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Exhaustive optimistic scoring can make action selection costly in linear bandits with a large fixed set of $K$ actions, requiring $\mathcal{O}(K)$ score evaluations per round. We introduce Persistent Candidate LinUCB (\textsc{PC-LinUCB}), which maintains $k$ active actions and samples $q$ challengers uniformly from the...
This work introduces SAGE (Self-Adapting Group of Experts), a training-free framework that uses answer agreement, prefix consistency, and reciprocal peer review to select a strategy donor and transfers the selected donor's reasoning strategy to the other agents while preserving their original roles.
The results show that residual-graph value learning yields state-dependent dynamic matching policies that adapt to realized connectivity and exit information.
PDC consistently improves upon the initial sample, outperforms independent generations under a computational constrained comparison, and is robust to different unmasking strategies and parameter settings, highlighting prefix-conditioned regeneration as an effective DLM-specific primitive for test-time verification.
The role of target-specific privilege is investigated with On-Policy Self-Distillation from Other Problems (On-Policy Self-Distillation from Other Problems), which replaces the paired reference with a problem and solution from a different example, while preserving the student rollout, teacher, and distillation objectiv...
The Structural Gap Hypothesis Agent (SGHA), a fully automated, corpus-first research-problem discovery system that runs entirely on a local LLM, is introduced and compared with the AI Scientist-v2 idea formulation module in five machine-learning domains.
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