👍 70
06/27 20:00
Reinforcement learning (RL) has gained growing attention in large language model (LLM) post-training, yet RL training remains fragile and can suffer from instability or collapse. One vital cause is training-inference mismatch: LLM adopts separate inference and training engines for generation efficie
中文介绍 针对大型语言模型(LLM)后续训练中的脆弱性,提出了一种优化的训练策略,通过确保推理策略与训练策略单调一致来解决训练与推理不匹配的问题。实验表明,此方法改善了模型的稳定性,显著降低了崩溃风险。该研究为大规模强化学习在LLM应用中的稳定性与高效性提供了新思路,意义:对提升Agent的推理能力具有重要影响。
👍 53
07/01 20:00
Memory for a long-horizon LLM agent is a contract about what each future decision is allowed to see. The simplest contract appends past observations, tool calls, and reflections to every prompt, which makes prior context easy to access but also turns it into a jumbled mixture in which the effect of
中文介绍 提出了AgenticSTS,一个具有边界记忆的长时间跨度LLM代理测试平台,旨在改善LLM代理在决策过程中对历史信息的利用。通过构建简单的合同管理机制,能更有效地编排过去的观察与反思,有助于提升模型在复杂任务下的表现。该研究为长时间决策过程中的信息管理提供了新的思路,意义:推动Agent在长期任务中的应用进展。
👍 45
07/01 20:00
Autonomous agents are increasingly expected to improve executable policies through feedback, yet existing evaluations often collapse this process into a final score or confound it with open-ended software-engineering progress. We introduce Autonomous Policy Evolution, a controlled evaluation setting
中文介绍 提出了EvoPolicyGym,一个用于在交互环境中评估自主策略演化的新框架。通过控制评估设置,该框架解决了现有评估方法过于简化的问题,能够有效监测自主体在反馈下的策略优化过程。实验证明,该方法在多轮反馈中展现出显著的性能提升,为自主体的学习与适应能力提供了重要评估手段,意义:有助于推动自主体在现实任务中的应用。
👍 42
06/28 20:00
Hybrid attention models improve long-context efficiency by retaining only a subset of full-attention layers and replacing the remaining layers with linear attention. However, the effectiveness of Transformer-to-hybrid conversion critically depends on which layers preserve full attention. Existing hy
中文介绍 研究了混合注意力模型,通过保留部分全注意力层和替换其余层为线性注意力,以提高长上下文的处理效率。研究表明,成功的Transformer到混合模型的转化取决于从全注意力中保留的具体层。提出的模型在多种长文本处理中相较于传统模型显示出明显的性能提升,意义:为处理长文本的能力提供了新的解决方案。
👍 30
07/01 20:00
Data science aims to derive actionable insights from heterogeneous raw data, unlocking the value of the massive amounts of data generated in modern society. Automating this process is essential to reducing labor-intensive efforts for data scientists and enabling scalable data-driven applications. Re
中文介绍 推出了AgenticDataBench,一个为数据代理的全面基准,旨在自动化从异构原始数据中提取可操作洞见的过程。这一方法显著减少了数据科学家在数据处理上的劳动强度,促使可扩展的数据驱动应用变得更加高效。该研究有助于推进数据科学领域的自动化程度,意义:影响数据驱动决策中的Agent应用。
👍 27
07/01 20:00
Embodied AI models now span vision-language-action (VLA) models and world-action models (WAMs), but practical deployment remains fragmented across model-specific Python stacks, backend assumptions, and robot-side glue code, especially on heterogeneous edge devices. Existing inference runtimes are de
中文介绍 提出了Embodied.cpp,一个可移植的嵌入式AI模型推理运行时,针对异构机器人设备的多样化应用场景。该平台解决了现有模型在实际部署中的碎片化问题,确保了一致的推理性能和可操作性。实验结果表明,该框架支持多种模型的无缝集成,显著提升了应用灵活性,意义:促进嵌入式AI在机器人领域的广泛应用。
👍 26
07/01 20:00
We present WorldDirector, a highly controllable video world model framework designed for persistent dynamic object memory and unrestricted viewpoint exploration. Unlike existing world models that entangle physical dynamics with pixel rendering and rely on continuous visual observation to sustain mot
中文介绍 推出了WorldDirector,一个高度可控的视频世界模型框架,支持持久动态对象记忆和无限视角探索。与传统世界模型不同,这一框架解耦了物理动态与像素渲染,提升了模型对环境变化的适应能力。实验表明,该模型更加强大且灵活,意义:为虚拟环境建模和控制提供新的可能性。
👍 20
06/29 20:00
Recent multimodal large language models have shown great promise in clinical image reasoning, but existing post-training pipelines remain predominantly outcome-centric, relying on final answer correctness or sequence-level preferences. This suffers from sparse credit assignment, making it difficult
中文介绍 针对最近在临床图像推理中的多模态大型语言模型,本文提出了一种分步强化学习的方法,以降低结果导向训练中稀疏信用分配的问题。实验显示,采用分步方法提高了多模态推理的准确性,为医疗领域的多模态推理提供了新的方向,意义:影响医疗推理系统中Agent的应用。
👍 17
06/30 20:00
Memory expertise is a learned skill: knowing what to encode, when to retrieve, and how to organize knowledge--a capacity known in cognitive science as metamemory. We bring this perspective to LLMs by treating memory management as a trainable skill. We promote file-system operations to first-class me
中文介绍 研究记忆作为认知技能的自动学习,提出了一种将文件系统操作提升为一流的记忆管理方法,帮助LLM提高记忆管理能力。实验证明该方法有效提升了模型在知识编码、检索和组织上的表现,推动了memory在LLM中的应用,意义:为Agent的知识管理系统提供了重要支持。
👍 17
06/30 20:00
In long-context use, large language models frequently synthesize answers from the meaning of a relevant context span rather than literally copy-pasting them. Identifying which attention heads perform this synthesis matters for interpreting long-context model behavior. Yet existing detectors miss the
中文介绍 通过分析注意力头在长上下文使用中的作用,提出了一种logit贡献评分方法,旨在识别进行语义综合的非字面检索头。此方法有效改善了对长上下文模型行为的解释性,实验结果显示该方法提高了模型的推理能力,意义:为理解和优化LLM的推理机制提供了新视角。
👍 17
07/01 20:00
Skills are becoming a reusable operational layer for LLM agents, encoding SOPs, domain rules, tool workflows, scripts, and validation routines. In realistic skill repositories, overlapping skills make reliable skill-use difficult. Final verifier success is too coarse for both evaluation and training
中文介绍 SkillCoach引入了自我演化的评估标准,以提高LLM代理的技能使用能力。研究通过分析重叠技能对可靠性评估的影响,提出了新的验证流程,实验证明这种方法能有效提高代理的任务完成率。该研究对技能管理在Agent应用中的重要性进行了探索,意义:推动LLM在复杂任务中的有效应用。
👍 11
06/25 20:00
Search agents powered by large language models (LLMs) are increasingly used to solve complex information-seeking tasks, requiring multi-step retrieval and reasoning to fulfill user goals. However, existing benchmarks often assume that user queries are complete and explicit, overlooking the fact that
中文介绍 研究表明,基于大型语言模型的搜索代理在复杂的信息检索任务中有着广泛应用,但现有基准未能考虑用户查询的不完整性。通过引入DiscoBench,分析何时搜索代理应该主动询问以获取更全面信息,显著提高了检索效果。该研究为改进信息检索系统提供了重要依据,意义:推动Agent在复杂信息检索中的表现。
👍 10
06/29 20:00
Controllable image generation methods, such as ControlNet, have demonstrated a remarkable capacity to introduce visual conditions(e.g., depth maps) to guide image generation. However, these methods often struggle with complex multi-instance scenes, frequently leading to attribute confusion among ins
中文介绍 研究了一种无实例标注的复杂图像生成控制方法,指出现有的控制方法在处理多实例场景时容易产生属性混淆。通过改进的生成框架,该方法在多个实例图像生成中表现出更强的控制能力,实验结果显示生成质量显著提升,意义:对复杂图像生成的应用提供了新的思路。
👍 10
06/29 20:00
This paper explores multi-turn visual reasoning and observes that MLLMs repeatedly fail to localize the target, leading to long, redundant trajectories. We attribute this failure to the entanglement of reasoning and perception within a single model, the MLLM reasons and localizes simultaneously, and
中文介绍 探讨了解耦感知与推理的多轮视觉推理问题,发现多模态大语言模型在目标定位时存在重复与冗长的问题。提出了一种解决方案,将推理与感知分离以提高定位精度,实验证明该方法增强了模型的视觉证据寻求能力,意义:提升商业和安全应用中的视觉推理效率。
👍 10
06/30 20:00
Accelerating materials discovery requires AI systems that can generate scientifically valid hypotheses through multi-step, domain-grounded reasoning. Standard large language models often produce fluent but weakly traceable responses to open-ended materials design problems, making it difficult to det
中文介绍 为加速材料发现,引入了一种基于图的强化学习方法,通过概念重组生成可追溯的科学假设。该方法解决了传统大型语言模型在开放式材料设计问题上的模糊性,实验结果表明生成的假设具备较高的可追溯性,意义:支撑科学研究中的先进假设生成能力。
👍 9
06/28 20:00
Large Language Model (LLM)-based agents can solve complex procedural tasks by interacting with environments over multiple turns, but this ability typically depends on large models, long contexts, and repeated inference calls. This makes advanced memory-augmented agents difficult to deploy on resourc
中文介绍 提出了DuoMem,通过双空间蒸馏实现能力强大的设备内记忆代理,以解决LLM在复杂任务中对资源密集型模型的依赖。实验表明该方法通过有效简化模型,保持了性能并提升了可部署性,意义:推动在资源有限的环境中使用记忆增强Agent的能力。
👍 9
07/01 20:00
Evaluating LLM agents on benchmarks like SWE-Bench and GAIA can be expensive, time-consuming, and requires complex infrastructure. A single evaluation can cost thousands of dollars and take days to complete. In contrast, non-agentic LLM benchmarks that test individual capabilities (e.g., reasoning,
中文介绍 开发了PACE,一种新型的标准化评估代理能力的方法,旨在简化LLM代理的评估流程,降低成本与时间。与现有基准相比,PACE显著提高了评估的可操作性和效率,为模型开发者提供了更友好的评估工具,意义:提高了Agent能力评估的便利性与有效性。
👍 8
07/01 20:00
Vision-Language Models (VLMs) have demonstrated immense promise in Spatio-Temporal Video Grounding (STVG). However, current evaluation protocols are largely confined to zero-shot assessments on general, daily-life benchmarks. This creates a critical disconnect from real-world applications in special
中文介绍 推出了AnyGroundBench,一个专门领域的视频基准,旨在改进视觉语言模型在时空视频定位中的评估。研究指出,现有评估程序往往缺乏针对专业领域的适用性,提出了一种新的协议进行有效测试。该开发为视频理解内容中的Agent应用提供了广泛支持,意义:推动视频理解任务的研究与应用。
👍 7
07/01 20:00
Foundation models are routinely released to the public, yet the data recipes used to train them -- such as domain mixture weights that determine how different sources are sampled -- are rarely disclosed. This creates an access asymmetry: researchers study the resulting models but lack visibility int
中文介绍 提出了WARP,一种用于恢复训练数据组合的权重空间分析方法,旨在解决传统基准中数据透明性不足的问题。该方法为研究人员提供了较好的数据采样视角,有效提高了对基础模型的理解与复现能力,意义:促进基础模型构建的可解释性与透明性。
👍 7
06/25 20:00
Benchmarks are widely used to evaluate task completion by Large Language Models (LLMs), but this approach has accumulated construction-validity problems, and a passing score may not show whether the requested task was delivered. We study both problems. In a controlled code-as-spec setup, two product
中文介绍 研究了在编码代理的任务完成时,基于基准的评估可能存在的构造有效性问题,尤其是在产品交付方面存在误差。通过对两种模型的比较,该研究提出了更合理的验证方法,以确保任务交付的准确性,意义:改善LLM在完整任务输出中的可靠性与有效性。