Context Rot in Long-Horizon Search
ParshinShojaee · x · 2026-07-14
This comment praises a paper on context rot in long-horizon search, particularly appreciating the experiments on "recursion" and "sub-agent isolation".
The paper highlights that as context accumulates, agents in long-horizon tasks become increasingly prone to giving up early or submitting uncertain answers prematurely. The longer the run, the worse the issue gets. Beyond diagnosing the phenomenon, the authors propose mitigation strategies like context management and rejection sampling. The commenter also notes that the varying vulnerability of different models to these strategies is worth attention.
More from Research
- Rewriting scaling law study: optimal synthetic data share rises from 10% to 32% with compute — heghbalz · 2026-10-26
- FLASH: GPU-native deformable sim learns cloth folding in minutes on one RTX 5090 — ChongZzZhang · 2026-10-26
- LEMON: zero-shot LLM misbehavior monitor flags hallucinations from attention maps — yoavartzi · 2026-10-26
- Behind the EMNLP keynote: LMs as simulated learners to generate child language acquisition hypotheses — najoungkim · 2026-10-26
- The paper behind the EMNLP 2026 keynote on bigram 'simulated learners' — najoungkim · 2026-10-26
- Laying out DeepLIFT as a standalone computation graph: from sequence to attributions directly — jmschreiber91 · 2026-10-26