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Results show that long-horizon reflective data is an effective route toward self-improving agents, and synthesize long-horizon improvement trajectories from machine learning and algorithmic programming tasks, two domains that offer verifiable feedback and reward sustained iteration.
Information-Gain-Gated Self-Distillation (IGSD) is proposed, which verifies on-policy token proposals with environment feedback before distillation, and supports environment-verified hindsight as an effective approach to reliable action-level supervision for search agents.
This work introduces a clustering mechanism and proposes the Cluster Sketch Linear Bandit algorithm, which preserves the full covariance information in each cluster to guarantee robust sublinear regret without spectral-tail vulnerabilities, performs cluster switching by assigning a sentinel for each cluster, and reduce...
Dense retrieval has become a cornerstone of modern local-lifestyle e-commerce search by encoding queries and items into semantic embedding spaces. While recent advancements have transitioned from BERT-based embedding models to Large Language Models (LLMs), most approaches still treat LLMs as static text encoders, negle...
SpecTD-MR, a novel medication recommendation framework for Disease Spectrum-aware and Time-evolving Dependency Learning, is proposed that surpasses state-of-the-art baselines in accuracy, while providing visualizable structural associations to support insights into dynamic disease evolution.
DisCo is presented, a skill-powered research agent that creates skills and uses them during research, and yields the AREX-Skill Library, with 5,000+ verified skills distilled from 1,000 widely used ML repositories and organized into 20 areas and 178 capability families.
This work proposes Dynamic Index-based RECommendation with Transport-Optimized Retrieval with Transport-Optimized Retrieval (DIRECTOR), a transport-guided parallel reranking framework that consistently outperforms strong reranking baselines, achieving significant improvement in large-scale industrial recommendation sce...
A Hierarchical Semantic Alignment module to align query's latent space with item's quantization path and synchronize multi-granular semantics, and a personalized GR framework that models user behavior by synergizing discrete SIDs for structural guidance and continuous representations for fine-grained semantic refinemen...
A novel method, namely AnDPro, is proposed, which introduces a projection-based scoring function to more accurately measure token importance and guide more accurate token selection in key-Value cache eviction.
It is found that OPD transfers a teacher's reasoning behavior rather than its answers to particular problems: training difficulty barely matters, and even problems the teacher never solves are useful.
The exact selection time for an isolated cycle of NOTEARS and DAGMA is derived, and a truth-free separation statistic predicts selection time on 320 official NOTEARS/DAGMA trajectories.
The resulting lesson is task-specific: a first stage for generated controls should be judged by control fidelity, downstream relevance, and graph compatibility together.
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