@inproceedings{wang-etal-2026-context,
title = "Context Compression for {LLM} Agents: A Survey of Methods, Failure Modes, and Evaluation",
author = "Wang, Yifei and
Wang, Ziteng and
Shi, Yuling and
Chen, Silin and
Wang, Xinrui and
Wang, Yueqi and
Shen, Beijun and
Li, Linjing and
Gu, Xiaodong and
McAuley, Julian and
Zeng, Daniel Dajun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2396/",
pages = "54460--54488",
ISBN = "979-8-89176-459-0",
abstract = "As Large Language Models (LLMs) evolve into autonomous agents for long-horizon tasks, managing unbounded interaction trajectories under fixed context budgets becomes a core systems challenge. Unlike standard long-context documents, agent trajectories are heterogeneous and interleave observations, reasoning traces, and tool executions, so compression must preserve temporal dependencies, actionable state, and structural fidelity. Yet existing methods remain fragmented, making it difficult to compare design choices and reason about their reliability implications. This survey introduces a unified taxonomy of agent context compression along three dimensions: compression target (what is compressed), compression mechanism (how it is transformed and retained), and control policy (who decides when compression is triggered). We further organize recurring failures in compressed execution into F1: Pre-compression Decision Error, F2: In-compression Information Loss, and F3: Post-compression Access Failure, and examine domain-specific trade-offs in software engineering, web navigation, and deep research. By unifying the design space, failure taxonomy, and evaluation perspective, this survey provides a foundation for building scalable and recoverable LLM agents."
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<abstract>As Large Language Models (LLMs) evolve into autonomous agents for long-horizon tasks, managing unbounded interaction trajectories under fixed context budgets becomes a core systems challenge. Unlike standard long-context documents, agent trajectories are heterogeneous and interleave observations, reasoning traces, and tool executions, so compression must preserve temporal dependencies, actionable state, and structural fidelity. Yet existing methods remain fragmented, making it difficult to compare design choices and reason about their reliability implications. This survey introduces a unified taxonomy of agent context compression along three dimensions: compression target (what is compressed), compression mechanism (how it is transformed and retained), and control policy (who decides when compression is triggered). We further organize recurring failures in compressed execution into F1: Pre-compression Decision Error, F2: In-compression Information Loss, and F3: Post-compression Access Failure, and examine domain-specific trade-offs in software engineering, web navigation, and deep research. By unifying the design space, failure taxonomy, and evaluation perspective, this survey provides a foundation for building scalable and recoverable LLM agents.</abstract>
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%0 Conference Proceedings
%T Context Compression for LLM Agents: A Survey of Methods, Failure Modes, and Evaluation
%A Wang, Yifei
%A Wang, Ziteng
%A Shi, Yuling
%A Chen, Silin
%A Wang, Xinrui
%A Wang, Yueqi
%A Shen, Beijun
%A Li, Linjing
%A Gu, Xiaodong
%A McAuley, Julian
%A Zeng, Daniel Dajun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-context
%X As Large Language Models (LLMs) evolve into autonomous agents for long-horizon tasks, managing unbounded interaction trajectories under fixed context budgets becomes a core systems challenge. Unlike standard long-context documents, agent trajectories are heterogeneous and interleave observations, reasoning traces, and tool executions, so compression must preserve temporal dependencies, actionable state, and structural fidelity. Yet existing methods remain fragmented, making it difficult to compare design choices and reason about their reliability implications. This survey introduces a unified taxonomy of agent context compression along three dimensions: compression target (what is compressed), compression mechanism (how it is transformed and retained), and control policy (who decides when compression is triggered). We further organize recurring failures in compressed execution into F1: Pre-compression Decision Error, F2: In-compression Information Loss, and F3: Post-compression Access Failure, and examine domain-specific trade-offs in software engineering, web navigation, and deep research. By unifying the design space, failure taxonomy, and evaluation perspective, this survey provides a foundation for building scalable and recoverable LLM agents.
%U https://aclanthology.org/2026.emnlp-main.2396/
%P 54460-54488
Markdown (Informal)
[Context Compression for LLM Agents: A Survey of Methods, Failure Modes, and Evaluation](https://aclanthology.org/2026.emnlp-main.2396/) (Wang et al., EMNLP 2026)
ACL
- Yifei Wang, Ziteng Wang, Yuling Shi, Silin Chen, Xinrui Wang, Yueqi Wang, Beijun Shen, Linjing Li, Xiaodong Gu, Julian McAuley, and Daniel Dajun Zeng. 2026. Context Compression for LLM Agents: A Survey of Methods, Failure Modes, and Evaluation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 54460–54488, Budapest, Hungary. Association for Computational Linguistics.