Heavy-tailed memory traces in long-horizon language agents (arXiv:2610.00010v1)
This arXiv preprint studies how external memory use in long-horizon language agents concentrates on a small core of frequently retrieved states while leaving rare states in a long tail where prediction errors accumulate, with tail shape depending on policy (log-normal-like for random-walk agents vs truncated-power-law-like for semantic LLM policies). The authors introduce the Core--Tail World Model (CTWM), a rank-based memory controller that allocates prompt budget with a single exponent τ and preserves a summarized tail; on Synthetic Graph World CTWM preserves state/transition coverage, reduces prompt tokens by 5.9% and lowers bottom-half tail prediction error by 13.6% versus a graph-memory baseline, and also shows token savings on ALFWorld and a 24.48% token reduction on LongMemEval with aggregate accuracy parity.