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Long-horizon agents learn from failure, but the value of a failure record can change as execution progresses. We study when retaining, compressing, or removing the same history block improves subsequent execution relative to full-history KEEP. We introduce RECAP, a framework that combines matched context interventions,...
Long-running LLM agents are stateful: beyond the transcript they accrete compressed summaries, plaintext memory, pending tool plans, and, under every serving API, a KV cache. Yet today's"forget"operations delete a plaintext memory record and stop, leaving every artifact derived from the revoked information intact. We f...
A reference-free judge is formalized as a latent solver, and a closed-form bound on discriminability (ROC-AUC) in the judge's competence and answer-space size is yields the result that the marginal AUC is confounded by item difficulty while a within-question estimator is not.
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