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  • Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via . . .
    Abstract Modern retrieval systems, whether lexical or semantic, expose a corpus through a fixed similarity interface that compresses access into a single top-k retrieval step before reasoning This abstraction is efficient, but for agentic search, it becomes
  • Retrieval from Within: An Intrinsic Capability of Attention-Based Models
    Abstract Retrieval-augmented generation (RAG) typically treats retrieval and generation as separate systems We ask whether an attention-based encoder-decoder can instead retrieve directly from its own internal representations We introduce INTRA (INTrinsic Retrieval via Attention), a framework where decoder attention queries score pre-encoded evidence chunks that are then directly reused as
  • T2PO: Uncertainty-Guided Exploration Control for Stable Multi-Turn . . .
    Abstract Recent progress in multi-turn reinforcement learn-ing (RL) has significantly improved reasoning LLMs’ performances on complex interactive tasks Despite advances in stabilization tech-niques such as fine-grained credit assignment and trajectory filtering, instability remains per-vasive and often leads to training collapse We argue that this instability stems from inefficient
  • Proximal-Based Generative Modeling for Bayesian Inverse Problems
    Abstract Score-based diffusion models demonstrate su-perior performance in generative tasks but en-counter fundamental bottlenecks in inverse prob-lems due to the analytical intractability of the time-dependent likelihood score To bridge this gap, we propose a novel proximal-based genera-tive modeling (PGM) framework that rigorously circumvents explicit likelihood evaluation Our framework is
  • arXiv. org
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  • SkillOS: Learning Skill Curation for Self-Evolving Agents - arXiv. org
    A key substrate for self-evolution is procedural memory (Fang et al , 2025b; Hu et al , 2025; Wu et al , 2025b), specifically, reusable skills (Anthropic, 2025b; Wang et al , 2025c) accumulated from past interactions In real-world streaming settings (Wu et al , 2024), a skill-based self-evolving agent typically follows a closed-loop workflow: for each new task, it selects relevant skills
  • BUILD-AND-FIND: An Effort-Aware Protocol for Evaluating Agent-Managed . . .
    Abstract Most coding-agent benchmarks ask whether generated code behaves correctly That remains essential, but repository-level engineering is increasingly agent-managed: one agent writes a repository, and later agents inspect, audit, or extend it as working context In that setting, a generated repository is not only an answer to a task but also a communication artifact for future work Even
  • MEMOREPAIR: Barrier-First Cascade Repair in Agentic Memory - arXiv. org
    source artifact is deleted, corrected, or invalidated by tool or API migration, descendants derived from that source can remain visible and steer future actions with stale support We formalize this failure mode as the cascade update problem, where repair targets the visible derived state of the memory store We present MEMOREPAIR, a barrier-first cascade-repair contract for agentic memory A
  • OpenSearch-VL: An Open Recipe for Frontier Multimodal Search Agents
    Deep search has become a crucial capability for frontier multimodal agents, enabling models to solve complex questions through active search, evidence verification, and multi-step reasoning Despite rapid progress, top-tier multimodal search agents remain difficult to reproduce, largely due to the absence of open high-quality training data, transparent trajectory synthesis pipelines, or
  • AI co-mathematician: Accelerating mathematicians with agentic AI
    WeintroducetheAIco-mathematician,aworkbenchformathematicianstointeractivelyleverageAI agentstopursueopen-endedresearch TheAIco





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