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Bait-and-Recover is proposed, a weight-level defense that places a bait adapter where attackers read activations and a paired recovery adapter at the subsequent layer that decouples the observation path from the behavior path.
It is found that generated harnesses remain substantially behind mature human-engineered references on code and on search and research, while matching or exceeding the selected references on writing and machine-learning experimentation, with large variation in execution cost.
Results show that looping can improve Transformers even under budget matching, offering a practical recipe that turns depth reuse into measurable gains.
Proof-Verified Benchmark Rewriting (RePro) is proposed, the first framework to integrate Lean-oriented neural automated theorem provers (ATPs) into benchmark rewriting, which rewrites problems and regenerates answers with correctness ensured by Lean-verified proofs.
This work introduces ASPIRE, a benchmark for vague-goal-driven self-evolution and shows that vague goals redirect search effort toward goal interpretation, and evaluates the resulting systems on a hidden, expert-authored set of 520 items spanning six goals.
These findings show that recognizing successful actions is insufficient; agents must also transform feedback into executable and transferable policies, and provide a unified framework for diagnosing this process and identifying the bottlenecks that prevent agents from translating interaction experience into reliable se...
DABO is proposed, a calibration-aware binary offloading method for collaborative large–small model inference that maintains competitive end-to-end accuracy while processing an average of 83.72% of requests at the edge.
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