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The EIR is introduced, a lower bound on the information that must remain accessible for correct completion under specified task and access conditions and LACUNA is developed, a framework that generates tasks with known dependencies and varies information demand, retention, and recovery separately from the difficulty of...
Results suggest that corpus-scale statistical reflection is a viable alternative to iterative search for prompt optimization, and given only a static corpus of agent trajectories, an off-the-shelf coding agent can directly synthesize an optimized prompt, requiring neither environment access nor validation data.
This work shows that reasoning traces are not a prerequisite: skills distilled from non-reasoning trajectories alone remain competitive with skills distilled from paired reasoning/non-reasoning corpora, with domain-dependent differences between the two sources.
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