The paper investigates how context compaction—losing information to free up a language model’s context window—affects performance on long‑horizon agentic tasks. Using the OpenCode scaffold and five compaction strategies, the authors evaluate three frontier models on PaperBench, ProgramBench, and PostTrainBench, finding that compaction thresholds largely do not hurt performance for 1M‑context models, but can degrade GPT‑5.3‑Codex on certain benchmarks. The study also shows that compaction can silently drop critical task constraints, especially on ProgramBench and real user‑agent sessions from SWE‑chat, yet current benchmarks remain largely compaction‑friendly because objectives are fixed and progress is stored externally.
By Jeanne Salle, Hardik Bhatnagar, Matthias Bethge, Sahar Abdelnabi, Maksym Andriushchenko
CliffCompaction is an autocompaction technique that reduces cost by up to 50% while maintaining or improving performance on benchmarks such as Terminal‑Bench and KernelBench. It achieves this by truncating or dropping content without rephrasing, ensuring compacted information remains faithful and preventing context drift. The method enables efficient test‑time scaling, matching or surpassing higher‑cost models like Opus 4.7 and GPT‑5.3 Codex, and delivers significant CUDA kernel speedups on KernelBench.
By Trang Nguyen, Eulrang Cho, Bingqing Chen, Tim Dettmers
Long‑horizon LLM agents use context compaction to replace an overflowing interaction history with a natural‑language summary. The paper shows that these summaries mix factual environment data with the compressor’s reasoning, making it hard for the agent to distinguish the two. The authors introduce Source‑Judged Compaction (SJC), a training‑free post‑processing step that scores each summary entry for a source in the environment or previous summary and removes unsupported entries, improving cross‑run reliability by 2.1%–8.9% on AppWorld and OfficeBench without increasing token usage.
By Guanghui Min, Chen Chen
The paper introduces CTXAgent, a self‑controlled context management system for large language model agents that compacts conversation history when a token threshold is reached. It evaluates seven frontier models, comparing their ability to manage context with and without dedicated tools, and benchmarks performance across coding, scientific, and software engineering tasks. Results show that well‑managed models keep live context below 100k tokens, maintain high benchmark scores, and achieve significant cost reductions, with Claude Opus 5.5 outperforming its explicit configuration by 34% lower cost.
By Matthias Busch, Marius Tacke, Roland C. Aydin, Christian Feiler, Christian J. Cyron
arXiv:2608. 16370v1 Announce Type: new Abstract: Task completion is the standard metric for evaluating context compression, yet it is incomplete: compression can increase an agent's interaction cost by forcing it to reacquire dropped state while leaving completion statistically unchanged.
By Shuyu Liu
arXiv:2607. 13071v1 Announce Type: cross Abstract: Agentic LLM coding tools compress long session histories into compaction summaries that subsequent sessions inherit as ground truth.
By Hiroki Tamba
The paper introduces LOHA, a context layout that compresses older tool observations into soft tokens while keeping the agent’s own turns and the last K observations in plain text, and ACD, a training method that distills full‑text predictions into this latent representation while anchoring behavior on plain text. This approach reduces context per call by up to 57% without significant loss in resolve rates, and improves instance throughput in single‑GPU serving. Experiments on SWE‑bench Verified show that K=3 yields a 43–57% compression with only modest performance impact, while larger windows favor task performance over compression.
By Zhensheng Zou (Peking University), Guoqing Wang (Peking University), Dan Hao (Peking University)
arXiv:2608. 11242v1 Announce Type: cross Abstract: When the context window is under pressure, LLM systems compact prior context to continue ongoing tasks.
By Zhiqi Wang, Yichi Zhang, Dongwon Lee, Yuchen Yang
arXiv:2610.33274v1 Announce Type: new
Abstract: Large language model (LLM) agents work in scenarios with very different memory needs. A question about past conversations needs the exact facts it name...
By Wujiang Xu, Jiaojiao Han, Xi Zhu, Qizheng Zhang, Kaiqu Liang, Yutong Yin, Kai Mei, Dimitris N. Metaxas
The paper introduces “Revise”, a runtime system that performs validity-guided, fine-grained recovery for online revisions in structured agent workflows. When a revision arrives, Revise intersects the change with recorded data and control dependencies, propagates the impact through the partially executed DAG, stops invalid work, preserves unaffected progress, and recomputes only the affected region. Experiments on real coding‑agent traces and LangGraph/LLMCompiler applications show that Revise matches a latest‑version oracle, reduces model calls by up to 56%, and improves service‑level objective goodput under load.
By Ruoling Qi, Xuaner Wu, Penghang Liu, Jian Chen, Yirui Liu
The paper introduces CTXGAP, a study measuring how much information coding agents omit from their user-facing reports compared to their full execution logs. Across 37 coding tasks, agents answer 93.6% of task-state questions when asked directly, but report-only readers recover only 52.0% of the same information, revealing a 40.7‑point gap. Even at final checkpoints of verified fixes, the gap remains significant, and detailed terminal scrollback improves recovery only to 73.6%. The study highlights that missing details in reports can leave supervisors unaware of failures, workarounds, and risks that need scrutiny.
By Guoyang Xu, Rui Zhang, Xiuyu Wu, Hao Chen
arXiv:2610.14146v1 Announce Type: new
Abstract: Long-horizon terminal benchmarks measure whether an agent can sustain progress through extended workflows with process rewards, not only whether its fi...
By Zongxia Li, Zhongzhi Li, Yucheng Shi, Ruhan Wang, Junyao Yang, Chengsong Huang, Zhichao Liu, Xiyang Wu, Anhao Li, Yue Yu, Ergan Shang, Fuxiao Liu, Tong Zheng, Ninghao Liu, Lichao Sun, Haitao Mi, Leowei Liang