Hugging Face Trending Papers August 17, 2026

LENS: In-Context Search via Latent Evidence Exploration over Dynamic Raw Documents

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LLM agents increasingly answer questions over dynamic raw-document collections, where files may change before preprocessing, and relevant evidence (spans, sections, pages, or tables) is query-dependent. Existing retrieval-augmented approaches pre-materialize evidence via fixed chunking, embeddings, or persistent indexes: effective for lookup, yet costly, stale-prone, and committed to a granularity before the query is known.

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arXiv AI

1d ago

LENS: In-Context Search via Latent Evidence Exploration over Dynamic Raw Documents

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Related stories

LENS: In-Context Search via Latent Evidence Exploration over Dynamic Raw Documents

DocMemo: Dynamic Evidence Discovery via Probabilistic Memory-Guided Retrieval for Multi-Modal Document Understanding

M-RAG: Semantic Key-Value Indexing for Retrieval-Augmented Generation

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VecTree-RAG: An Agentic Retrieval-Augmented Generation Framework Combining Vector and Tree Retrieval for Efficiency and Accuracy