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Time-series foundation models offer a unified approach to forecasting across heterogeneous domains. Textual context and auxiliary observations provide complementary information about temporal dynamics, yet reusable multimodal predictive representations remain underexplored. We introduce Pythia, a foundation world model...
TimEvolve is introduced, a frozen-backbone time series agent that converts each realized outcome into persistent joint updates of expert trust, agent path selection, and intervention strength, and demonstrates the value of learning forecasting policies from the futures encountered during deployment.
Overall, Code2Skill transforms procedural knowledge embedded in repositories into grounded, verifiable, and transferable agent skills, providing initial evidence that the pipeline can expand with the growing volume of AI-generated software.
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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