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We introduce Sequent-Prover, a family of verifier-using language-model agents, and the post-training framework that produces them. Agents reason in an executable SMT-LIB scratchpad: they write complete SMT-LIB programs, query Z3 across turns, inspect solver feedback, repair formalizations, and terminate with an explici...
While Vision-Language-Action (VLA) models enable flexible action generation, their generalization across diverse environmental elements, including manipulated objects, destinations, and backgrounds, is limited by the lack of diversity in robotic training data. Trained end-to-end on such data, VLAs tend to exploit visua...
The ablations analyse why Foil works and turn the findings into design guidance for looped MoE: the returns of looping and of widening the expert layers amplify each other, routing confidence tracks healthy expert use better than load balance, and a sparse looped MoE should therefore use more experts per layer and more...
This empirical study covers broad knowledge, symbolic reasoning, and competition mathematics, and it introduces an evaluation profile whose coordinates and simple functionals recover or bound common repeated-sampling metrics, and require compute accounting and uncertainty estimates that match the protocol.
Large language models can solve substantially harder reasoning problems with more inference-time compute. The term"test-time scaling,"however, now covers diverse inference algorithms that extend deliberation along a single trajectory, sample completed candidates and aggregate them through voting or verification, or sea...
Counterfactual Realignment (CoRe), a training-free framework that recovers a frozen VLA at inference time without failure data, is proposed, a training-free framework that recovers a frozen VLA at inference time without policy fine-tuning or failure-specific recovery training.
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