ResearchResearch paperLarge Language Models · Reinforcement Learning · Efficiency & Inference1 source · Oct 19, 2026

When the harness decides success: state that cannot be re-observed

Agent harnesses decide what a language model sees across turns: they drop old turns, summarize them, or replay the model's own earlier reasoning.

We ask when these choices change whether an agent succeeds, not only what it costs, and report three findings.
First, in a pre-registered synthetic probe, truncating the context window costs a low-cost reasoning model nothing in success when a needed fact can be looked up again (it looks the fact up instead), but drops its success to chance when the fact cannot be re-observed: the two penalties differ by 92.5 percentage points (90% CI [89.5, 95.5]).
A pre-registered replication on one model from a second provider showed the same contrast.
Once the fact was lost, that model chose no answer rather than guessing.

[1]When the harness decides success: state that cannot be re-observed
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 03:59 PM
Agent harnesses decide what a language model sees across turns: they drop old turns, summarize them, or replay the model's own earlier reasoning.
We ask when these choices change whether an agent succeeds, not only what it costs, and report three findings.

Extractive summary: sentences quoted from the sources.

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