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Model routing can reduce the cost of LLM applications by assigning simpler requests to cheaper models without sacrificing the quality of the response. However, most routers treat requests as independent. Stateful agents do not operate under this assumption: a user turn may require multiple model and tool calls, its dif...
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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