  The advent of Large Language Models (LLMs) and Generative AI has
revolutionized natural language applications across various domains. However,
high-stakes decision-making tasks in fields such as medical, legal and finance
require a level of precision, comprehensiveness, and logical consistency that
pure LLM or Retrieval-Augmented-Generation (RAG) approaches often fail to
deliver. At Elemental Cognition (EC), we have developed a neuro-symbolic AI
platform to tackle these problems. The platform integrates fine-tuned LLMs for
knowledge extraction and alignment with a robust symbolic reasoning engine for
logical inference, planning and interactive constraint solving. We describe
Cora, a Collaborative Research Assistant built on this platform, that is
designed to perform complex research and discovery tasks in high-stakes
domains. This paper discusses the multi-step inference challenges inherent in
such domains, critiques the limitations of existing LLM-based methods, and
demonstrates how Cora's neuro-symbolic approach effectively addresses these
issues. We provide an overview of the system architecture, key algorithms for
knowledge extraction and formal reasoning, and present preliminary evaluation
results that highlight Cora's superior performance compared to well-known LLM
and RAG baselines.
