Agents
10d agoLLM Agents Suffer From Memory Tail Errors
Semantic LLM policies create truncated-power-law memory traces, concentrating retrieval on a small core of data. This leaves rare states in a long tail where prediction errors accumulate. Researchers at arXiv propose a Core-Tail World Model to address this imbalance.
The Signal
Practitioners should audit memory distribution to prevent agent failure in edge-case scenarios.