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This paper presents an autoresearch loop for generating provider preference taxonomies in service marketplaces using large language models, transitioning from legacy forms to AI-native matching, with deployment results from a major U.S. marketplace.
EXHOLD is a two-stage framework for real-time hold control in large-scale ride-hailing matching, improving passenger-driver experience and marketplace efficiency. Deployed in DiDi's Brazil market, it uses experience-aware pair assessment and constrained optimization to reduce cancellations and increase trip completion.
APCyc is a target-aware generative framework that designs cyclic peptides with controlled physicochemical properties by explicitly modeling cyclization patterns and using Bayesian posterior guidance.
This paper proposes ManiF-SMC, a method for approximate machine unlearning that operates entirely in the representation space by pushing erased samples away from their original learned manifold representation toward their nearest semantic neighbors in the retained data, using a margin-based triplet loss guided by a self-mode-connectivity module for adaptive margins.
A paper on building cutting-edge agents using automated prompt optimization and evaluations has been accepted to KDD 2026.
KDD 2026 Cycle 2 paper reviews have disappeared from author view, while remaining visible in reviewer view, raising concerns about a potential technical issue with the conference submission system.