Public articles linked to the same research event.
arXiv The work introduces OMVV (Online Multi-Verifier Verification), an algorithm that maintains a pool of K candidate weak verifiers with differing cost and verification performance and adaptively routes each round's decision to a verifier selected via an online score combiner and an exponential-weights routing policy; OMVV provides a distribution-free, finite-time guarantee on false-accept and false-reject rates across the full pool of verifiers and achieves sublinear regret against the best fixed verifier in hindsight under a combined cost and consistency objective, and experiments on reasoning dataset benchmarks show higher accuracy at lower verification cost than any single fixed verifier across a range of operating budgets.
The work introduces OMVV (Online Multi-Verifier Verification), an algorithm that maintains a pool of K candidate weak verifiers with differing cost and verification performance and adaptively routes each round's decision to a verifier selected via an online score combiner and an exponential-weights routing policy; OMVV provides a distribution-free, finite-time guarantee on false-accept and false-reject rates across the full pool of verifiers and achieves sublinear regret against the best fixed verifier in hindsight under a combined cost and consistency objective, and experiments on reasoning dataset benchmarks show higher accuracy at lower verification cost than any single fixed verifier across a range of operating budgets.
The work introduces OMVV (Online Multi-Verifier Verification), an algorithm that maintains a pool of K candidate weak verifiers with differing cost and verification performance and adaptively routes each round's decision to a verifier selected via an online score combiner and an exponential-weights routing policy; OMVV provides a distribution-free, finite-time guarantee on false-accept and false-reject rates across the full pool of verifiers and achieves sublinear regret against the best fixed verifier in hindsight under a combined cost and consistency objective, and experiments on reasoning dataset benchmarks show higher accuracy at lower verification cost than any single fixed verifier across a range of operating budgets.
The work introduces OMVV (Online Multi-Verifier Verification), an algorithm that maintains a pool of K candidate weak verifiers with differing cost and verification performance and adaptively routes each round's decision to a verifier selected via an online score combiner and an exponential-weights routing policy; OMVV provides a distribution-free, finite-time guarantee on false-accept and false-reject rates across the full pool of verifiers and achieves sublinear regret against the best fixed verifier in hindsight under a combined cost and consistency objective, and experiments on reasoning dataset benchmarks show higher accuracy at lower verification cost than any single fixed verifier across a range of operating budgets.