Librarian Agents: Training LLMs to Curate Long-Context Workspaces that Anticipate Future Queries
To instantiate this capability, we introduce Librarian Agents, a framework in which a builder agent transforms raw documents into a hierarchical filesystem of synthetic files and reference links, which forms a workspace tailored for a downstream answering agent.
ProofPaper ↗
Key points
- Reasoning over long document contexts remains a challenge for large language models (LLMs).
- Standard retrieval methods are inherently reactive, bottlenecking agents with complex, cross-document reasoning at inference time, while precomputed structures like knowledge graphs often fall short because they rely on rigid heuristics.
- Instead, can we allow agents to perform anticipatory directed curation: learning to proactively reorganize unstructured corpora prior to inference to efficiently serve diverse future queries.
- We formulate workspace construction as a Markov decision process and optimize the builder policy via reinforcement learning to directly maximize downstream answering accuracy.
Sources (1)
- [1]Librarian Agents: Training LLMs to Curate Long-Context Workspaces that Anticipate Future QueriesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 05:35 PM
To instantiate this capability, we introduce Librarian Agents, a framework in which a builder agent transforms raw documents into a hierarchical filesystem of synthetic files and reference links, which forms a workspace tailored for a downstream answering agent.
Reasoning over long document contexts remains a challenge for large language models (LLMs).
Extractive summary: sentences quoted from the sources.
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