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Learning What to Skip is introduced, which learns action-specific safety models from these interventions and combines held-out calibration with domain-native guards to select skips and reduces recorded token cost while matching or improving aggregate full-workflow accuracy in the evaluated settings.
This work proposes AgentMMRec, an agent-based multimodal recommendation framework with two coordinated roles that consistently improves Recall and NDCG over recent multimodal baselines, remains effective under sparsity and item cold-start settings, and can transfer its constructed knowledge to existing backbones.
This paper introduces CoCA, an on-policy learning framework that recovers a deployable capability-subset policy from sparse conditional comparisons, and introduces dual-level on-policy distillation to address distribution mismatch both across environment states and within the partial subsets encountered during set cons...
This paper proposes Neural Tree Collaborative Filtering (NTCF), a framework that re-interprets each node's local neighborhood as a rooted tree and assigns a node-specific propagation depth based on a closed-form local-degree-imbalance score that serves as a discrete Ricci-curvature proxy.
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