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BENCHCOMPASS is introduced, a payment-domain benchmark whose construction pipeline builds scenario-grounded tasks from typed evidence packs, applies LLM-based quality checks, creates task-input attack variants, and reserves final item admission for domain experts.
Reusable skills give agents transferable procedural knowledge, making scalable acquisition essential for extending agents beyond prior experience. Existing methods face two limitations: trajectory-based synthesis requires interactions with specific environments, while document-derived skills may lack executable evidenc...
The proposed ARC (Advantage Regularization via Conditioning), a training recipe that restores fairer relative comparison through strategy-conditioned rollout grouping, together with hybrid rewards and entropy regularization, is proposed.
ACA-RL supports a new mission for NLP evaluation: measuring whether models can recognize when a task is underdetermined and handle uncertainty, not only whether they can answer fully specified questions.
This work proposes \methodname, a stability-guided active-set controller for controlled objective admission, a stability-guided active-set controller for controlled objective admission in reward-vector RLHF, which positions objective-entry timing as a concrete control variable in reward-vector RLHF.
DARC is proposed, a diagnosis-guided recovery harness that profiles task-family failure modes, prunes mismatched interventions from a shared recovery library, and freezes a verifier-selected success-cost policy for deployment, providing a practical route toward more reliable agents in domains where compiler-like feedba...
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