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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.
On-policy self-distillation densifies agent training without external teachers: a policy conditioned on privileged hindsight provides step-level guidance for its own unprivileged rollouts. For search agents, however, hindsight can make the teacher prefer a query that does not improve retrieval from the student's state....
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...
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.
The results show that simple parameter averaging, when paired with lightweight dimensional adaptation and carefully controlled ratios, is a surprisingly strong baseline for heterogeneous LLM merging, suggesting that the limits of direct weighted fusion may also bound what more complex heterogeneous merging methods can...
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