👍 261
08/14 08:00
Recent video generators can fabricate realistic depictions of wars, disasters, public emergencies, and other real-world crises, creating substantial risks of misinformation. Existing benchmarks, however, provide limited evidence on detector and generator behavior in such settings, including how dete
中文介绍 针对AI生成视频对战争、灾难等真实危机事件的虚假伪造风险,系统评估检测器与生成器在此类场景下的表现,并考察社交传播的影响。现有基准缺乏相关证据,该研究构建系统化评测框架,为防御AI生成视频攻击提供实证参考。
👍 44
08/13 08:00
Autonomous agents are increasingly capable of improving models, systems, and other technical artifacts through long-horizon experimentation. To understand the current state of this capability, however, evaluation must go beyond final scores, which neither reveal where progress is gained or lost nor
中文介绍 针对自主智能体在长期AI研发任务中的评估局限,提出超越最终得分的系统化评估方法,以揭示能力提升或损失的来源。重点分析智能体在长时间实验中的过程表现,而非仅看重结果分数,为理解和发展长期研发型智能体提供更细粒度参考。
👍 30
08/15 08:00
Constructing an interactive 3D open world from a user query is important. However, existing methods are primarily evaluated on idealized, simple queries, making it difficult to systematically analyze and compare how multimodal agents understand user intent, use 3D tools, and reason over textual and
中文介绍 研究多模态智能体能否根据用户查询端到端构建交互式3D开放世界。现有评估局限于理想化简单查询,难以比较智能体理解用户意图、使用3D工具及跨文本图像推理的能力。该工作提出系统化评测与分析框架,探讨多模态智能体构建3D世界的可行性与局限。
👍 28
08/14 08:00
On-policy distillation (OPD) offers a promising way to transfer reasoning capabilities from stronger teacher models, but applying it to long-context reasoning teachers and short-context students introduces practical challenges, including tokenizer mismatch, teacher-student distribution mismatch, res
中文介绍 提出SimpleOPD,一种简单且与tokenizer无关的on-policy蒸馏方法,用于将长上下文推理能力从强教师模型迁移到短上下文学生模型。针对tokenizer不匹配、师生分布不匹配等问题进行优化,降低长上下文推理蒸馏的实现难度。
👍 23
08/17 08:00
A benchmark should deliver more than a scalar score: what makes an evaluation trustworthy is the reasoning that justifies the score. This is especially critical for world models, where judging a rollout requires understanding whether physics, causality, and world state evolve correctly. Humans spot
中文介绍 提出HarnessEval-W,将视觉世界模型的评估“智能体化”,不仅给出标量分数,还生成支撑评分的推理过程。针对物理、因果和世界状态演化等关键维度,模拟人类识别错误的能力,使评估更可信、更能解释。
👍 21
08/13 08:00
Current large language model development relies on massive, often non-permissible datasets, creating a high barrier for researchers committed to open-source and ethically sourced data. We introduce Mimir v1, a 1-billion-parameter language model based on the Hierarchical Reasoning Model (HRM) archite
中文介绍 发布Mimir v1,一个仅使用许可后训练数据的10亿参数开放语言模型,基于层次推理模型(HRM)架构,在1B规模上实现前沿性能。旨在降低依赖不可许可数据的研究门槛,推动开源与数据伦理合规的模型发展。
👍 20
08/11 08:00
The next generation of AI agents is increasingly moving beyond systems that answer isolated questions toward persistent personal assistants that can understand, remember, and continuously learn from users' experiences. Such assistants require long-term memory to accumulate and leverage user-specific
中文介绍 面向持久化个人助理场景,提出MobileMem,让AI智能体从一年的移动端使用经验中学习,构建长期记忆以积累并利用用户特定信息。解决孤立问答之外的记忆与持续学习问题,提升助理对用户需求的理解和个性化能力。
👍 19
08/17 08:00
Agent harnesses have substantially improved performance on long-horizon tasks by coordinating agent interactions with the environment. However, reinforcement learning through complex harnesses remains largely unexplored, as scaling such training to long-horizon agent tasks introduces fundamental cha
中文介绍 探索智能体交互框架(agent harness)上的黑盒强化学习。尽管agent harness能协调智能体与环境交互、提升长程任务表现,但通过复杂harness进行RL训练仍面临根本性扩展难题。该研究系统分析这些挑战,并为智能体harness的强化学习训练提供思路。
👍 17
08/13 08:00
Enabling agents to learn from experience and internalize it into their policy has become a central problem in self-evolving AI. On-policy self-distillation (OPSD) offers an effective pathway by using a privileged self-teacher to provide dense supervision on the student's own trajectories; however, e
中文介绍 针对自进化AI中智能体从经验学习并内化到策略的问题,提出潜在on-policy自蒸馏方法。传统OPSD依赖特权自教师对学生轨迹提供密集监督,但存在局限;该工作在潜在空间中进行自蒸馏,提升学习效率与策略内化能力。
👍 15
08/13 08:00
Humanoid motion tracking is central to teleoperation and whole-body imitation, yet evaluation often disagrees with what people perceive in videos. Kinematic errors average per-frame pose differences but miss the physical artifacts that matter most, particularly unstable support and incorrect contact
中文介绍 提出HumanTracker,一个全面且与人类感知对齐的人形运动跟踪基准。现有运动跟踪评估使用逐帧姿态误差,但忽视了不稳定支撑、错误接触等物理伪影,与人类视频感知不一致。该基准纳入物理合理性指标,改进评估方式。
👍 14
08/14 08:00
Large language model (LLM) agents are evolving from conversational assistants into autonomous systems that execute long-horizon tasks through reasoning, tool use, code generation, and workspace manipulation. As agents increasingly operate over persistent environments and multi-step workflows, they f
中文介绍 提出Agentic Transaction概念,推动LLM智能体系统满足ACID事务特性(原子性、一致性、隔离性、持久性)。随着智能体在持久化环境与多步工作流中自主执行,面临可靠性问题,该研究将数据库事务原则引入智能体系统,提升任务执行的正确性与稳定性。
👍 13
08/16 08:00
Foundation GUI agents can automate complex digital tasks, but deployment is hindered by scarce and biased training data, ambiguous prompts, and unreliable execution. Routine workflows rely on user-specific tools and tacit conventions, so unstated instructions can produce arbitrary variations across
中文介绍 提出UI-Mate,通过上下文演示(in-context demonstrations)提升开放权重基础GUI智能体的能力。针对训练数据稀缺有偏、提示模糊和执行不可靠的问题,利用示例演示传递用户特定工具与隐性约定,减少指令未说明导致的行为差异,提高复杂数字任务自动化成功率。
👍 13
08/17 08:00
The rapid evolution of text-to-image (T2I) generation models has effectively solved the foundational challenge of raw pixel synthesis, shifting the community's focus toward fulfilling increasingly intricate user requests. While recent agentic image generation workflows enhance static inference with
中文介绍 提出GenRouter,面向智能体图像生成的统一工作流路由框架。随着T2I模型解决基础像素生成,复杂用户请求成为焦点。GenRouter在静态推理基础上引入工作流路由,根据请求选择合适的生成流程,以完成更精细的图像生成任务。
👍 13
08/14 08:00
With the rapid advancement of image editing models and their widespread application across various domains, there is an increasingly urgent need to deploy these model capabilities directly into real-world scenarios. However, existing benchmarks remain confined to simple single-image tasks, suffering
中文介绍 提出CPI-Bench,一个全面、实用且智能的真实世界图像编辑基准。现有基准局限于简单单图任务,难以支撑实际部署。该基准构建更贴近真实场景的评测体系,覆盖复杂编辑需求,推动图像编辑模型从实验室走向实际应用。
👍 12
08/15 08:00
Frontier agentic systems powered by large language models (LLMs) exhibit human-like patterns of cognition. As these systems become deeply integrated across different domains, their cognitive engagement raises critical concerns for human society that remain insufficiently studied. To address this gap
中文介绍 研究LLM驱动的前沿智能体系统展现类人认知模式所引发的风险。随着这类系统在各领域深度集成,其认知参与可能带来社会层面的关键隐患,但相关研究不足。该工作系统分析认知诱发风险,提出风险分类或治理框架,以提升安全性与可控性。
👍 12
08/14 08:00
Part-aware 3D object generation is essential for graphics applications such as controllable modeling, editing, and articulation, where objects are represented as coherent assemblies of semantic parts. However, existing part-aware generation methods, do not scale well to highly complex objects. As th
中文介绍 提出MegaParts,通过token高效的自回归建模,将部件感知3D物体生成扩展到300个部件。现有方法难以应对高复杂物体,该工作以语义部件为组装单元,提高可扩展性,支持可控建模、编辑与关节化等图形学应用。
👍 12
06/27 09:56
The human brain exhibits a striking degree of functional specialization, with distinct networks supporting language, formal reasoning, reasoning about other minds, and reasoning about the physical world. Is this modular organization a fundamental principle of how intelligent systems must be built, o
中文介绍 发现大语言模型会涌现出类似人脑的模块化认知架构。人类大脑具有功能特化,不同网络分别支持语言、形式推理、心理推理和物理世界推理;该研究检验LLM中是否出现同样的模块化组织,为理解智能系统结构原理提供证据。
👍 11
08/14 02:04
LLM agents in the ReAct paradigm alternate between reasoning, acting, and observing, but deliberate reasoning is confined to the Thought phase: while the agent serializes an action and waits for the environment, its reasoning is frozen. We identify this recurring interval for Action and Observation
中文介绍 针对ReAct范式中LLM智能体仅在Thought阶段推理、在Action与Observation阶段推理空闲的问题,提出“Second Thought”并行推理机制:在智能体等待环境响应时同步进行推理,充分利用行动与观察之间的时间,提升长程任务中的推理效率和决策质量。
👍 10
08/17 08:00
In cognitive science, resource rationality asks how an agent should allocate limited computation to maximize expected value. Most reasoning and agent benchmarks use independent per-task budgets; existing shared-budget studies do not calibrate suite performance against the same model's demonstrated s
中文介绍 提出R^3-Bench,评测LLM在共享预算下的资源理性推理能力。现有基准多为独立任务预算,无法反映真实有限计算分配。该基准要求模型在多个任务间统筹分配资源、最大化总期望价值,结果发现LLM在此类共享预算推理上表现显著不足。
👍 10
08/12 08:00
We propose claim-level falsification as a principle for test-time scaling and instantiate it through Claim-Level Reliability Assessment (CLR), a training-free framework that reallocates test-time compute from additional solution sampling to targeted verification. Since whole-trace evaluation often o
中文介绍 提出基于声明级证伪的测试时扩展原则,并实现CLR框架。该免训练方法将测试时算力从额外采样答案转向针对性验证,逐条检查推理轨迹中的声明可靠性,避免整条轨迹评估的低效,提升推理效率与准确性。