Towards Reliable Context Compression for Long-Horizon Agents: An Empirical Study of Execution Instability

Abstract:Recurrent context compression controls context growth in long-horizon agents, but its behavioral effects remain poorly understood. In this preliminary empirical study, we show that compression can weaken the influence of recent interactions, increasing blocked actions, repeated exploration, and instability across runs. Motivated by these observations, we introduce TRACE, a verifier-guided framework that evaluates individual compaction events through paired closed-loop continuations from the same environment state and uses summary preferences to optimize a natural-language compression prompt while keeping all models frozen. Initial results on AppWorld show improvements over existing compression baselines in task performance, multi-run reliability, and context--execution efficiency. These findings provide early evidence for boundary-local evaluation as a promising direction for reliable agent context compression.

Machine Learning (cs.LG)

arXiv:2608.06503 [cs.LG]

(or arXiv:2608.06503v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.06503 Focus to learn more arXiv-issued DOI via DataCite

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