庞玉栋博客
//

人工智能

83 篇 · 资讯流
  2. arXiv cs.RO（机器人）

    GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitives

    Precise grounding matters. It specifies which object is the target and where that object is, even in clutter and for tin

    原文 ↗
  3. arXiv cs.RO（机器人）

    GroundAnything: Reconciling Parallel Decoding with Precise Visual Grounding at Flash Speed

    Autoregressive (AR) grounding models serialize spatial predictions, introducing sequential latency and imposing a causal

    原文 ↗
  4. arXiv cs.RO（机器人）

    Text-to-3D Policy: Fine-Grained Language-Behavior Alignment for Unseen Specification Generalization

    3D visuomotor policies provide a strong foundation for spatially precise manipulation, yet current text-to-3D policies s

    原文 ↗
  5. arXiv cs.AI（人工智能）

    $S^3$: Spectral Null-Space Swap Makes Reasoning Models Efficient

    LLMs trained with Chain-of-thought excel in reasoning capability, but often come with excessive token cost. We find that

    原文 ↗
  7. arXiv cs.RO（机器人）

    Explore, Execute, Evolve: A Skill Acquisition and Reuse Loop for Embodied Agents

    Vision-language-action and world-action models have demonstrated impressive capabilities in robotics, yet generalization

    原文 ↗
  8. arXiv cs.DC（分布式并行）

    SPLASH: Switching Parallel Layouts of Attention with Seamless Handoff for LLM Serving

    No single way of parallelizing attention serves large language models well under all loads. Low concurrency favors tenso

    原文 ↗
  9. arXiv cs.DC（分布式并行）

    Janus: Evidence-Before-Effect Sagas and Offline-Verifiable Provenance for Agentic LLMs

    Agentic large language models (LLMs) now move money through tools, yet the record of what they did is usually a trace th

    原文 ↗
  10. arXiv cs.DC（分布式并行）

    Purlin: Separating Orchestration from the Datapath of Collectives

    Distributed inference depends on GPU collective communication that must keep pace with evolving hardware and specialized

    原文 ↗
  12. arXiv cs.RO（机器人）

    Rethinking Causal Action Tokenization with Conditional Annealing in Flow Matching

    Autoregressive Vision-Language-Action (VLA) models offer a scalable path to robot learning, yet existing action tokenize

    原文 ↗
  13. arXiv cs.CL（自然语言）

    Self-Adapting Group of Experts for Multi-Agent Reasoning

    Multi-agent systems bring together language model agents with different roles to propose, review, and refine solutions.

    原文 ↗