Get Started
Research questionHow can LLM agents compress growing factual memories without losing retrievable evidence?Redundant records can obscure useful evidence during retrieval, while aggressive compression can remove facts that become relevant later. The challenge is preserving retrievable structure as the interaction history grows.
AI Agents
AI Memory
Information Retrieval
Latest papersRecent research connected to this question, newest first.MemCoRe: Recovering Evidence from Progressively Compressed Factual Knowledge for Agent MemoryThe source evaluates hierarchical progressive compression from detailed records to keywords and topic groups, with retrieval across these levels. Evidence concerns factual agent memory and experiments against existing memory baselines.research paper · Sep 4, 2026
Related questions
How can LLM agents extract useful long-term memories for unknown future tasks without storing hallucinated facts?How can long-term LLM agents reconcile evolving textual evidence across interactions?How can memory-augmented LLM agents coordinate memory construction, retrieval, and repair over long-horizon interactions?How can personalized LLM agents retrieve time-valid memories of persistent and evolving user states?
Home
Topics
Search
Library