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

2026-08-06

AI Summary

This work addresses the degradation of recent interaction influence in long-horizon agents due to recursive context compression, which often leads to action blocking, redundant exploration, and cross-run instability. To mitigate these issues, the authors propose TRACE, a framework that employs a verifier-guided closed-loop contrastive mechanism to refine natural language compression prompts through boundary-localized evaluation—without updating the underlying model. By integrating paired closed-loop continuations with summary preference learning, TRACE substantially enhances the reliability of context compression. Evaluated on the AppWorld benchmark, the method consistently outperforms existing baselines in task performance, multi-run stability, and context-to-execution efficiency.

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.

Problem

• context compression
• long-horizon agents
• execution instability
• recurrent compression
• behavioral effects

Innovation

• context compression
• long-horizon agents
• execution instability
• verifier-guided framework
• closed-loop evaluation