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Author

Guang-Hui Min

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#artificial intelligence Preprint Sep 2026

Adapting Context Compression for Long-Horizon Agents with Counterfactual Continuations

It is found that compression degrades reliability before solvability and proposed PAIR (Prompt Adaptation using Interventional Rollouts) for adapting structured compression prompts achieves the strongest cross-run reliability among compressed methods in every main benchmark-scope combination.

Guang-Hui Min, Liang Wu, Ming-Jia Shi et al. · 0 citations
Preprint Aug 2026

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

TRACE is introduced, 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.

Guang-Hui Min, Liang Wu, Mayank Darbari et al. · 5 citations · ⚡1

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