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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...
This paper analyzes the intermediate reasoning steps of LLMs to uncover hidden stigmatizing language and the internal rationales driving it, and rates the severity of these statements, distinguishing between overt prejudice and more subtle, less immediately harmful biases.
This article presents a joint connectivity-compute view of AI-native 6G, and introduces AI Grid as a distributed AI infrastructure platform for placing workloads according to latency, cost, policy, and service-level constraints.
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...
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