[Submitted on 9 Jul 2026]
Title:Heavy-Tailed Memory Traces in Long-Horizon Language Agents
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Abstract:Long-horizon language agents increasingly rely on external memory as a frozen world model, yet current memory systems are usually judged only by task success or token cost. We argue that the missing object is the shape of memory use: under finite context and repeated retrieval, agent memory can concentrate on a small core while leaving rare states in a long tail where prediction errors accumulate. We study this effect through a conservative tail audit and find that concentration is reproducible but policy-dependent. Random-walk agents produce log-normal-compatible retrieval artifacts, whereas semantic LLM policies yield the strongest truncated-power-law-compatible core--tail traces. Motivated by this audit, we propose Core--Tail World Model (CTWM), a rank-based memory controller that allocates prompt budget with a single exponent $\tau$ while retaining a summarized tail. On Synthetic Graph World, CTWM preserves full state and transition coverage, reduces prompt tokens by 5.9%, and lowers bottom-half tail prediction error by 13.6% relative to a graph-memory baseline. The same paired comparison gives consistent token savings on ALFWorld and a 24.48% token reduction on LongMemEval with aggregate accuracy parity. These results suggest that heavy-tailed memory traces are not only a diagnostic of finite retrieval, but also a practical control signal for token-efficient agent world models.
Comments: Under Review
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.00010 [cs.AI]
(or arXiv:2610.00010v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.00010
arXiv-issued DOI via DataCite
Submission history
From: Zekun Cai [view email] [v1] Thu, 9 Jul 2026 15:00:39 UTC (3,093 KB)
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