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

arXiv: https://arxiv.org/abs/2608.06503 · HTML: https://mlsanity.com/paper/2608.06503/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Efficient reasoning and agent inference, one of 12 directions in Efficient LLM inference and serving.
- Builds on 22 of its 66 references that are in the map.
- First posted on arXiv 6 Aug 2026.

## Placed in

- Efficient LLM inference and serving / [Efficient reasoning and agent inference](https://mlsanity.com/inference/efficient-reasoning-and-agent-inference/)

## Builds on (most cited first, 22 of 22)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024)
- [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023)
- [τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environment](https://mlsanity.com/paper/2506.07982/) (2025)
- [Executable Code Actions Elicit Better LLM Agents](https://mlsanity.com/paper/2402.01030/) (2024)
- [AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents](https://mlsanity.com/paper/2407.18901/) (2024)
- [Learning to Compress Prompts with Gist Tokens](https://mlsanity.com/paper/2304.08467/) (2023)
- [Adapting Language Models to Compress Contexts](https://mlsanity.com/paper/2305.14788/) (2023)
- [LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression](https://mlsanity.com/paper/2403.12968/) (2024)
- [LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models](https://mlsanity.com/paper/2310.05736/) (2023)
- [In-context Autoencoder for Context Compression in a Large Language Model](https://mlsanity.com/paper/2307.06945/) (2023)
- [Compressing Context to Enhance Inference Efficiency of Large Language Models](https://mlsanity.com/paper/2310.06201/) (2023)
- [ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization](https://mlsanity.com/paper/2509.13313/) (2025)
- [ACON: Optimizing Context Compression for Long-horizon LLM Agents](https://mlsanity.com/paper/2510.00615/) (2025)
- [Long Context Compression with Activation Beacon](https://mlsanity.com/paper/2401.03462/) (2024)
- [OfficeBench: Benchmarking Language Agents across Multiple Applications for Office Automation](https://mlsanity.com/paper/2407.19056/) (2024)
- [Scaling LLM Multi-turn RL with End-to-end Summarization-based Context Management](https://mlsanity.com/paper/2510.06727/) (2025)
- [500xCompressor: Generalized Prompt Compression for Large Language Models](https://mlsanity.com/paper/2408.03094/) (2024)
- [TACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learning](https://mlsanity.com/paper/2409.13035/) (2024)
- [Fix the Structural Bottleneck: Context Compression via Explicit Information Transmission](https://mlsanity.com/paper/2602.03784/) (2026)

## Built on by (most cited first, 10 of 10)

- [AnyJev Technical Report](https://mlsanity.com/paper/2610.00831/) (2026)
- [Correct Now, Insufficient Later: Auditing Update Sufficiency in Context Compression](https://mlsanity.com/paper/2609.20045/) (2026)
- [Evaluating Budgeted Context Projection with Unexecuted Companion Runs](https://mlsanity.com/paper/2609.31381/) (2026)
- [Beyond Token Savings: A Systematic Study of Context Compression in LLM Agents](https://mlsanity.com/paper/2609.32961/) (2026)
- [Beyond Skill Evolution: Self-Evolving Context Management Policies for Long-Horizon Agent Harnesses](https://mlsanity.com/paper/2609.34649/) (2026)
- [Does an Agent's History Tell You When Compaction Will Hurt? A Modest, Bounded Effect on the TRACE Paired-Replay Corpus](https://mlsanity.com/paper/2610.08722/) (2026)
- [State Representation Shapes Long-Horizon Agent Learning and Transfer: An Empirical Study](https://mlsanity.com/paper/2610.15407/) (2026)
- [Glasseek: A Transparent Recipe for Training and Trustworthy Evaluation of DeepSearch Agents](https://mlsanity.com/paper/2610.16998/) (2026)
- [DeLoop: Repeated Actions as Self-Supervision for Long-Horizon Agents](https://mlsanity.com/paper/2610.19376/) (2026)
- [Maximising Transparency in Context Compaction Improves Retention of Facts and Commitments for Lightweight Coding Agents](https://mlsanity.com/paper/2610.20116/) (2026)

## Abstract (arXiv)

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.