A device-native autonomous agent system using zero-knowledge proofs and distilled world models achieves 87% negotiation success, 2.4x lower latency than cloud systems, and 27% higher user trust in privacy-sensitive scenarios.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Neural network surrogates approximate expected operational costs in multistage stochastic TEP, delivering near-optimal investment plans with up to 13x faster computation on IEEE test systems.
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Device-Native Autonomous Agents for Privacy-Preserving Negotiations
A device-native autonomous agent system using zero-knowledge proofs and distilled world models achieves 87% negotiation success, 2.4x lower latency than cloud systems, and 27% higher user trust in privacy-sensitive scenarios.
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Learning a Non-linear Surrogate Model for Multistage Stochastic Transmission Planning
Neural network surrogates approximate expected operational costs in multistage stochastic TEP, delivering near-optimal investment plans with up to 13x faster computation on IEEE test systems.