR-CLIP outperforms the previous state-of-the-art model by 18.82% accuracy and also provides faster inference speed. Further analysis on DL-CSVTR confirms the superior performance of CSTR-CLIP in handling various text layouts. The dataset and code will be publicly available to facilitate research in Chinese scene text retrieval.
|
Scene text retrieval, Multimodal retrieval, Text understanding, Text layout
|
We introduce a new benchmark for Chinese scene text retrieval, highlighting the limitations of previous methods and proposing an approach that outperforms existing techniques.
| 16,125 |
2506.04999
|
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] | null |
139 |
Controlling Large Language Model with Latent Action
|
https://openreview.net/forum?id=cEKrGCFXPA
|
[
"Chengxing Jia",
"Ziniu Li",
"Pengyuan Wang",
"Yi-Chen Li",
"Zhenyu Hou",
"Yuxiao Dong",
"Yang Yu"
] |
Poster
|
Adapting Large Language Models (LLMs) to downstream tasks using Reinforcement Learning (RL) has proven to be an effective approach. However, LLMs do not inherently define the structure of an agent for RL training, particularly in terms of specifying the action space. This paper studies learning a compact latent action space to enhance the controllability and exploration of RL for LLMs. Inspired by reinforcement learning from observations, we propose **Co**ntrolling Large Language Models with **L**atent **A**ctions **CoLA**, a framework that integrates a latent action space into pre-trained LLMs. **CoLA** employs an \emph{inverse dynamics model} to extract latent actions conditioned on future tokens, ensuring that the next token prediction is partially influenced by these actions. Simultaneously, **CoLA** fine-tunes the pre-trained LLM to function as a \emph{language world model}, capable of incorporating latent actions as inputs. Additionally, **CoLA** trains a \emph{policy model} to generate actions within this language world model. The policy model can be trained via behavior cloning to mimic a standard language model or through RL to maximize task-specific rewards. In this work, we apply **CoLA** to the Llama-3.1-8B model. Our experiments demonstrate that, compared to RL with token-level actions, **CoLA**'s latent actions enable greater semantic diversity. For enhancing downstream tasks, we show that **CoLA** with RL achieves a score of 42.4 on the \emph{math500} benchmark, surpassing the baseline score of 38.2, and reaches 68.2 when augmented with a Monte Carlo Tree Search variant. Furthermore, **CoLA** with RL consistently improves performance on agent-based tasks without degrading the pre-trained LLM's capabilities, unlike the baseline. Finally, **CoLA** reduces computation time by half in tasks involving enhanced thinking prompts for LLMs via RL. These results highlight **CoLA**'s potential to advance RL-based adaptation of LLMs for downstream applications. The CoLA model is available at \url{https://huggingface.co/LAMDA-RL/Llama-3.1-CoLA-10B}.
|
Controllable language model, latent action model
|
Develop a new architecture of language model to control language with latent action
| 16,114 |
2503.21383
|
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] |
https://github.com/LAMDA-RL/CoLA
|
140 |
Neutral residues: revisiting adapters for model extension
|
https://openreview.net/forum?id=gmdElnwBxt
|
[
"Franck SIGNE TALLA",
"Edouard Grave",
"Herve Jegou"
] |
Poster
|
We address the problem of extending a pre-trained large language model to a new domain that was not seen during training. Standard techniques, such as fine-tuning or low-rank adaptation (LoRA) are successful at domain adaptation, but do not formally add capacity to the model. This often leads to a trade-off, between performing well on the new domain vs. degrading performance on the original domain.
Here, we propose to revisit and improve adapters to extend LLMs. Our paper analyzes this extension problem from three angles: data, architecture and training procedure, which are advantageously considered jointly. The resulting method, called neutral residues, modifies adapters in a way that leads to each new residual block to output near-zeros on the original domain. This solution leads to strong results when adapting a state-of-the-art model originally trained on English to a new language. Neutral residues significantly outperforms competing approaches such as fine-tuning, LoRA or vanilla adapters in terms of the trade-off between learning the new language and not forgetting English.
|
LLM, Adapters, Domain Adaptation, Catastrophic Forgetting
|
We add new capabilities to a pre-trained large language model, without forgetting its original knowledge.
| 16,101 |
2410.02744
|
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] | null |
141 |
Certification for Differentially Private Prediction in Gradient-Based Training
|
https://openreview.net/forum?id=viXwXCkA7N
|
[
"Matthew Robert Wicker",
"Philip Sosnin",
"Igor Shilov",
"Adrianna Janik",
"Mark Niklas Mueller",
"Yves-Alexandre de Montjoye",
"Adrian Weller",
"Calvin Tsay"
] |
Poster
|
We study private prediction where differential privacy is achieved by adding noise to the outputs of a non-private model. Existing methods rely on noise proportional to the global sensitivity of the model, often resulting in sub-optimal privacy-utility trade-offs compared to private training. We introduce a novel approach for computing dataset-specific upper bounds on prediction sensitivity by leveraging convex relaxation and bound propagation techniques. By combining these bounds with the smooth sensitivity mechanism, we significantly improve the privacy analysis of private prediction compared to global sensitivity-based approaches. Experimental results across real-world datasets in medical image classification and natural language processing demonstrate that our sensitivity bounds are can be orders of magnitude tighter than global sensitivity. Our approach provides a strong basis for the development of novel privacy preserving technologies.
|
Differential Privacy, Convex Optimization, Deep Learning
|
We utilize novel bound propagation algorithms to upper-bound local sensitivity of machine learning predictions and leverage this bound for improved private prediction.
| 16,097 |
2406.13433
|
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] |
https://github.com/psosnin/AbstractGradientTraining
|
142 |
Measuring In-Context Computation Complexity via Hidden State Prediction
|
https://openreview.net/forum?id=X21P8etjWL
|
[
"Vincent Herrmann",
"Róbert Csordás",
"Jürgen Schmidhuber"
] |
Poster
|
Detecting when a neural sequence model does "interesting" computation is an open problem. The next token prediction loss is a poor indicator: Low loss can stem from trivially predictable sequences that are uninteresting, while high loss may reflect unpredictable but also irrelevant information that can be ignored by the model. We propose a better metric: measuring the model's ability to predict its own future hidden states. We show empirically that this metric–in contrast to the next token prediction loss–correlates with the intuitive interestingness of the task. To measure predictability, we introduce the architecture-agnostic "prediction of hidden states" (PHi) layer that serves as an information bottleneck on the main pathway of the network (e.g., the residual stream in Transformers). We propose a novel learned predictive prior that enables us to measure the novel information gained in each computation step, which serves as our metric. We show empirically that our metric predicts the description length of formal languages learned in-context, the complexity of mathematical reasoning problems, and the correctness of self-generated reasoning chains.
|
in-context learning, interpretability, transformers
|
Hidden state unpredictability in sequence models is a meaningful measure for in-context reasoning complexity
| 16,096 |
2503.13431
|
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] |
https://github.com/vincentherrmann/predicting-hidden-states
|
143 |
Let LLM Tell What to Prune and How Much to Prune
|
https://openreview.net/forum?id=zFR5aWGaUs
|
[
"Mingzhe Yang",
"Sihao Lin",
"Changlin Li",
"Xiaojun Chang"
] |
Poster
|
Large language models (LLMs) have revolutionized various AI applications. However, their billions of parameters pose significant challenges for practical deployment. Structured pruning is a hardware-friendly compression technique and receives widespread attention. Nonetheless, existing literature typically targets a single structure of LLMs. We observe that the structure units of LLMs differ in terms of inference cost and functionality. Therefore, pruning a single structure unit in isolation often results in an imbalance between performance and efficiency. In addition, previous works mainly employ a prescribed pruning ratio. Since the significance of LLM modules may vary, it is ideal to distribute the pruning load to a specific structure unit according to its role within LLMs. To address the two issues, we propose a pruning method that targets multiple LLM modules with dynamic pruning ratios. Specifically, we find the intrinsic properties of LLMs can guide us to determine the importance of each module and thus distribute the pruning load on demand, i.e., what to prune and how much to prune. This is achieved by quantifying the complex interactions within LLMs. Extensive experiments on multiple benchmarks and LLM variants demonstrate that our method effectively balances the trade-off between efficiency and performance.
|
large language models, model compression, network pruning, structured pruning
|
We propose a pruning method that targets multiple LLM modules with dynamic pruning ratios by quantifying the complex interactions within LLMs, achieving better trade-off between efficiency and performance.
| 16,094 | null |
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] | null |
144 |
Multilayer Matrix Factorization via Dimension-Reducing Diffusion Variational Inference
|
https://openreview.net/forum?id=Dd7Qo7TJpf
|
[
"Junbin Liu",
"Farzan Farnia",
"Wing-Kin Ma"
] |
Poster
|
Multilayer matrix factorization (MMF) has recently emerged as a generalized model of, and potentially a more expressive approach than, the classic matrix factorization.
This paper considers MMF under a probabilistic formulation, and our focus is on inference methods under variational inference.
The challenge in this context lies in determining a variational process that leads to a computationally efficient and accurate approximation of the maximum likelihood inference.
One well-known example is the variational autoencoder (VAE), which uses neural networks for the variational process.
In this work, we take insight from variational diffusion models in the context of generative models to develop variational inference for MMF.
We propose a dimension-reducing diffusion process that results in a new way to interact with the layered structures of the MMF model.
Experimental results demonstrate that the proposed diffusion variational inference method leads to improved performance scores compared to several existing methods, including the VAE.
|
Multilayer matrix factorization, Variational inference, Variational diffusion models, Dimension reduction
| null | 16,092 | null |
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] |
https://github.com/Szanzang/DRDVI
|
145 |
Compressing tree ensembles through Level-wise Optimization and Pruning
|
https://openreview.net/forum?id=9Klg7ce8D7
|
[
"Laurens Devos",
"Timo Martens",
"Deniz Can Oruc",
"Wannes Meert",
"Hendrik Blockeel",
"Jesse Davis"
] |
Poster
|
Tree ensembles (e.g., gradient boosting decision trees) are often used in practice because they offer excellent predictive performance while still being easy and efficient to learn. In some contexts, it is important to additionally optimize their size: this is specifically the case when models need to have verifiable properties (verification of fairness, robustness, etc. is often exponential in the ensemble's size), or when models run on battery-powered devices (smaller ensembles consume less energy, increasing battery autonomy). For this reason, compression of tree ensembles is worth studying. This paper presents LOP, a method for compressing a given tree ensemble by pruning or entirely removing trees in it, while updating leaf predictions in such a way that predictive accuracy is mostly unaffected. Empirically, LOP achieves compression factors that are often 10 to 100 times better than that of competing methods.
|
ensembles, decision forests, model efficiency, energy efficiency, verification, compression
|
A method is proposed that can reduce the size of decision tree ensembles by orders of magnitude with negligible cost in accuracy.
| 16,090 | null |
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] |
https://github.com/ML-KULeuven/lop_compress
|
146 |
Time Series Representations with Hard-Coded Invariances
|
https://openreview.net/forum?id=SaKPKyjDp6
|
[
"Thibaut Germain",
"Chrysoula Kosma",
"Laurent Oudre"
] |
Poster
|
Automatically extracting robust representations from large and complex time series data is becoming imperative for several real-world applications. Unfortunately, the potential of common neural network architectures in capturing invariant properties of time series remains relatively underexplored. For instance, convolutional layers often fail to capture underlying patterns in time series inputs that encompass strong deformations, such as trends. Indeed, invariances to some deformations may be critical for solving complex time series tasks, such as classification, while guaranteeing good generalization performance.
To address these challenges, we mathematically formulate and technically design efficient and hard-coded *invariant convolutions* for specific group actions applicable to the case of time series.
We construct these convolutions by considering specific sets of deformations commonly observed in time series, including *scaling*, *offset shift*, and *trend*.
We further combine the proposed invariant convolutions with standard convolutions in single embedding layers, and we showcase the layer capacity to capture complex invariant time series properties in several scenarios.
|
Time Series, Invariances, Neural Networks, Convolutions
|
Mathematical formalism of time series invariances and design of hard-coded invariant convolutions to commonly observed deformations.
| 16,086 | null |
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] |
https://github.com/sissykosm/TS-InvConv
|
147 |
Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning
|
https://openreview.net/forum?id=hrBfufwMzg
|
[
"Mengmeng Chen",
"Xiaohu Wu",
"QIQI LIU",
"Tiantian He",
"Yew-Soon Ong",
"Yaochu Jin",
"Qicheng Lao",
"Han Yu"
] |
Poster
|
Multi-objective optimization (MOO) exists extensively in machine learning, and aims to find a set of Pareto-optimal solutions, called the Pareto front, e.g., it is fundamental for multiple avenues of research in federated learning (FL). Pareto-Front Learning (PFL) is a powerful method implemented using Hypernetworks (PHNs) to approximate the Pareto front. This method enables the acquisition of a mapping function from a given preference vector to the solutions on the Pareto front. However, most existing PFL approaches still face two challenges: (a) sampling rays in high-dimensional spaces; (b) failing to cover the entire Pareto Front which has a convex shape. Here, we introduce a novel PFL framework, called as PHN-HVVS, which decomposes the design space into Voronoi grids and deploys a genetic algorithm (GA) for Voronoi grid partitioning within high-dimensional space. We put forward a new loss function, which effectively contributes to more extensive coverage of the resultant Pareto front and maximizes the HV Indicator. Experimental results on multiple MOO machine learning tasks demonstrate that PHN-HVVS outperforms the baselines significantly in generating Pareto front. Also, we illustrate that PHN-HVVS advances the methodologies of several recent problems in the FL field. The code is available at
https://github.com/buptcmm/phnhvvs.
|
Hypernetwork, Multi-objective optimization, Hypervolume, Collaborative federated learning
| null | 16,040 |
2505.20648
|
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] |
https://github.com/buptcmm/phnhvvs
|
148 |
Optimal Sensor Scheduling and Selection for Continuous-Discrete Kalman Filtering with Auxiliary Dynamics
|
https://openreview.net/forum?id=CAPNgWkEEk
|
[
"Mohamad Al Ahdab",
"john leth",
"Zheng-Hua Tan"
] |
Poster
|
We study the Continuous-Discrete Kalman Filter (CD-KF) for State-Space Models (SSMs) where continuous-time dynamics are observed via multiple sensors with discrete, irregularly timed measurements. Our focus extends to scenarios in which the measurement process is coupled with the states of an auxiliary SSM. For instance, higher measurement rates may increase energy consumption or heat generation, while a sensor’s accuracy can depend on its own spatial trajectory or that of the measured target. Each sensor thus carries distinct costs and constraints associated with its measurement rate and additional constraints and costs on the auxiliary state. We model measurement occurrences as independent Poisson processes with sensor-specific rates and derive an upper bound on the mean posterior covariance matrix of the CD-KF along the mean auxiliary state. The bound is continuously differentiable with respect to the measurement rates, which enables efficient gradient-based optimization. Exploiting this bound, we propose a finite-horizon optimal control framework to optimize measurement rates and auxiliary-state dynamics jointly. We further introduce a deterministic method for scheduling measurement times from the optimized rates. Empirical results in state-space filtering and dynamic temporal Gaussian process regression demonstrate that our approach achieves improved trade-offs between resource usage and estimation accuracy.
|
Kalman Filtering, Sensor Scheduling, Bayesian State-Space Models, Control
|
Scheduling and selecting sensor measurements under varying enviroments in a continuous-discrete kalman filter setup.
| 16,017 | null |
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0.015377126634120941,
0.009131666272878647,
-0.020228061825037003,
0.01575477235019207
] |
https://github.com/MOHAMMADZAHD93/When2measureKF
|
149 |
Fairness on Principal Stratum: A New Perspective on Counterfactual Fairness
|
https://openreview.net/forum?id=7jxa1o8rDW
|
[
"Haoxuan Li",
"Zeyu Tang",
"Zhichao Jiang",
"Zhuangyan Fang",
"Yue Liu",
"Zhi Geng",
"Kun Zhang"
] |
Poster
|
Fairness in human and algorithmic decision-making is crucial in areas such as criminal justice, education, and social welfare. Recently, counterfactual fairness has drawn increasing research interest, suggesting that decision-making for individuals should remain the same when intervening with different values on protected attributes. Nevertheless, the question of "which attributes and individuals should be protected" is rarely discussed in the existing counterfactual fairness literature. For example, when considering leg disability as a protected attribute, the algorithms should not treat individuals with leg disabilities differently in college admissions, but one may naturally consider this factor when selecting runner athletes. In other words, when and how to enforce fairness is expected to depend on the causal relation between the protected attribute and the outcome of interest. Formally, this paper proposes principal counterfactual fairness using the concept of principal stratification from the causal inference literature, focusing on whether an algorithm is counterfactually fair for individuals whose protected attribute has no individual causal effect on the outcome of interest. To examine whether an algorithm satisfies principal counterfactual fairness, we derive the statistical bounds and propose a post-processing approach to achieving principal counterfactual fairness with minimal individual decision changes. Experiments are conducted using synthetic and real-world datasets to verify the effectiveness of our methods.
|
Counterfactual fairness, Principal strata
| null | 16,009 | null |
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] | null |
150 |
Exponential Family Variational Flow Matching for Tabular Data Generation
|
https://openreview.net/forum?id=kjtvCSkSsy
|
[
"Andrés Guzmán-Cordero",
"Floor Eijkelboom",
"Jan-Willem van de Meent"
] |
Poster
|
While denoising diffusion and flow matching have driven major advances in generative modeling, their application to tabular data remains limited, despite its ubiquity in real-world applications.
To this end, we develop *TabbyFlow*, a variational Flow Matching (VFM) method for tabular data generation.
To apply VFM to data with mixed continuous and discrete features, we introduce **Exponential Family Variational Flow Matching (EF-VFM)**, which represents heterogeneous data types using a general exponential family distribution.
We hereby obtain an efficient, data-driven objective based on moment matching, enabling principled learning of probability paths over mixed continuous and discrete variables.
We also establish a connection between variational flow matching and generalized flow matching objectives based on Bregman divergences.
Evaluation on tabular data benchmarks demonstrates state-of-the-art performance compared to baselines.
|
Variational Flow Matching, Exponential Families, Tabular Data
|
TabbyFlow extends flow matching to tabular data generation by leveraging exponential family distributions to handle mixed data types efficiently.
| 16,008 |
2506.05940
|
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] | null |
151 |
LLMs Can Reason Faster Only If We Let Them
|
https://openreview.net/forum?id=uTv5rOPZr4
|
[
"Bilgehan Sel",
"Lifu Huang",
"Naren Ramakrishnan",
"Ruoxi Jia",
"Ming Jin"
] |
Poster
|
Large language models (LLMs) are making inroads into classical AI problems such as automated planning, yet key shortcomings continue to hamper their integration. Chain-of-Thought (CoT) struggles in complex multi-step reasoning, and Tree-of-Thoughts requires multiple queries that increase computational overhead. Recently, Algorithm-of-Thoughts (AoT) have shown promise using in-context examples, at the cost of significantly longer solutions compared to CoT. Aimed at bridging the solution length gap between CoT and AoT, this paper introduces AoT-O3, which combines supervised finetuning on AoT-style plans with a reinforcement learning (RL) framework designed to reduce solution length. The RL component uses a reward model that favors concise, valid solutions while maintaining planning accuracy. Empirical evaluations indicate that AoT-O3 shortens solution length by up to 80\% compared to baseline AoT while maintaining or surpassing prior performance. These findings suggest a promising pathway for more efficient, scalable LLM-based planning.
|
large language models, decision-making, planning
|
Enabling faster large language model solutions for autonomous reasoning and planning
| 15,998 | null |
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] | null |
152 |
Controlled Generation with Equivariant Variational Flow Matching
|
https://openreview.net/forum?id=YSVSMV0lXQ
|
[
"Floor Eijkelboom",
"Heiko Zimmermann",
"Sharvaree Vadgama",
"Erik J Bekkers",
"Max Welling",
"Christian A. Naesseth",
"Jan-Willem van de Meent"
] |
Poster
|
We derive a controlled generation objective within the framework of Variational Flow Matching (VFM),
which casts flow matching as a variational inference problem.
We demonstrate that controlled generation can be implemented two ways: (1) by way of end-to-end training of conditional generative models, or (2) as a Bayesian inference problem, enabling post hoc control of unconditional models without retraining.
Furthermore, we establish the conditions required for equivariant generation and provide an equivariant formulation of VFM tailored for molecular generation, ensuring invariance to rotations, translations, and permutations.
We evaluate our approach on both uncontrolled and controlled molecular generation, achieving state-of-the-art performance on uncontrolled generation and outperforming state-of-the-art models in controlled generation, both with end-to-end training and in the Bayesian inference setting.
This work strengthens the connection between flow-based generative modeling and Bayesian inference, offering a scalable and principled framework for constraint-driven and symmetry-aware generation.
|
Variational Flow Matching, Conditional Generation, Equivariance, Molecular Generation
|
We propose Variational Flow Matching for controlled generation, unifying conditional training and post hoc Bayesian inference with an equivariant formulation for molecular generation.
| 15,993 |
2506.18340
|
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] | null |
153 |
MAGELLAN: Metacognitive predictions of learning progress guide autotelic LLM agents in large goal spaces
|
https://openreview.net/forum?id=hRMAo5N66M
|
[
"Loris Gaven",
"Thomas Carta",
"Clément ROMAC",
"Cédric Colas",
"sylvain lamprier",
"Olivier Sigaud",
"Pierre-Yves Oudeyer"
] |
Poster
|
Open-ended learning agents must efficiently prioritize goals in vast possibility spaces, focusing on those that maximize learning progress (LP). When such autotelic exploration is achieved by LLM agents trained with online RL in high-dimensional and evolving goal spaces, a key challenge for LP prediction is modeling one’s own competence, a form of metacognitive monitoring. Traditional approaches either require extensive sampling or rely on brittle expert-defined goal groupings. We introduce MAGELLAN, a metacognitive framework that lets LLM agents learn to predict their competence and learning progress online. By capturing semantic relationships between goals, MAGELLAN enables sample-efficient LP estimation and dynamic adaptation to evolving goal spaces through generalization. In an interactive learning environment, we show that MAGELLAN improves LP prediction efficiency and goal prioritization, being the only method allowing the agent to fully master a large and evolving goal space. These results demonstrate how augmenting LLM agents with a metacognitive ability for LP predictions can effectively scale curriculum learning to open-ended goal spaces.
|
LLM agents, Open-Ended Learning, Learning Progress, Goal-conditionned RL, Automatic Curriculum Learning
|
We introduce MAGELLAN, a metacognitive framework that lets LLM agents learn to predict their competence and learning progress online to guide their curriculum in large goal spaces.
| 15,991 |
2502.07709
|
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] |
https://github.com/flowersteam/MAGELLAN
|
154 |
The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback
|
https://openreview.net/forum?id=OmQcPgq9RN
|
[
"Côme Fiegel",
"Pierre Menard",
"Tadashi Kozuno",
"Michal Valko",
"Vianney Perchet"
] |
Poster
|
We study the problem of learning in zero-sum matrix games with repeated play and bandit feedback.
Specifically, we focus on developing uncoupled algorithms that guarantee, without communication between players, convergence of the last-iterate to a Nash equilibrium. Although the non-bandit case has been studied extensively, this setting has only been explored recently, with a bound of $\mathcal{O}(T^{-1/8})$ on the exploitability gap. We show that, for uncoupled algorithms, guaranteeing convergence of the policy profiles to a Nash equilibrium is detrimental to the performances, with the best attainable rate being $\mathcal{O}(T^{-1/4})$ in contrast to the usual $\mathcal{O}(T^{-1/2})$ rate for convergence of the average iterates. We then propose two algorithms that achieve this optimal rate. The first algorithm leverages a straightforward tradeoff between exploration and exploitation, while the second employs a regularization technique based on a two-step mirror descent approach.
|
Game theory, Bandit, Last-iterate convergence, Online learning
|
We show that learning how to play a game is sometimes harder when using uncoupled algorithms.
| 15,967 | null |
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] | null |
155 |
Towards Efficient Online Tuning of VLM Agents via Counterfactual Soft Reinforcement Learning
|
https://openreview.net/forum?id=H76PMm7hf2
|
[
"Lang Feng",
"Weihao Tan",
"Zhiyi Lyu",
"Longtao Zheng",
"Haiyang Xu",
"Ming Yan",
"Fei Huang",
"Bo An"
] |
Poster
|
Online fine-tuning vision-language model (VLM) agents with reinforcement learning (RL) has shown promise for equipping agents with multi-step, goal-oriented capabilities in dynamic environments. However, their open-ended textual action space and non-end-to-end nature of action generation present significant challenges to effective online exploration in RL, e.g., explosion of the exploration space. We propose a novel online fine-tuning method, Counterfactual Soft Reinforcement Learning (CoSo), better suited to the textual output space of VLM agents. Compared to prior methods that assign uniform uncertainty to all tokens, CoSo leverages counterfactual reasoning to dynamically assess the causal influence of individual tokens on post-processed actions. By prioritizing the exploration of action-critical tokens while reducing the impact of semantically redundant or low-impact tokens, CoSo enables a more targeted and efficient online rollout process. We provide theoretical analysis proving CoSo's convergence and policy improvement guarantees, and extensive empirical evaluations supporting CoSo's effectiveness. Our results across a diverse set of agent tasks, including Android device control, card gaming, and embodied AI, highlight its remarkable ability to enhance exploration efficiency and deliver consistent performance gains. The code is available at https://github.com/langfengQ/CoSo.
|
vision-language model, agent, reinforcement learning, online fine-tuning, counterfactual
| null | 15,959 |
2505.03792
|
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] |
https://github.com/langfengQ/CoSo
|
156 |
Towards Attributions of Input Variables in a Coalition
|
https://openreview.net/forum?id=h5TXCnnEyy
|
[
"Xinhao Zheng",
"Huiqi Deng",
"Quanshi Zhang"
] |
Poster
|
This paper focuses on the fundamental challenge of partitioning input variables in attribution methods for Explainable AI, particularly in Shapley value-based approaches. Previous methods always compute attributions given a predefined partition but lack theoretical guidance on how to form meaningful variable partitions. We identify that attribution conflicts arise when the attribution of a coalition differs from the sum of its individual variables' attributions. To address this, we analyze the numerical effects of AND-OR interactions in AI models and extend the Shapley value to a new attribution metric for variable coalitions. Our theoretical findings reveal that specific interactions cause attribution conflicts, and we propose three metrics to evaluate coalition faithfulness. Experiments on synthetic data, NLP, image classification, and the game of Go validate our approach, demonstrating consistency with human intuition and practical applicability.
|
Attribution methods, Shapley value
|
This paper proves the internal mechanism for the conflict of attributions computed under different partitions of input variables.
| 15,947 |
2309.13411
|
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] | null |
157 |
Neural Guided Diffusion Bridges
|
https://openreview.net/forum?id=4LClOWTAth
|
[
"Gefan Yang",
"Frank van der Meulen",
"Stefan Sommer"
] |
Poster
|
We propose a novel method for simulating conditioned diffusion processes (diffusion bridges) in Euclidean spaces. By training a neural network to approximate bridge dynamics, our approach eliminates the need for computationally intensive Markov Chain Monte Carlo (MCMC) methods or reverse-process modeling. Compared to existing methods, it offers greater robustness across various diffusion specifications and conditioning scenarios. This applies in particular to rare events and multimodal distributions, which pose challenges for score-learning- and MCMC-based approaches. We propose a flexible variational family for approximating the diffusion bridge path measure which is partially specified by a neural network. Once trained, it enables efficient independent sampling at a cost comparable to sampling the unconditioned (forward) process.
|
Diffusion bridge, variational approximation, change of measure
| null | 15,945 |
2502.11909
|
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] |
https://github.com/bookdiver/neuralbridge
|
158 |
ELMO : Efficiency via Low-precision and Peak Memory Optimization in Large Output Spaces
|
https://openreview.net/forum?id=d6CTIPrTTC
|
[
"Jinbin Zhang",
"Nasib Ullah",
"Erik Schultheis",
"Rohit Babbar"
] |
Poster
|
Large output spaces, also referred to as Extreme multilabel classification (XMC), is a setting that arises, e.g., in large-scale tagging and product-to-product recommendation, and is characterized by the number of labels ranging from hundreds of thousands to millions. This means that the linear classification head, usually only a tiny fraction of the overall model, turns into the main driver for compute and memory demand. Current state-of-the-art XMC methods predominantly rely on FP16-FP32 mixed-precision training, which we show can be unstable, and inefficient in terms of memory usage and computational overhead. Meanwhile, existing low-precision methods typically retain higher precision for the classification layer. In this work, we propose ELMO, a pure low-precision training framework for XMC models using BFloat16 and Float8 data types. By leveraging Kahan summation and stochastic rounding, we demonstrate that XMC models can be effectively trained entirely in Float8, without relying on single-precision master weights or tensor scaling. Low-precision training, combined with our proposed memory optimizations---gradient fusion and chunking---enables significant reductions in GPU memory usage. For example, we train a 3-million-label XMC model with only 6.6 GiB of GPU memory, compared to the 39.7GiB required by the optimized SOTA method, Renee without compromising accuracy.
|
Extreme Multi-Label Classification, Low-Precision Training, Peak Memory Optimization, FLOAT8 training
|
We propose a pure low-precision training framework for XMC models using BFLOAT16 and FP8, achieving significant GPU memory savings while competing baselines on most public datasets under low-bitwidth constraints.
| 15,923 | null |
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] |
https://github.com/xmc-aalto/elmo
|
159 |
Hyper-Transforming Latent Diffusion Models
|
https://openreview.net/forum?id=yhgcRwJ9Dn
|
[
"Ignacio Peis",
"Batuhan Koyuncu",
"Isabel Valera",
"Jes Frellsen"
] |
Poster
|
We introduce a novel generative framework for functions by integrating Implicit Neural Representations (INRs) and Transformer-based hypernetworks into latent variable models. Unlike prior approaches that rely on MLP-based hypernetworks with scalability limitations, our method employs a Transformer-based decoder to generate INR parameters from latent variables, addressing both representation capacity and computational efficiency. Our framework extends latent diffusion models (LDMs) to INR generation by replacing standard decoders with a Transformer-based hypernetwork, which can be trained either from scratch or via hyper-transforming—a strategy that fine-tunes only the decoder while freezing the pre-trained latent space. This enables efficient adaptation of existing generative models to INR-based representations without requiring full retraining. We validate our approach across multiple modalities, demonstrating improved scalability, expressiveness, and generalization over existing INR-based generative models. Our findings establish a unified and flexible framework for learning structured function representations.
|
Latent Diffusion Models, Transformers, INRs
|
We propose a generative framework for INRs that integrates a Transformer-based hypernetwork decoder into latent diffusion models, enabling scalable INR generation and efficient adaptation via hyper-transforming, which fine-tunes only the decoder.
| 15,913 |
2504.16580
|
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] |
https://github.com/ipeis/LDMI
|
160 |
Emergent Response Planning in LLMs
|
https://openreview.net/forum?id=Ce79P8ULPY
|
[
"Zhichen Dong",
"Zhanhui Zhou",
"Zhixuan Liu",
"Chao Yang",
"Chaochao Lu"
] |
Poster
|
In this work, we argue that large language models (LLMs), though trained to predict only the next token, exhibit emergent planning behaviors: $\textbf{their hidden representations encode future outputs beyond the next token}$. Through simple probing, we demonstrate that LLM prompt representations encode global attributes of their entire responses, including $\textit{structure attributes}$ (e.g., response length, reasoning steps), $\textit{content attributes}$ (e.g., character choices in storywriting, multiple-choice answers at the end of response), and $\textit{behavior attributes}$ (e.g., answer confidence, factual consistency). In addition to identifying response planning, we explore how it scales with model size across tasks and how it evolves during generation. The findings that LLMs plan ahead for the future in their hidden representations suggest potential applications for improving transparency and generation control.
|
Large Language Models, Emergent Planning, Model Probing and Hidden Representations
|
This paper shows that Large Language Models (LLMs) exhibit emergent response planning, as their internal hidden representations encode predictable, global attributes of their entire future output.
| 15,911 |
2502.06258
|
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] | null |
161 |
Explicit Preference Optimization: No Need for an Implicit Reward Model
|
https://openreview.net/forum?id=iXvm0zvspb
|
[
"Xiangkun Hu",
"Lemin Kong",
"Tong He",
"David Wipf"
] |
Poster
|
The generated responses of large language models (LLMs) are often fine-tuned to human preferences through a process called reinforcement learning from human feedback (RLHF). As RLHF relies on a challenging training sequence, whereby a separate reward model is independently learned and then later applied to LLM policy updates, ongoing research effort has targeted more straightforward alternatives. In this regard, direct preference optimization (DPO) and its many offshoots circumvent the need for a separate reward training step. Instead, through the judicious use of a reparameterization trick that induces an implicit reward, DPO and related methods consolidate learning to the minimization of a single loss function. And yet despite demonstrable success in some real-world settings, we prove that DPO-based objectives are nonetheless subject to sub-optimal regularization and counter-intuitive interpolation behaviors, underappreciated artifacts of the reparameterizations upon which they are based. To this end, we introduce an explicit preference optimization framework termed EXPO that requires no analogous reparameterization to achieve an implicit reward. Quite differently, we merely posit intuitively-appealing regularization factors from scratch that transparently avoid the potential pitfalls of key DPO variants, provably satisfying regularization desiderata that prior methods do not. Empirical results serve to corroborate our analyses and showcase the efficacy of EXPO.
|
direct preference optimization, reinforcement learning from human feedback, preference alignment, regularized regression
|
This paper introduces and analyzes an alternative to direct preference optimization that relies on no implicit reward.
| 15,908 |
2506.07492
|
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] |
https://github.com/lmkong020/explicit-preference-optimization
|
162 |
CaDA: Cross-Problem Routing Solver with Constraint-Aware Dual-Attention
|
https://openreview.net/forum?id=CS4RyQuTig
|
[
"Han Li",
"Fei Liu",
"Zhi Zheng",
"Yu Zhang",
"Zhenkun Wang"
] |
Poster
|
Vehicle routing problems (VRPs) are significant combinatorial optimization problems (COPs) holding substantial practical importance. Recently, neural combinatorial optimization (NCO), which involves training deep learning models on extensive data to learn vehicle routing heuristics, has emerged as a promising approach due to its efficiency and the reduced need for manual algorithm design. However, applying NCO across diverse real-world scenarios with various constraints necessitates cross-problem capabilities. Current cross-problem NCO methods for VRPs typically employ a constraint-unaware model, limiting their cross-problem performance. Furthermore, they rely solely on global connectivity, which fails to focus on key nodes and leads to inefficient representation learning. This paper introduces a \underline{C}onstraint-\underline{A}ware \underline{D}ual-\underline{A}ttention Model (CaDA), designed to address these limitations. CaDA incorporates a constraint prompt that efficiently represents different problem variants. Additionally, it features a dual-attention mechanism with a global branch for capturing broader graph-wide information and a sparse branch that selectively focuses on the key node connections. We comprehensively evaluate our model on 16 different VRPs and compare its performance against existing cross-problem VRP solvers. CaDA achieves state-of-the-art results across all tested VRPs. Our ablation study confirms that each component contributes to its cross-problem learning performance. The source code for CaDA is publicly available at \url{https://github.com/CIAM-Group/CaDA}.
|
Vehicle Routing Problem, Multi-Task Learning, Task-Specific Prompt, Dual Attention Mechanism, Cross-Problem Learning
| null | 15,901 |
2412.00346
|
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] |
https://github.com/CIAM-Group/CaDA
|
163 |
Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory
|
https://openreview.net/forum?id=GDvO6viRCF
|
[
"Dominik Fuchsgruber",
"Tom Wollschläger",
"Johannes Bordne",
"Stephan Günnemann"
] |
Poster
|
While uncertainty estimation for graphs recently gained traction, most methods rely on homophily and deteriorate in heterophilic settings.
We address this by analyzing message passing neural networks from an information-theoretic perspective and developing a suitable analog to data processing inequality to quantify information throughout the model's layers. In contrast to non-graph domains, information about the node-level prediction target can *increase* with model depth if a node's features are semantically different from its neighbors.
Therefore, on heterophilic graphs, the latent embeddings of an MPNN each provide different information about the data distribution - different from homophilic settings.
This reveals that considering all node representations simultaneously is a key design principle for epistemic uncertainty estimation on graphs beyond homophily.
We empirically confirm this with a simple post-hoc density estimator on the joint node embedding space that provides state-of-the-art uncertainty on heterophilic graphs. At the same time, it matches prior work on homophilic graphs without explicitly exploiting homophily through post-processing.
|
Heterophilic Graphs, Uncertainty Estimation, Information Theory, Graph Neural Networks
|
Information theory based joint density estimatior on the hidden representations to quantify uncertainty for heterophilic graphs.
| 15,892 |
2505.22152
|
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] | null |
164 |
Near Optimal Best Arm Identification for Clustered Bandits
|
https://openreview.net/forum?id=3Jr5Al16MS
|
[
"Yash",
"Avishek Ghosh",
"Nikhil Karamchandani"
] |
Poster
|
This work investigates the problem of best arm identification for multi-agent multi-armed bandits. We consider $N$ agents grouped into $M$ clusters, where each cluster solves a stochastic bandit problem. The mapping between agents and bandits is \textit{a priori} unknown. Each bandit is associated with $K$ arms, and the goal is to identify the best arm for each agent under a $\delta$-probably correct ($\delta$-PC) framework, while minimizing sample complexity and communication overhead. We propose two novel algorithms: \emph{Clustering then Best Arm Identification} (\texttt{Cl-BAI}) and \emph{Best Arm Identification then Clustering} (\texttt{BAI-Cl}). \texttt{Cl-BAI} employs a two-phase approach that first clusters agents based on the bandit problems they are learning, followed by identifying the best arm for each cluster. \texttt{BAI-Cl} reverses the sequence by identifying the best arms first and then clustering agents accordingly. Both algorithms exploit the successive elimination framework to ensure computational efficiency and high accuracy. Theoretical analysis establishes $\delta$-PC guarantees for both methods, derives bounds on their sample complexity, and provides a lower bound for the problem class. Moreover, when $M$ is small (a constant), we show that the sample complexity of (a variant of) \texttt{BAI-Cl} is (order-wise) minimax optimal. Experiments on synthetic and real-world (Movie Lens, Yelp) data demonstrates the superior performance of the proposed algorithms in terms of sample and communication efficiency, particularly in settings where $M \ll N$.
|
Best Arm Identification, Multi-Armed Bandits (MAB), Clustered Bandits, Pure Exploration
|
We address the best arm identification problem for Federated (Distributed) Bandits
| 15,890 |
2505.10147
|
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] | null |
165 |
High Probability Bound for Cross-Learning Contextual Bandits with Unknown Context Distributions
|
https://openreview.net/forum?id=rTPq8VzhmZ
|
[
"Ruiyuan Huang",
"Zengfeng Huang"
] |
Poster
|
Motivated by applications in online bidding and sleeping bandits, we examine the problem of contextual bandits with cross learning, where the learner observes the loss associated with the action across all possible contexts, not just the current round’s context. Our focus is on a setting where losses are chosen adversarially, and contexts are sampled i.i.d. from a specific distribution. This problem was first studied by Balseiro et al. (2019), who proposed an algorithm that achieves near-optimal regret under the assumption that the context distribution is known in advance. However, this assumption is often unrealistic. To address this issue, Schneider & Zimmert (2023) recently proposed a new algorithm that achieves nearly optimal expected regret. It is well-known that expected regret can be significantly weaker than high-probability bounds. In this paper, we present a novel, in-depth analysis of their algorithm and demonstrate that it actually achieves near-optimal regret with high probability. There are steps in the original analysis by Schneider & Zimmert (2023) that lead only to an expected bound by nature. In our analysis, we introduce several new insights. Specifically, we make extensive use of the weak dependency structure between different epochs, which was overlooked in previous analyses. Additionally, standard martingale inequalities are not directly applicable, so we refine martingale inequalities to complete our analysis.
|
bandit, contextual bandit, cross-learning, high probability bounds
|
We give a nearly optimal high probability bound for the cross-learning contextual bandits with unknown context distributions.
| 15,881 |
2410.04080
|
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] | null |
166 |
FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making
|
https://openreview.net/forum?id=UTT5OTyIWm
|
[
"Yucen Wang",
"Rui Yu",
"Shenghua Wan",
"Le Gan",
"De-Chuan Zhan"
] |
Poster
|
Foundation Models (FMs) and World Models (WMs) offer complementary strengths in task generalization at different levels. In this work, we propose FOUNDER, a framework that integrates the generalizable knowledge embedded in FMs with the dynamic modeling capabilities of WMs to enable open-ended task solving in embodied environments in a reward-free manner. We learn a mapping function that grounds FM representations in the WM state space, effectively inferring the agent's physical states in the world simulator from external observations. This mapping enables the learning of a goal-conditioned policy through imagination during behavior learning, with the mapped task serving as the goal state. Our method leverages the predicted temporal distance to the goal state as an informative reward signal. FOUNDER demonstrates superior performance on various multi-task offline visual control benchmarks, excelling in capturing the deep-level semantics of tasks specified by text or videos, particularly in scenarios involving complex observations or domain gaps where prior methods struggle. The consistency of our learned reward function with the ground-truth reward is also empirically validated. Our project website is https://sites.google.com/view/founder-rl.
|
Reinforcement Learning, World Models, Foundation Models, Open-ended Tasks, Goal-Conditioned Reinforcement Learning, Multi-task Reinforcement Learning, Learning Reward Functions
|
We propose FOUNDER that grounds Foundation Model task representations into World Model goal states, enabling open-ended task specification and completion in embodied environments by capturing deep-level task semantics.
| 15,878 | null |
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] | null |
167 |
Understanding Sharpness Dynamics in NN Training with a Minimalist Example: The Effects of Dataset Difficulty, Depth, Stochasticity, and More
|
https://openreview.net/forum?id=XfjrLEPOQV
|
[
"Geonhui Yoo",
"Minhak Song",
"Chulhee Yun"
] |
Poster
|
When training deep neural networks with gradient descent, sharpness often increases---a phenomenon known as *progressive sharpening*---before saturating at the *edge of stability*. Although commonly observed in practice, the underlying mechanisms behind progressive sharpening remain poorly understood. In this work, we study this phenomenon using a minimalist model: a deep linear network with a single neuron per layer. We show that this simple model effectively captures the sharpness dynamics observed in recent empirical studies, offering a simple testbed to better understand neural network training. Moreover, we theoretically analyze how dataset properties, network depth, stochasticity of optimizers, and step size affect the degree of progressive sharpening in the minimalist model. We then empirically demonstrate how these theoretical insights extend to practical scenarios. This study offers a deeper understanding of sharpness dynamics in neural network training, highlighting the interplay between depth, training data, and optimizers.
|
progressive sharpening, sharpness
|
We propose a minimalist model that successfully replicates the progressive sharpening and edge of stability phenomena, and empirically and theoretically analyze the effect of problem parameters in progressive sharpening
| 15,877 |
2506.06940
|
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] |
https://github.com/Yoogeonhui/understand_progressive_sharpening
|
168 |
Comparing Few to Rank Many: Active Human Preference Learning Using Randomized Frank-Wolfe Method
|
https://openreview.net/forum?id=cUNfm13VUR
|
[
"Kiran Koshy Thekumparampil",
"Gaurush Hiranandani",
"Kousha Kalantari",
"Shoham Sabach",
"Branislav Kveton"
] |
Poster
|
We study learning human preferences from limited comparison feedback, a core machine learning problem that is at the center of reinforcement learning from human feedback (RLHF). We formulate the problem as learning a Plackett-Luce (PL) model from a limited number of $K$-subset comparisons over a universe of $N$ items, where typically $K \ll N$. Our objective is to select the $K$-subsets such that all items can be ranked with minimal mistakes within the budget. We solve the problem using the D-optimal design, which minimizes the worst-case ranking loss under the estimated PL model. All known algorithms for this problem are computationally infeasible in our setting because we consider exponentially many subsets in $K$. To address this challenge, we propose a randomized Frank-Wolfe algorithm with memoization and sparse updates that has a low $O(N^2 + K^2)$ per-iteration complexity. We analyze it and demonstrate its empirical superiority on synthetic and open-source NLP datasets.
|
active learning, human preference learning, comparison feedback, optimal design, Frank-Wolfe method
|
We propose an efficient active learning algorithm for learning human preferences from K-way comparison of a large number of choices.
| 15,875 | null |
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] |
https://github.com/tkkiran/DopeWolfe
|
169 |
Vector Grimoire: Codebook-based Shape Generation under Raster Image Supervision
|
https://openreview.net/forum?id=xYtLsWiUli
|
[
"Marco Cipriano",
"Moritz Feuerpfeil",
"Gerard de Melo"
] |
Poster
|
Scalable Vector Graphics (SVG) is a popular format on the web and in the design industry. However, despite the great strides made in generative modeling, SVG has remained underexplored due to the discrete and complex nature of such data. We introduce GRIMOIRE, a text-guided SVG generative model that is comprised of two modules: A Visual Shape Quantizer (VSQ) learns to map raster images onto a discrete codebook by reconstructing them as vector shapes, and an Auto-Regressive Transformer (ART) models the joint probability distribution over shape tokens, positions and textual descriptions, allowing us to generate vector graphics from natural language. Unlike existing models that require direct supervision from SVG data, GRIMOIRE learns shape image patches using only raster image supervision which opens up vector generative modeling to significantly more data. We demonstrate the effectiveness of our method by fitting GRIMOIRE for closed filled shapes on the MNIST and Emoji, and for outline strokes on icon and font data, surpassing previous image-supervised methods in generative quality and vector-supervised approach in flexibility.
|
Image Generation, Scalable Vector Graphics, VQ-VAE, Differentiable Rasterizer
|
We propose a text-guided SVG generative model that creates vector graphics from natural language descriptions, learning from raster images without direct SVG supervision
| 15,874 |
2410.05991
|
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] |
https://github.com/potpov/VectorGrimoire
|
170 |
Score-Based Diffusion Policy Compatible with Reinforcement Learning via Optimal Transport
|
https://openreview.net/forum?id=2dqiqST8ZJ
|
[
"Mingyang Sun",
"Pengxiang Ding",
"Weinan Zhang",
"Donglin Wang"
] |
Poster
|
Diffusion policies have shown promise in learning complex behaviors from demonstrations, particularly for tasks requiring precise control and long-term planning. However, they face challenges in robustness when encountering distribution shifts. This paper explores improving diffusion-based imitation learning models through online interactions with the environment. We propose OTPR (Optimal Transport-guided score-based diffusion Policy for Reinforcement learning fine-tuning), a novel method that integrates diffusion policies with RL using optimal transport theory. OTPR leverages the Q-function as a transport cost and views the policy as an optimal transport map, enabling efficient and stable fine-tuning. Moreover, we introduce masked optimal transport to guide state-action matching using expert keypoints and a compatibility-based resampling strategy to enhance training stability. Experiments on three simulation tasks demonstrate OTPR's superior performance and robustness compared to existing methods, especially in complex and sparse-reward environments. In sum, OTPR provides an effective framework for combining IL and RL, achieving versatile and reliable policy learning.
|
Diffusion Policy, Reinforcement Learning, Optimal Transport
| null | 15,847 |
2502.12631
|
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] |
https://github.com/sunmmyy/otpr
|
171 |
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity
|
https://openreview.net/forum?id=UNrfYfbLZ3
|
[
"Alessandro Pierro",
"Steven Abreu",
"Jonathan Timcheck",
"Philipp Stratmann",
"Andreas Wild",
"Sumit Bam Shrestha"
] |
Poster
|
Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge, but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption.
Unstructured sparsity offers a compelling solution, enabling substantial reductions in compute and memory requirements--when accelerated by compatible hardware platforms.
In this paper, we conduct a scaling study to investigate the Pareto front of performance and efficiency across inference compute budgets.
We find that highly sparse linear RNNs *consistently* achieve better efficiency-performance trade-offs than dense baselines, with $2\times$ less compute and $36$\% less memory at iso-accuracy.
Our models achieve state-of-the-art results on a real-time streaming task for audio denoising.
By quantizing our sparse models to fixed-point arithmetic and deploying them on the Intel Loihi 2 neuromorphic chip for real-time processing, we translate model compression into tangible gains of $42\times$ lower latency and $149\times$ lower energy consumption compared to a dense model on an edge GPU.
Our findings showcase the transformative potential of unstructured sparsity, paving the way for highly efficient recurrent neural networks in real-world, resource-constrained environments.
|
Linear RNNs, Sparsity, Pruning, Quantization, Neuromorphic Hardware
| null | 15,842 |
2502.01330
|
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] |
https://github.com/IntelLabs/SparseRNNs
|
172 |
Complex Wavelet Mutual Information Loss: A Multi-Scale Loss Function for Semantic Segmentation
|
https://openreview.net/forum?id=iwkCnlOa2A
|
[
"Renhao Lu"
] |
Poster
|
Recent advancements in deep neural networks have significantly enhanced the performance of semantic segmentation. However, class imbalance and instance imbalance remain persistent challenges, where smaller instances and thin boundaries are often overshadowed by larger structures. To address the multiscale nature of segmented objects, various models have incorporated mechanisms such as spatial attention and feature pyramid networks. Despite these advancements, most loss functions are still primarily pixel-wise, while regional and boundary-focused loss functions often incur high computational costs or are restricted to small-scale regions. To address this limitation, we propose the complex wavelet mutual information (CWMI) loss, a novel loss function that leverages mutual information from subband images decomposed by a complex steerable pyramid. The complex steerable pyramid captures features across multiple orientations and preserves structural similarity across scales. Meanwhile, mutual information is well-suited to capturing high-dimensional directional features and offers greater noise robustness. Extensive experiments on diverse segmentation datasets demonstrate that CWMI loss achieves significant improvements in both pixel-wise accuracy and topological metrics compared to state-of-the-art methods, while introducing minimal computational overhead. Our code is available at https://github.com/lurenhaothu/CWMI
|
Semantic segmentation, wavelet transform, steerable pyramid, mutual information
|
We propose a novel loss function, CWMI, that enhances image segmentation by combining multi-scale wavelet analysis with mutual information, achieving better edge precision and robustness without extra computation.
| 15,833 |
2502.00563
|
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] |
https://github.com/lurenhaothu/CWMI
|
173 |
Balancing Model Efficiency and Performance: Adaptive Pruner for Long-tailed Data
|
https://openreview.net/forum?id=1d1ssNedLv
|
[
"Zhe Zhao",
"HaiBin Wen",
"Pengkun Wang",
"ShuangWang",
"Zhenkun Wang",
"Qingfu Zhang",
"Yang Wang"
] |
Poster
|
Long-tailed distribution datasets are prevalent in many machine learning tasks, yet existing neural network models still face significant challenges when handling such data. This paper proposes a novel adaptive pruning strategy, LTAP (Long-Tailed Adaptive Pruner), aimed at balancing model efficiency and performance to better address the challenges posed by long-tailed data distributions. LTAP introduces multi-dimensional importance scoring criteria and designs a dynamic weight adjustment mechanism to adaptively determine the pruning priority of parameters for different classes. By focusing on protecting parameters critical for tail classes, LTAP significantly enhances computational efficiency while maintaining model performance. This method combines the strengths of long-tailed learning and neural network pruning, overcoming the limitations of existing approaches in handling imbalanced data. Extensive experiments demonstrate that LTAP outperforms existing methods on various long-tailed datasets, achieving a good balance between model compression rate, computational efficiency, and classification accuracy. This research provides new insights into solving model optimization problems in long-tailed learning and is significant for improving the performance of neural networks on imbalanced datasets. The code is available at https://github.com/DataLab-atom/LT-VOTE.
|
Neural network pruning，Long-tail learning
| null | 15,821 | null |
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] |
https://github.com/DataLab-atom/LT-VOTE
|
174 |
Contour Integration Underlies Human-Like Vision
|
https://openreview.net/forum?id=ftR9OuiUJA
|
[
"Ben Lonnqvist",
"Elsa Scialom",
"Abdulkadir Gokce",
"Zehra Merchant",
"Michael Herzog",
"Martin Schrimpf"
] |
Poster
|
Despite the tremendous success of deep learning in computer vision, models still fall behind humans in generalizing to new input distributions. Existing benchmarks do not investigate the specific failure points of models by analyzing performance under many controlled conditions. Our study systematically dissects where and why models struggle with contour integration - a hallmark of human vision -- by designing an experiment that tests object recognition under various levels of object fragmentation. Humans (n=50) perform at high accuracy, even with few object contours present. This is in contrast to models which exhibit substantially lower sensitivity to increasing object contours, with most of the over 1,000 models we tested barely performing above chance. Only at very large scales ($\sim5B$ training dataset size) do models begin to approach human performance. Importantly, humans exhibit an integration bias - a preference towards recognizing objects made up of directional fragments over directionless fragments. We find that not only do models that share this property perform better at our task, but that this bias also increases with model training dataset size, and training models to exhibit contour integration leads to high shape bias. Taken together, our results suggest that contour integration is a hallmark of object vision that underlies object recognition performance, and may be a mechanism learned from data at scale.
|
psychophysics, machine vision, human vision, contour integration, robustness, visual perception
|
Models fail at human-like contour integration; those that integrate contours in a more human-like way also reach better accuracy and robustness.
| 15,810 |
2504.05253
|
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] | null |
175 |
Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings
|
https://openreview.net/forum?id=NOV32X1Rq3
|
[
"Rong-Xi Tan",
"Ming Chen",
"Ke Xue",
"Yao Wang",
"Yaoyuan Wang",
"Fu Sheng",
"Chao Qian"
] |
Poster
|
The pursuit of universal black-box optimization (BBO) algorithms is a longstanding goal. However, unlike domains such as language or vision, where scaling structured data has driven generalization, progress in offline BBO remains hindered by the lack of unified representations for heterogeneous numerical spaces. Thus, existing offline BBO approaches are constrained to single-task and fixed-dimensional settings, failing to achieve cross-domain universal optimization. Recent advances in language models (LMs) offer a promising path forward: their embeddings capture latent relationships in a unifying way, enabling universal optimization across different data types possible. In this paper, we discuss multiple potential approaches, including an end-to-end learning framework in the form of next-token prediction, as well as prioritizing the learning of latent spaces with strong representational capabilities. To validate the effectiveness of these methods, we collect offline BBO tasks and data from open-source academic works for training. Experiments demonstrate the universality and effectiveness of our proposed methods. Our findings suggest that unifying language model priors and learning string embedding space can overcome traditional barriers in universal BBO, paving the way for general-purpose BBO algorithms. The code is provided at https://github.com/lamda-bbo/universal-offline-bbo.
|
Universal optimization; Offline optimization
| null | 15,805 |
2506.07109
|
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] |
https://github.com/lamda-bbo/universal-offline-bbo
|
176 |
Nonlinear transformers can perform inference-time feature learning
|
https://openreview.net/forum?id=xQTSvP57C3
|
[
"Naoki Nishikawa",
"Yujin Song",
"Kazusato Oko",
"Denny Wu",
"Taiji Suzuki"
] |
Poster
|
Pretrained transformers have demonstrated the ability to implement various algorithms at inference time without parameter updates. While theoretical works have established this capability through constructions and approximation guarantees, the optimization and statistical efficiency aspects remain understudied. In this work, we investigate how transformers learn features in-context -- a key mechanism underlying their inference-time adaptivity. We focus on the in-context learning of single-index models $y=\sigma_*(\langle \\boldsymbol{x},\\boldsymbol{\beta}\rangle)$, which are low-dimensional nonlinear functions parameterized by feature vector $\\boldsymbol\beta$. We prove that transformers pretrained by gradient-based optimization can perform *inference-time feature learning*, i.e., extract information of the target features $\\boldsymbol{\beta}$ solely from test prompts (despite $\\boldsymbol
{\beta}$ varying across different prompts), hence achieving an in-context statistical efficiency that surpasses any non-adaptive (fixed-basis) algorithms such as kernel methods. Moreover, we show that the inference-time sample complexity surpasses the Correlational Statistical Query (CSQ) lower bound, owing to nonlinear label transformations naturally induced by the Softmax self-attention mechanism.
|
transformers, in-context learning, feature learning, single-index models
| null | 15,804 | null |
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] | null |
177 |
Bongard in Wonderland: Visual Puzzles that Still Make AI Go Mad?
|
https://openreview.net/forum?id=R65zHNqND0
|
[
"Antonia Wüst",
"Tim Tobiasch",
"Lukas Helff",
"Inga Ibs",
"Wolfgang Stammer",
"Devendra Singh Dhami",
"Constantin A. Rothkopf",
"Kristian Kersting"
] |
Poster
|
Recently, newly developed Vision-Language Models (VLMs), such as OpenAI's o1, have emerged, seemingly demonstrating advanced reasoning capabilities across text and image modalities. However, the depth of these advances in language-guided perception and abstract reasoning remains underexplored, and it is unclear whether these models can truly live up to their ambitious promises. To assess the progress and identify shortcomings, we enter the wonderland of Bongard problems, a set of classic visual reasoning puzzles that require human-like abilities of pattern recognition and abstract reasoning. With our extensive evaluation setup, we show that while VLMs occasionally succeed in identifying discriminative concepts and solving some of the problems, they frequently falter. Surprisingly, even elementary concepts that may seem trivial to humans, such as simple spirals, pose significant challenges. Moreover, when explicitly asked to recognize ground truth concepts, they continue to falter, suggesting not only a lack of understanding of these elementary visual concepts but also an inability to generalize to unseen concepts. We compare the results of VLMs to human performance and observe that a significant gap remains between human visual reasoning capabilities and machine cognition.
|
Visual Reasoning, Vision Language Models, Bongard problems
|
Diagnostic evaluation of VLMs on Bongard Problems that reveals problems in perception and reasoning.
| 15,802 |
2410.19546
|
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] |
https://github.com/ml-research/bongard-in-wonderland
|
178 |
Harmonizing Geometry and Uncertainty: Diffusion with Hyperspheres
|
https://openreview.net/forum?id=A82tIFgJaK
|
[
"Muskan Dosi",
"Chiranjeev Chiranjeev",
"Kartik Thakral",
"Mayank Vatsa",
"Richa Singh"
] |
Poster
|
Do contemporary diffusion models preserve the class geometry of hyperspherical data? Standard diffusion models rely on isotropic Gaussian noise in the forward process, inherently favoring Euclidean spaces. However, many real-world problems involve non-Euclidean distributions, such as hyperspherical manifolds, where class-specific patterns are governed by angular geometry within hypercones. When modeled in Euclidean space, these angular subtleties are lost, leading to suboptimal generative performance. To address this limitation, we introduce \textbf{HyperSphereDiff} to align hyperspherical structures with directional noise, preserving class geometry and effectively capturing angular uncertainty. We demonstrate both theoretically and empirically that this approach aligns the generative process with the intrinsic geometry of hyperspherical data, resulting in more accurate and geometry-aware generative models. We evaluate our framework on four object datasets and two face datasets, showing that incorporating angular uncertainty better preserves the underlying hyperspherical manifold.
|
Diffusion Model, vMF Distribution
|
Exploring diffusion to generate class-aware hyperspherical geometry by leveraging angular uncertainty.
| 15,800 |
2506.10576
|
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] |
https://github.com/IAB-IITJ/Harmonizing-Geometry-and-Uncertainty-Diffusion-with-Hyperspheres
|
179 |
Subgroups Matter for Robust Bias Mitigation
|
https://openreview.net/forum?id=P0RkH1RT5z
|
[
"Anissa Alloula",
"Charles Jones",
"Ben Glocker",
"Bartlomiej Papiez"
] |
Poster
|
Despite the constant development of new bias mitigation methods for machine learning, no method consistently succeeds, and a fundamental question remains unanswered: when and why do bias mitigation techniques fail? In this paper, we hypothesise that a key factor may be the often-overlooked but crucial step shared by many bias mitigation methods: the definition of subgroups. To investigate this, we conduct a comprehensive evaluation of state-of-the-art bias mitigation methods across multiple vision and language classification tasks, systematically varying subgroup definitions, including coarse, fine-grained, intersectional, and noisy subgroups. Our findings reveal that subgroup choice significantly impacts performance, with certain groupings paradoxically leading to worse outcomes than no mitigation at all. They suggest that observing a disparity between a set of subgroups is not a sufficient reason to use those subgroups for mitigation. Through theoretical analysis, we explain these phenomena and uncover a counter-intuitive insight that, in some cases, improving fairness with respect to a particular set of subgroups is best achieved by using a different set of subgroups for mitigation. Our work highlights the importance of careful subgroup definition in bias mitigation and presents it as an alternative lever for improving the robustness and fairness of machine learning models.
|
bias mitigation, robustness, spurious correlations, generalisation, fairness
|
Our work highlights the importance of careful subgroup definition in bias mitigation and suggest it as a alternative lever for improving the robustness and fairness of machine learning models.
| 15,799 |
2505.21363
|
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] |
https://github.com/anissa218/subgroups_bias_mit
|
180 |
Hyperbolic-PDE GNN: Spectral Graph Neural Networks in the Perspective of A System of Hyperbolic Partial Differential Equations
|
https://openreview.net/forum?id=UJXbcJ7qXB
|
[
"Juwei Yue",
"Haikuo Li",
"Jiawei Sheng",
"Xiaodong Li",
"Taoyu Su",
"Tingwen Liu",
"Li Guo"
] |
Poster
|
Graph neural networks (GNNs) leverage message passing mechanisms to learn the topological features of graph data. Traditional GNNs learns node features in a spatial domain unrelated to the topology, which can hardly ensure topological features. In this paper, we formulates message passing as a system of hyperbolic partial differential equations (hyperbolic PDEs), constituting a dynamical system that explicitly maps node representations into a particular solution space. This solution space is spanned by a set of eigenvectors describing the topological structure of graphs. Within this system, for any moment in time, a node features can be decomposed into a superposition of the basis of eigenvectors. This not only enhances the interpretability of message passing but also enables the explicit extraction of fundamental characteristics about the topological structure. Furthermore, by solving this system of hyperbolic partial differential equations, we establish a connection with spectral graph neural networks (spectral GNNs), serving as a message passing enhancement paradigm for spectral GNNs.We further introduce polynomials to approximate arbitrary filter functions. Extensive experiments demonstrate that the paradigm of hyperbolic PDEs not only exhibits strong flexibility but also significantly enhances the performance of various spectral GNNs across diverse graph tasks.
|
Graph Neural Networks, Hyperbolic Partial Differential Equations
| null | 15,796 |
2505.23014
|
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] |
https://github.com/yueawu/hyperbolic-gnn
|
181 |
Mind the Gap: A Practical Attack on GGUF Quantization
|
https://openreview.net/forum?id=TV17MLZGuA
|
[
"Kazuki Egashira",
"Robin Staab",
"Mark Vero",
"Jingxuan He",
"Martin Vechev"
] |
Poster
|
With the increasing size of frontier LLMs, post-training quantization has become the standard for memory-efficient deployment. Recent work has shown that basic rounding-based quantization schemes pose security risks, as they can be exploited to inject malicious behaviors into quantized models that remain hidden in full precision. However, existing attacks cannot be applied to more complex quantization methods, such as the GGUF family used in the popular ollama and llama.cpp frameworks. In this work, we address this gap by introducing the first attack on GGUF. Our key insight is that the quantization error -- the difference between the full-precision weights and their (de-)quantized version -- provides sufficient flexibility to construct malicious quantized models that appear benign in full precision. Leveraging this, we develop an attack that trains the target malicious LLM while constraining its weights based on quantization errors. We demonstrate the effectiveness of our attack on three popular LLMs across nine GGUF quantization data types on three diverse attack scenarios: insecure code generation ($\Delta$=$88.7\%$), targeted content injection ($\Delta$=$85.0\%$), and benign instruction refusal ($\Delta$=$30.1\%$). Our attack highlights that (1) the most widely used post-training quantization method is susceptible to adversarial interferences, and (2) the complexity of quantization schemes alone is insufficient as a defense.
|
quantization, large language models, security, poisoning, gguf
|
Building on existing LLM quantization exploitation attacks targeting naive quantization, we extend them to the popular GGUF quantization by a simple modification.
| 15,795 |
2505.23786
|
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] | null |
182 |
Elucidating the design space of language models for image generation
|
https://openreview.net/forum?id=EIfCH9OgjR
|
[
"Xuantong LIU",
"Shaozhe Hao",
"Xianbiao Qi",
"Tianyang Hu",
"Jun Wang",
"Rong Xiao",
"Yuan Yao"
] |
Poster
|
The success of large language models (LLMs) in text generation has inspired their application to image generation. However, existing methods either rely on specialized designs with inductive biases or adopt LLMs without fully exploring their potential in vision tasks. In this work, we systematically investigate the design space of LLMs for image generation and demonstrate that LLMs can achieve near state-of-the-art performance without domain-specific designs, simply by making proper choices in tokenization methods, modeling approaches, scan patterns, vocabulary design, and sampling strategies. We further analyze autoregressive models' learning and scaling behavior, revealing how larger models effectively capture more useful information than the smaller ones. Additionally, we explore the inherent differences between text and image modalities, highlighting the potential of LLMs across domains. The exploration provides valuable insights to inspire more effective designs when applying LLMs to other domains. With extensive experiments, our proposed model, **ELM** achieves an FID of 1.54 on 256$\times$256 ImageNet and an FID of 3.29 on 512$\times$512 ImageNet, demonstrating the powerful generative potential of LLMs in vision tasks.
|
Image generation, Large language model, Generative model
|
This work fully explores the use of language models for image generation, analyzing their optimization behavior, investigating tokenization, sampling strategies, and model scalability to achieve optimal performance.
| 15,791 |
2410.16257
|
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] |
https://github.com/Pepper-lll/LMforImageGeneration
|
183 |
Be a Goldfish: Forgetting Bad Conditioning in Sparse Linear Regression via Variational Autoencoders
|
https://openreview.net/forum?id=aTQtGq7IyT
|
[
"Kuheli Pratihar",
"Debdeep Mukhopadhyay"
] |
Poster
|
Variational Autoencoders (VAEs), a class of latent-variable generative models, have seen extensive use in high-fidelity synthesis tasks, yet their loss landscape remains poorly understood. Prior theoretical works on VAE loss analysis have focused on their latent-space representational capabilities, both in the optimal and limiting cases. Although these insights have guided better VAE designs, they also often restrict VAEs to problem settings where classical algorithms, such as Principal Component Analysis (PCA), can trivially guarantee globally optimal solutions. In this work, we push the boundaries of our understanding of VAEs beyond these traditional regimes to tackle NP-hard sparse inverse problems, for which no classical algorithms exist. Specifically, we examine the nontrivial Sparse Linear Regression (SLR) problem of recovering optimal sparse inputs in the presence of an ill-conditioned design matrix having correlated features. We provably show that, under a linear encoder-decoder architecture incorporating the product of the SLR design matrix with a trainable, sparsity-promoting diagonal matrix, any minimum of VAE loss is guaranteed to be an optimal solution. This property is especially useful for identifying (a) a preconditioning factor that reduces the eigenvalue spread, and (b) the corresponding optimal sparse representation. Lastly, our empirical analysis with different types of design matrices validates these findings and even demonstrates a higher recovery rate at low sparsity where traditional algorithms fail. Overall, this work highlights the flexible nature of the VAE loss, which can be adapted to efficiently solve computationally hard problems under specific constraints.
|
Variational Autoencoders, Sparse Linear Regression, NP-Hard, Smoothing, Sparsity, Matrix Conditioning
|
We use Variational Autoencoders that smoothen out bad local minima to solve the NP-hard inverse problem of Sparse linear regression and perform better than conventional methods.
| 15,780 | null |
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] | null |
184 |
Incremental Gradient Descent with Small Epoch Counts is Surprisingly Slow on Ill-Conditioned Problems
|
https://openreview.net/forum?id=LiXD7mpjU0
|
[
"Yujun Kim",
"Jaeyoung Cha",
"Chulhee Yun"
] |
Poster
|
Recent theoretical results demonstrate that the convergence rates of permutation-based SGD (e.g., random reshuffling SGD) are faster than uniform-sampling SGD; however, these studies focus mainly on the large epoch regime, where the number of epochs $K$ exceeds the condition number $\kappa$. In contrast, little is known when $K$ is smaller than $\kappa$, and it is still a challenging open question whether permutation-based SGD can converge faster in this small epoch regime (Safran and Shamir, 2021). As a step toward understanding this gap, we study the naive deterministic variant, Incremental Gradient Descent (IGD), on smooth and strongly convex functions. Our lower bounds reveal that for the small epoch regime, IGD can exhibit surprisingly slow convergence even when all component functions are strongly convex. Furthermore, when some component functions are allowed to be nonconvex, we prove that the optimality gap of IGD can be significantly worse throughout the small epoch regime. Our analyses reveal that the convergence properties of permutation-based SGD in the small epoch regime may vary drastically depending on the assumptions on component functions. Lastly, we supplement the paper with tight upper and lower bounds for IGD in the large epoch regime.
|
Permutation-based SGD, Incremental Gradient Descent, Lower Bound, Convex Optimization
|
We study Incremental Gradient Descent in the small epoch regime and show that it exhibits severe slowdown especially in the presence of nonconvex components.
| 15,777 |
2506.04126
|
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] | null |
185 |
Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty
|
https://openreview.net/forum?id=9rLxi2cnZC
|
[
"Yeseul Cho",
"Baekrok Shin",
"Changmin Kang",
"Chulhee Yun"
] |
Poster
|
Recent advances in deep learning rely heavily on massive datasets, leading to substantial storage and training costs. Dataset pruning aims to alleviate this demand by discarding redundant examples. However, many existing methods require training a model with a full dataset over a large number of epochs before being able to prune the dataset, which ironically makes the pruning process more expensive than just training the model on the entire dataset. To overcome this limitation, we introduce the **Difficulty and Uncertainty-Aware Lightweight (DUAL)** score, which aims to identify important samples from the early training stage by considering both example difficulty and prediction uncertainty. To address a catastrophic accuracy drop at an extreme pruning ratio, we further propose a pruning ratio-adaptive sampling using Beta distribution.
Experiments on various datasets and learning scenarios such as image classification with label noise and image corruption, and model architecture generalization demonstrate the superiority of our method over previous state-of-the-art (SOTA) approaches. Specifically, on ImageNet-1k, our method reduces the time cost for pruning to 66\% compared to previous methods while achieving a SOTA 60\% test accuracy at a 90\% pruning ratio. On CIFAR datasets, the time cost is reduced to just 15\% while maintaining SOTA performance.
|
Dataset Pruning, Coreset Selection, Example Difficulty, Prediction Uncertainty
|
For dataset pruning, we introduce the DUAL (Difficulty and Uncertainty-Aware Lightweight) score, a new method that identifies important training examples early in the training process by considering both example difficulty and prediction uncertainty.
| 15,764 |
2502.06905
|
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] |
https://github.com/behaapyy/dual-pruning
|
186 |
The Case for Learned Provenance-based System Behavior Baseline
|
https://openreview.net/forum?id=SY4owu5BK6
|
[
"Yao Zhu",
"Zhenyuan LI",
"Yangyang Wei",
"Shouling Ji"
] |
Poster
|
Provenance graphs describe data flows and causal dependencies of host activities, enabling to track the data propagation and manipulation throughout the systems, which provide a foundation for intrusion detection. However, these Provenance-based Intrusion Detection Systems (PIDSes) face significant challenges in storage, representation, and analysis, which impede the efficacy of machine learning models such as Graph Neural Networks (GNNs) in processing and learning from these graphs. This paper presents a novel learning-based anomaly detection method designed to efficiently embed and analyze large-scale provenance graphs. Our approach integrates dynamic graph processing with adaptive encoding, facilitating compact embeddings that effectively address out-of-vocabulary (OOV) elements and adapt to normality shifts in dynamic real-world environments. Subsequently, we incorporate this refined baseline into a tag-propagation framework for real-time detection. Our evaluation demonstrates the method's accuracy and adaptability in anomaly path mining, significantly advancing the state-of-the-art in handling and analyzing provenance graphs for anomaly detection.
|
graph representation learning, provenance graph, cyber attack detection
| null | 15,752 | null |
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] | null |
187 |
Flexible, Efficient, and Stable Adversarial Attacks on Machine Unlearning
|
https://openreview.net/forum?id=ba3sSfEnj1
|
[
"Zihan Zhou",
"Yang Zhou",
"Zijie Zhang",
"Lingjuan Lyu",
"Da Yan",
"Ruoming Jin",
"Dejing Dou"
] |
Poster
|
Machine unlearning (MU) aims to remove the influence of specific data points from trained models, enhancing compliance with privacy regulations. However, the vulnerability of basic MU models to malicious unlearning requests in adversarial learning environments has been largely overlooked. Existing adversarial MU attacks suffer from three key limitations: inflexibility due to pre-defined attack targets, inefficiency in handling multiple attack requests, and instability caused by non-convex loss functions. To address these challenges, we propose a Flexible, Efficient, and Stable Attack (DDPA). First, leveraging Carathéodory's theorem, we introduce a convex polyhedral approximation to identify points in the loss landscape where convexity approximately holds, ensuring stable attack performance. Second, inspired by simplex theory and John's theorem, we develop a regular simplex detection technique that maximizes coverage over the parameter space, improving attack flexibility and efficiency. We theoretically derive the proportion of the effective parameter space occupied by the constructed simplex. We evaluate the attack success rate of our DDPA method on real datasets against state-of-the-art machine unlearning attack methods. Our source code is available at https://github.com/zzz0134/DDPA.
|
Machine unlearning, poisoning attack, thrust vector control theory, John's Theorem, polyhedral approximation
|
Flexible, Efficient, and Stable Adversarial Attacks against machine unlearning that processes multiple arbitrary attack targets at a time
| 15,747 | null |
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] |
https://github.com/zzz0134/DDPA
|
188 |
PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization
|
https://openreview.net/forum?id=O0lxLP4ABD
|
[
"Xinyi Wan",
"Penghui Qi",
"Guangxing Huang",
"Min Lin",
"Jialin Li"
] |
Poster
|
Pipeline parallelism (PP) is widely used for training large language models (LLMs), yet its scalability is often constrained by high activation memory consumption as the number of in-flight microbatches grows with the degree of PP. In this paper, we focus on addressing this challenge by leveraging the under-explored memory offload strategy in PP. With empirical study, we discover that in the majority of standard configurations, at least half, and potentially all, of the activations can be offloaded with negligible overhead. In the cases where full overload is not possible, we introduce a novel selective offload strategy that decreases peak activation memory in a better-than-linear manner. Furthermore, we integrate memory offload with other techniques to jointly consider overall throughput and memory limitation. Our experiments proves that the per-device activation memory effectively reduces with the total number of stages, making PP a stronger alternative than TP, offering up to a 19\% acceleration with even lower memory consumption.
|
LLM Training, Pipeline Parallelism, Offloading, Memory Optimization
| null | 15,731 |
2503.01328
|
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] |
https://github.com/sail-sg/zero-bubble
|
189 |
Unified Screening for Multiple Diseases
|
https://openreview.net/forum?id=z4XS0Ie391
|
[
"Yiğit Narter",
"Alihan Hüyük",
"Mihaela van der Schaar",
"Cem Tekin"
] |
Poster
|
Current screening programs that focus on improving patient health while minimizing screening costs are tailored for individual diseases. Designing unified screening programs for multiple diseases requires carefully balancing competing disease risks, which is an open problem. In this work, we address this problem by casting unified screening as a referral problem, in which we choose to activate a subset of screening policies for individual diseases by accounting for competing risks that influence patient outcomes. We introduce a novel optimization framework that incorporates disease risks, budget constraints, and diagnostic error limits and characterize the structural properties of the optimal referral policy. For the unified screening of two diseases, we show that the optimal activation threshold for the screening of one disease depends on the risk of the other, resulting in decision boundaries with distinct risk-dependent profiles. We compare our unified model with independent screening programs that apply isolated activation thresholds for screening of each disease. Our approach optimizes screening decisions collectively, improving overall survival outcomes, particularly for patients with high disease risks.
|
Unified screening, healthcare, competing risks, constrained optimization, threshold policy
| null | 15,730 | null |
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] |
https://github.com/ynarter/UniScreen
|
190 |
Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants
|
https://openreview.net/forum?id=39JKH8k3FS
|
[
"Daniele Tramontano",
"Yaroslav Kivva",
"Saber Salehkaleybar",
"Negar Kiyavash",
"Mathias Drton"
] |
Poster
|
This paper investigates causal effect identification in latent variable Linear Non-Gaussian Acyclic Models (lvLiNGAM) using higher-order cumulants, addressing two prominent setups that are challenging in the presence of latent confounding: (1) a single proxy variable that may causally influence the treatment and (2) underspecified instrumental variable cases where fewer instruments exist than treatments. We prove that causal effects are identifiable with a single proxy or instrument and provide corresponding estimation methods. Experimental results demonstrate the accuracy and robustness of our approaches compared to existing methods, advancing the theoretical and practical understanding of causal inference in linear systems with latent confounders.
|
Causal Effect Identification, Structural Causal Models, Cumulants.
| null | 15,727 |
2506.05202
|
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] |
https://github.com/danieletramontano/CEId-from-Moments
|
191 |
STAMP Your Content: Proving Dataset Membership via Watermarked Rephrasings
|
https://openreview.net/forum?id=qF6mxani2X
|
[
"Saksham Rastogi",
"Pratyush Maini",
"Danish Pruthi"
] |
Poster
|
Given how large parts of publicly available text are crawled to pretrain large language models (LLMs), data creators increasingly worry about the inclusion of their proprietary data for model training without attribution or licensing. Their concerns are also shared by benchmark curators whose test-sets might be compromised. In this paper, we present STAMP, a framework for detecting dataset membership—i.e., determining the inclusion of a dataset in the pretraining corpora of LLMs. Given an original piece of content, our proposal involves first generating multiple rephrases, each embedding a watermark with a unique secret key. One version is to be released publicly, while others are to be kept private. Subsequently, creators can compare model likelihoods between public and private versions using paired statistical tests to prove membership. We show that our framework can successfully detect contamination across four benchmarks which appear only once in the training data and constitute less than 0.001% of the total tokens, outperforming several contamination detection and dataset inference baselines. We verify that STAMP preserves both the semantic meaning and utility of the original data. We apply STAMP to two real-world scenarios to confirm the inclusion of paper abstracts and blog articles in the pretraining corpora.
|
LLM, membership inference, dataset inference, watermarking, test set contamination
|
We present STAMP, a framework to detect whether a given dataset was used in LLM pretraining.
| 15,721 |
2504.13416
|
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] |
https://github.com/codeboy5/stamp
|
192 |
Counterfactual Effect Decomposition in Multi-Agent Sequential Decision Making
|
https://openreview.net/forum?id=jHLSnYNt1m
|
[
"Stelios Triantafyllou",
"Aleksa Sukovic",
"Yasaman Zolfimoselo",
"Goran Radanovic"
] |
Poster
|
We address the challenge of explaining counterfactual outcomes in multi-agent Markov decision processes. In particular, we aim to explain the total counterfactual effect of an agent's action on the outcome of a realized scenario through its influence on the environment dynamics and the agents' behavior. To achieve this, we introduce a novel causal explanation formula that decomposes the counterfactual effect by attributing to each agent and state variable a score reflecting their respective contributions to the effect. First, we show that the total counterfactual effect of an agent's action can be decomposed into two components: one measuring the effect that propagates through all subsequent agents' actions and another related to the effect that propagates through the state transitions. Building on recent advancements in causal contribution analysis, we further decompose these two effects as follows. For the former, we consider agent-specific effects -- a causal concept that quantifies the counterfactual effect of an agent's action that propagates through a subset of agents. Based on this notion, we use Shapley value to attribute the effect to individual agents. For the latter, we consider the concept of structure-preserving interventions and attribute the effect to state variables based on their "intrinsic'' contributions. Through extensive experimentation, we demonstrate the interpretability of our approach in a Gridworld environment with LLM-assisted agents and a sepsis management simulator.
|
counterfactual reasoning, causal explanation formula, multi-agent Markov decision processes, accountability
|
We address the challenge of explaining the total counterfactual effect of an agent’s action on the outcome of a realized scenario in multi-agent Markov decision processes.
| 15,708 |
2410.12539
|
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] |
https://github.com/stelios30/cf-effect-decomposition
|
193 |
A Mixed-Curvature based Pre-training Paradigm for Multi-Task Vehicle Routing Solver
|
https://openreview.net/forum?id=JsPyLqCgks
|
[
"Suyu Liu",
"Zhiguang Cao",
"Shanshan Feng",
"Yew-Soon Ong"
] |
Poster
|
Solving various types of vehicle routing problems (VRPs) using a unified neural solver has garnered significant attentions in recent years. Despite their effectiveness, existing neural multi-task solvers often fail to account for the geometric structures inherent in different tasks, which may result in suboptimal performance. To address this limitation, we propose a curvature-aware pre-training framework. Specifically, we leverage mixed-curvature spaces during the feature fusion stage, encouraging the model to capture the underlying geometric properties of each instance. Through extensive experiments, we evaluate the proposed pre-training strategy on existing neural multi-task solvers across a variety of testing scenarios. The results demonstrate that the curvature-aware pre-training approach not only enhances the generalization capabilities of existing neural VRP solvers on synthetic datasets but also improves solution quality on real-world benchmarks.
|
Combinatorial Optimization, Constrained Optimization, Vehicle Routing Problems, Deep Reinforcement Learning
|
We propose a geometric pre-training method to get a more powerful foundation model for various kinds of VRPs.
| 15,699 | null |
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] | null |
194 |
GRAM: A Generative Foundation Reward Model for Reward Generalization
|
https://openreview.net/forum?id=rxKC8v2uHc
|
[
"Chenglong Wang",
"Yang Gan",
"Yifu Huo",
"Yongyu Mu",
"Qiaozhi He",
"MuRun Yang",
"Bei Li",
"Tong Xiao",
"Chunliang Zhang",
"Tongran Liu",
"JingBo Zhu"
] |
Poster
|
In aligning large language models (LLMs), reward models have played an important role, but are standardly trained as discriminative models and rely only on labeled human preference data. In this paper, we explore methods that train reward models using both unlabeled and labeled data. Building on the generative models in LLMs, we develop a generative reward model that is first trained via large-scale unsupervised learning and then fine-tuned via supervised learning. We also show that by using label smoothing, we are in fact optimizing a regularized pairwise ranking loss. This result, in turn, provides a new view of training reward models, which links generative models and discriminative models under the same class of training objectives. The outcome of these techniques is a foundation reward model, which can be applied to a wide range of tasks with little or no further fine-tuning effort. Extensive experiments show that this model generalizes well across several tasks, including response ranking, reinforcement learning from human feedback, and task adaptation with fine-tuning, achieving significant performance improvements over several strong baseline models.
|
Large Language Model, Reward Modeling, RLHF
|
This study introduces a generative foundation reward model (GRAM), which pre-learns preferences through a two-stage training process and selective label smoothing.
| 15,682 |
2506.14175
|
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] | null |
195 |
Fine-Grained Captioning of Long Videos through Scene Graph Consolidation
|
https://openreview.net/forum?id=aTC2euLwnh
|
[
"Sanghyeok Chu",
"Seonguk Seo",
"Bohyung Han"
] |
Poster
|
Recent advances in vision-language models have led to impressive progress in caption generation for images and short video clips. However, these models remain constrained by their limited temporal receptive fields, making it difficult to produce
coherent and comprehensive captions for long videos. While several methods have been proposed to aggregate information across video segments, they often rely on supervised fine-tuning or incur significant computational overhead. To address these challenges, we introduce a novel framework for long video captioning based on graph consolidation. Our approach first generates segment-level captions, corresponding to individual frames or short video intervals, using off-the-shelf visual captioning models. These captions are then parsed into individual scene graphs, which are subsequently consolidated into a unified graph representation that preserves both holistic context and fine-grained details throughout the video. A lightweight graph-to-text decoder then produces the final video-level caption. This framework effectively extends the temporal understanding capabilities of existing models without requiring any additional fine-tuning on long video datasets. Experimental results show that our method significantly outperforms existing LLM-based consolidation approaches, achieving strong zero-shot performance while substantially reducing computational costs.
|
long video captioning, zero-shot video captioning, scene graph
|
We propose a novel framework for long video captioning based on graph consolidation.
| 15,674 |
2502.16427
|
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] | null |
196 |
M3-JEPA: Multimodal Alignment via Multi-gate MoE based on the Joint-Embedding Predictive Architecture
|
https://openreview.net/forum?id=tYwKQMMjJA
|
[
"Hongyang Lei",
"Xiaolong Cheng",
"Qi Qin",
"Dan Wang",
"Huazhen Huang",
"Qingqing Gu",
"Yetao Wu",
"Luo Ji"
] |
Poster
|
Current multimodal learning strategies primarily optimize in the original token space. Such a framework is easy to incorporate with the backbone of pretrained language model, but might result in modality collapse. To alleviate such issues, we leverage the Joint-Embedding Predictive Architecture (JEPA) on the multimodal tasks, which converts the input embedding into the output embedding space by a predictor and then conducts the cross-modal alignment on the latent space. We implement this predictor by a Multi-Gate Mixture of Experts (MMoE) and name the framework as M3-JEPA, accordingly. The gating function disentangles the modality-specific and shared information and derives information-theoretic optimality. The framework is implemented with both contrastive and regularization loss, and solved by alternative gradient descent (AGD) between different multimodal tasks. By thoroughly designed experiments, we show that M3-JEPA can obtain state-of-the-art performance on different modalities and tasks, generalize to unseen datasets and domains, and is computationally efficient in both training and inference. Our observation suggests that M3-JEPA might become a new basis to self-supervised learning in the open world.
|
JEPA, MoE, multimodal, alignment
|
We propose M3-JEPA, which implements a multi-gate MoE predictor to JEPA, and shows theoretical optimality and reasonable performance on multimodal tasks..
| 15,673 | null |
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] |
https://github.com/HongyangLL/M3-JEPA
|
197 |
Inverse problems with experiment-guided AlphaFold
|
https://openreview.net/forum?id=qzM37nOy3N
|
[
"Sai Advaith Maddipatla",
"Nadav Bojan",
"Meital Bojan",
"Sanketh Vedula",
"Paul Schanda",
"Ailie Marx",
"Alexander Bronstein"
] |
Poster
|
Proteins exist as a dynamic ensemble of multiple conformations, and these motions are often crucial for their functions. However, current structure prediction methods predominantly yield a single conformation, overlooking the conformational heterogeneity revealed by diverse experimental modalities. Here, we present a framework for building experiment-grounded protein structure generative models that infer conformational ensembles consistent with measured experimental data. The key idea is to treat state-of-the-art protein structure predictors (e.g., AlphaFold3) as sequence-conditioned structural priors, and cast ensemble modeling as posterior inference of protein structures given experimental measurements. Through extensive real-data experiments, we demonstrate the generality of our method to incorporate a variety of experimental measurements. In particular, our framework uncovers previously unmodeled conformational heterogeneity from crystallographic densities, generates high-accuracy NMR ensembles orders of magnitude faster than status quo, and incorporates pairwise cross-link constraints. Notably, we demonstrate that our ensembles outperform AlphaFold3 and sometimes better fit experimental data than publicly deposited structures to the protein database (PDB). We believe that this approach will unlock building predictive models that fully embrace experimentally observed conformational diversity.
|
protein structure prediction, alphafold, protein generative models, experiment-grounded generative models
|
We develop experiment-guided AlphaFold-3 to solve inverse problems in structural biology, leading to faster experimental cycles and improved modeling in X-ray crystallography and NMR spectroscopy.
| 15,661 |
2502.09372
|
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] | null |
198 |
Synthesizing Software Engineering Data in a Test-Driven Manner
|
https://openreview.net/forum?id=P9DQ2IExgS
|
[
"Lei Zhang",
"Jiaxi Yang",
"Min Yang",
"Jian Yang",
"Mouxiang Chen",
"Jiajun Zhang",
"Zeyu Cui",
"Binyuan Hui",
"Junyang Lin"
] |
Poster
|
We introduce **SWE-Flow**, a novel data synthesis framework grounded in Test-Driven Development (TDD).
Unlike existing software engineering data that rely on human-submitted issues, **SWE-Flow** automatically infers incremental development steps directly from unit tests, which inherently encapsulate high-level requirements.
The core of **SWE-Flow** is the construction of a Runtime Dependency Graph (RDG), which precisely captures function interactions, enabling the generation of a structured, step-by-step *development schedule*.
At each step, **SWE-Flow** produces a partial codebase, the corresponding unit tests, and the necessary code modifications, resulting in fully verifiable TDD tasks.
With this approach, we generated 16,061 training instances and 2,020 test instances from real-world GitHub projects, creating the **SWE-Flow-Eval** benchmark.
Our experiments show that fine-tuning open model on this dataset significantly improves performance in TDD-based coding.
To facilitate further research, we release all code, datasets, models, and Docker images at [Github](https://github.com/Hambaobao/SWE-Flow).
|
Large language Model, Software Engineering, Test-Driven Development, Code Agent
| null | 15,658 |
2506.09003
|
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] |
https://github.com/Hambaobao/SWE-Flow
|
199 |
AEQA-NAT : Adaptive End-to-end Quantization Alignment Training Framework for Non-autoregressive Machine Translation
|
https://openreview.net/forum?id=mQE0EsrX1y
|
[
"Xiangyu Qu",
"guojing liu",
"Liang Li"
] |
Poster
|
Non-autoregressive Transformers (NATs) have garnered significant attention due to their efficient decoding compared to autoregressive methods. However, existing conditional dependency modeling schemes based on masked language modeling introduce a *training-inference gap* in NATs. For instance, while NATs sample target words during training to enhance input, this condition cannot be met during inference, and simply annealing the sampling rate to zero during training leads to model performance degradation. We demonstrate that this *training-inference gap* prevents NATs from fully realizing their potential. To address this, we propose an adaptive end-to-end quantization alignment training framework, which introduces a semantic consistency space to adaptively align NAT training, eliminating the need for target information and thereby bridging the *training-inference gap*.Experimental results demonstrate that our method outperforms most existing fully NAT models, delivering performance on par with Autoregressive Transformer (AT) while being 17.0 times more efficient in inference.
|
Machine Translation, Parallel decoding, Vector Quantization
| null | 15,652 | null |
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] | null |
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