提出一种自适应交错代码推理方法，让多模态大语言模型在混合推理中更灵活高效
arXiv:2606.23678v1 Announce Type: cross Abstract: Following the paradigm shift initiated by OpenAI o3, interleaved reasoning with code to enhance mult…
被ICML Workshop收录，提出一种用于大语言模型的线性路由器，有望简化模型推理中的资源分配问题。
arXiv:2606.06098v1 Announce Type: new Abstract: Foundational Large Language Models (LLMs) demonstrate proficiency on a wide range of general tasks, an…
LLM推理时缩放的新方法：双维度一致性，有效平衡采样预算与推理质量，提升效率。
arXiv:2605.15100v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable abilities in reasoning. However, maximizing …
基于案例的CAST框架，利用历史执行轨迹校准LLM工具使用的推理深度与结构有效性，突破参数知识局限。
arXiv:2605.15041v1 Announce Type: new Abstract: Tool use extends large language models beyond parametric knowledge, but reliable execution requires ba…
将推理预算设定问题重构为风险控制问题，在限制错误率的同时最小化计算量，用分布自由的方法自适应控制推理何时停下，既省Token又保精准。
arXiv:2602.03814v2 Announce Type: replace Abstract: Reasoning Large Language Models (LLMs) enable test-time scaling, with dataset-level accuracy impro…