Agents
Banger paper from Microsoft and colleagues. If you run agents that search a large document collection, this one is worth your time. (bookmar…
Banger paper from Microsoft and colleagues. If you run agents that search a large document collection, this one is worth your time. (bookmark it) They introduce CorpusMap, which resolves recurring ent
Banger paper from Microsoft and colleagues. If you run agents that search a large document collection, this one is worth your time. (bookmark it) They introduce CorpusMap, which resolves recurring entities across the collection in advance and gives each entity a page that links to every document that mentions it. The original documents stay in place. The agent reads a document, follows an entity to related documents, and avoids searching for the same evidence again. Across 7 models and three benchmarks, answer quality goes up 6.4 to 11.7 points while input tokens drop 34% to 57%. It also beats an LLM Wiki layer and three other navigation layers. The map can be built without LLM calls and updated as new documents arrive. Paper: https://arxiv.org/abs/2609.37226 Chat with Paper: https://academy.dair.ai/papers/follow-the-entities-a-corpus-map-for-agentic-search-2609.37226
Related
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- Interesting paper from Microsoft. If you run persistent memory for a production agent, this one is worth your time. (bookmark it) Memory cur…
- Nice paper discussing context trimming for agents. This is a hot topic at the moment, so it might be worth your time. Context trimming for a…
Source: DAIR.AI (X) | 2026-10-05