  Information retrieval is a rapidly evolving field. However it still faces
significant limitations in the scientific and industrial vast amounts of
information, such as semantic divergence and vocabulary gaps in sparse
retrieval, low precision and lack of interpretability in semantic search, or
hallucination and outdated information in generative models. In this paper, we
introduce a two-block approach to tackle these hurdles for long documents. The
first block enhances language understanding in sparse retrieval by query
expansion to retrieve relevant documents. The second block deepens the result
by providing comprehensive and informative answers to the complex question
using only the information spread in the long document, enabling bidirectional
engagement. At various stages of the pipeline, intermediate results are
presented to users to facilitate understanding of the system's reasoning. We
believe this bidirectional approach brings significant advancements in terms of
transparency, logical thinking, and comprehensive understanding in the field of
scientific information retrieval.
