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Recommendation recursive self-improvement (Rec-RSI) feeds recommender outputs into subsequent training. Evaluating each round solely through its latest model assumes that the successor consolidates the update, although pre- and post-update models may retain complementary ranking decisions. We term this \emph{distribute...
Long-term multi-agent systems continuously accumulate the memories produced by different agents. Existing memory methods typically treat retrieved memories as independent evidence and combine them through voting or weighting. However, this independence assumption often fails in multi-agent settings: memories written by...
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.
This paper proposes Dude, the first Dual-Detection Multi-Agent System for paper-code discrepancy detection, with a granularity-aligned negotiation and a two-stage salience-filtering mechanism in Dude that effectively prevents agents from falsely reporting discrepancies.
A client-specific adaptation channel based on private prompt tokens, which tracks local adaptation dynamics separately from the shared backbone and provides a lightweight signal for detecting whether client adaptation remains active, and a shallow sufficiency estimator that combines cross-client semantic alignment, tem...
A domain-sensitive federated pruning framework that preserves domain-invariant structures while retaining domain-specific representations and a structure-aware aggregation algorithm that fuses heterogeneous personalized architectures into a domain-generalized global model is proposed.
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