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2 stories in the last 24h

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Q&A on Any Spreadsheet Requires Interpreting Its Grid Structure

A cell-role annotation framework improves spreadsheet chunking for LLM-driven RAG, but the authors argue discrete classification cannot fully capture 2D grid structure.

The paper proposes splitting spreadsheets into interpretable chunks using semantic cell role annotation, improving chunking interpretability for LLM-driven RAG systems. It beats the state of the art on answer generation through enriched context but hits a hard ceiling, since finite predefined classes cannot capture spreadsheets' continuous relationships and infinite potential cell roles. The authors argue the field must develop dimensionality-reduction techniques that directly flatten 2D unstructured spreadsheets into 1D text for downstream RAG.

arXiv cs.AI / cs.LG / cs.CL · 20h agoAI research

RAFT: A Stateful Retrieval-Augmented Framework for Troubleshooting Agents

Researchers introduce RAFT, a stateful retrieval-augmented framework that retrieves timeline entries from historical cases to improve enterprise troubleshooting agents.

RAFT abstracts closed support cases into directed chains of timeline entries and retrieves at the entry level, returning parent-case trajectories anchored at matched states, with an optional case-level similarity graph. It beat vanilla RAG and GraphRAG baselines on Case Hit at every stage of case progress, using a synthetic benchmark built from Microsoft Learn Windows Server documentation and real Apache Jira issues with human-created duplicate labels. The benchmark, implementation, and Jira evaluation set are publicly released.

arXiv cs.AI / cs.LG / cs.CL · 20h agoAI research