LLM4Branch discovers branching policies for MILP solvers as LLM-generated executable programs whose parameters are tuned via zeroth-order optimization on solver performance.
Deepseek-r1 incentivizes reasoning in llms through reinforcement learning
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SelectiveRM applies optimal transport with a joint consistency discrepancy and partial mass relaxation to produce reward models that optimize a tighter upper bound on clean risk while autonomously dropping noisy preference samples.
TRUST is a decentralized AI auditing framework that decomposes reasoning into HDAGs, maps agent interactions via the DAAN protocol to CIGs, and uses stake-weighted multi-tier consensus to achieve 72.4% accuracy while proving a Safety-Profitability Theorem that rewards honest auditors.
citing papers explorer
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LLM4Branch: Large Language Model for Discovering Efficient Branching Policies of Integer Programs
LLM4Branch discovers branching policies for MILP solvers as LLM-generated executable programs whose parameters are tuned via zeroth-order optimization on solver performance.
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Optimal Transport for LLM Reward Modeling from Noisy Preference
SelectiveRM applies optimal transport with a joint consistency discrepancy and partial mass relaxation to produce reward models that optimize a tighter upper bound on clean risk while autonomously dropping noisy preference samples.
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TRUST: A Framework for Decentralized AI Service v.0.1
TRUST is a decentralized AI auditing framework that decomposes reasoning into HDAGs, maps agent interactions via the DAAN protocol to CIGs, and uses stake-weighted multi-tier consensus to achieve 72.4% accuracy while proving a Safety-Profitability Theorem that rewards honest auditors.