Diagnosing and Mitigating Context Rot in Long-horizon Search
Extensive context has become the norm as Large Language Models (LLMs) are increasingly deployed in long-horizon search tasks. The concern that increasing context length degrades model capabilities, known as context rot, has become a widely recognized issue for these applications. However, in deep search scenarios, it remains unclear how models actually fail under extensive context, and to what extent existing methods can mitigate such failures. Through a systematic study of four flagship models across three benchmarks, we identify a previously overlooked phenomenon, which we term premature termination: under extensive context, models give up or provide uncertain incorrect answers long bef...
| Authors | Shijie Xia, Yikun Wang, Zhen Huang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 29 June 2026 |
| Last updated at source | 4 August 2026 |
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