👍 197
09/23 08:00
Object permanence and solidity are hallmarks of human cognitive priors. Recent studies show that video generation models, a paradigmatic class of current world models, have begun to show emerged reasoning abilities, making them ideal candidates for building human-like physical intelligence. Do video
中文介绍 针对视频生成类世界模型是否具备客体永久性与固体性认知先验,本文提出在 world models 中训练客体永久性的方法,考察模型对物体被遮挡后仍持续存在、不可穿透等物理规律的推理能力,并给出评估与训练方案,为构建类人物理智能提供依据。
👍 83
09/22 08:00
Long-term conversational memory in multi-party settings requires more than retrieving relevant content from long-term conversations: it must distinguish who said what, whom each statement concerns, how individuals perceive one another, what information is shared by the group, and how states change o
中文介绍 针对多方可对话中长期记忆需区分谁说了什么、语句涉及谁、个体间认知及群体共享信息等问题,提出以说话人为中心的双轨记忆框架 SpeakerMem-R1,分别建模个体与群体状态及其动态变化,提升多方对话中的记忆检索与推理表现。
👍 64
09/24 08:00
While Large Language Models (LLMs) rely on highly non-linear components, in this work we demonstrate that they exhibit fundamental linearity: when inputs from distinct text streams are linearly combined, the model outputs a superposition of the individual next-token distributions. We term this the S
中文介绍 研究发现尽管 LLM 含大量非线性组件,却表现出基础线性性:不同文本流的输入线性组合时,输出为各自下一 token 分布的叠加,称为线性叠加现象。该发现为理解 Transformer 内部表示与多流信息处理提供了新视角。
👍 47
09/19 08:00
Spatial reasoning is essential for vision-language models (VLMs) to understand and act in the physical world. Reasoning in dynamic environments requires VLMs to perceive local state transitions caused by object motion and viewpoint changes and integrate them over long trajectories to maintain an upd
中文介绍 为提升 VLM 在动态环境中的空间推理能力,提出 Spatial-Interactor,让模型通过与可观测物理世界交互,感知由物体运动与视角变化引起的局部状态转移,并在长轨迹上整合以维持更新的空间理解,增强物理世界中的推理与行动能力。
👍 41
09/21 08:00
Evaluating world models requires assessing both the quality of the worlds they generate and their consistency and responsiveness under exploration, interaction, and modification. We introduce HappyWorld-Bench, a comprehensive benchmark that evaluates whether generated worlds remain reliable as agent
中文介绍 提出综合基准 HappyWorld-Bench,用于评估世界模型生成世界的质量,以及在探索、交互与修改下的一致性和响应性,考察生成世界能否作为可靠环境持续支撑智能体行为,为世界模型的系统化评测提供统一标准。
👍 39
09/23 08:00
Advances in generative models have improved video fidelity, enabling long-horizon generation, interactive world modeling, and evolving visual environments. Autoregressive (AR) video generation extends visual sequences through causal rollouts. However, a fundamental bottleneck emerges: as the generat
中文介绍 针对自回归视频生成在长时程 rollout 中随序列增长出现的一致性与记忆退化瓶颈,提出以记忆机制利用历史帧框定未来生成,缓解因果外推中的误差累积,提升长时视频生成与交互式世界建模的稳定性。
👍 34
09/23 08:00
Agentic memory systems reuse past experience to improve future performance, yet most existing designs curate memory at write time: once a task is completed, its trajectory is distilled into a fixed artifact, such as a reflection, workflow, skill, or reasoning strategy, that is later retrieved by sim
中文介绍 现有智能体记忆系统多在写入时固化反思、工作流或技能,检索时难以适配新任务。本文提出 Just-in-Time Memory,让 LLM agent 在任务执行时按需整理与筛选记忆,实现任务自适应记忆管理,提升跨任务复用效果。
👍 20
09/20 08:00
Humans can effortlessly localize the direction of a sound source and integrate it with visual cues for reasoning, yet this remains challenging for embodied agents. In particular, it is still unclear how to effectively evaluate and model spatial audio understanding in embodied settings. To address th
中文介绍 针对具身智能体如何评估与建模空间音频理解尚不明确的问题,提出 OmniEcho,研究声源方向定位并与视觉线索融合进行推理的方法,构建相应的评估与建模框架,推动具身场景下空间音频感知能力的发展。
👍 17
09/23 08:00
Recent advances in large language models (LLMs) have enabled agents to tackle long-horizon tasks across diverse environments. To further improve agent performance, existing language world models typically predict environment observations, yet reconstructing high-entropy, execution-dependent tool res
中文介绍 现有语言世界模型多预测环境观测,重建高熵的工具执行结果成本高。本文提出 Agent-Editing World Model,改以编辑方式建模环境状态变化而非逐字重建观测,为 LLM agent 的长时程规划提供更高效的世界建模思路。
👍 16
09/22 08:00
Reinforcement learning has become a central component of large language model (LLM) post-training, yet token-level credit lacks a generally accepted mathematical definition, leaving its relationship to commonly used training signals unclear. We formulate three regularity conditions, namely Completen
中文介绍 针对 LLM 后训练中 token 级 credit 缺乏公认数学定义、与常用训练信号关系不清的问题,提出完整性等三条正则性条件,将 credit assignment 与 critic alignment 相联系,为 RLHF/RL 训练信号设计提供理论框架。
👍 15
09/22 08:00
The rapid capability gains of frontier language models are widely attributed to improved reasoning abilities, yet this cannot be verified as raw CoT traces in closed-source systems are hidden. By registering a simple custom tool through a standard API feature, we induce frontier models to externaliz
中文介绍 闭源前沿模型的原始 CoT 不可见,其推理能力提升难以验证。本文通过标准 API 注册自定义工具,诱导前沿模型外显隐藏的思维链,进而提取并刻画其结构特征,为理解与评估前沿模型的隐式推理行为提供新手段。
👍 13
09/24 08:00
Rufus-Air is an open and reproducible post-training recipe on GLM-4.5-Air-Base (106B-A12B), organized as a serial pipeline of eight stages: SFT, Reasoning RL, Coding RL, Instruction-Following RL, General Agent, Coding Agent, Search Agent, and RLHF. We document the data, reward design, infrastructure
中文介绍 发布基于 GLM-4.5-Air-Base(106B-A12B)的开源可复现后训练配方 Rufus-Air,串联 SFT、推理 RL、代码 RL、指令遵循 RL、通用/代码/搜索 agent 与 RLHF 共八阶段,并公开数据、奖励设计与基础设施细节。
👍 12
09/23 08:00
Language-model agents increasingly face long-horizon tasks with evolving state, interdependent decisions, and delayed outcomes. Scaling their training requires diverse agentic environments, dependable outcome signals, and low extension cost. Existing generation pipelines commonly construct an enviro
中文介绍 针对语言模型 agent 长时程训练环境稀缺、结果信号不可靠且扩展成本高的问题,提出从已解机制出发生成可验证的隐藏动力学任务环境,自动构造多样且带可靠结果信号的 agentic RL 训练环境,降低环境扩展成本。
👍 11
09/20 08:00
Robotic bin packing requires long-horizon sequential decision-making, as each object placement affects the available space for subsequent packing. Existing methods primarily rely on hand-crafted geometric heuristics that optimize predefined objectives or reinforcement learning policies learned throu
中文介绍 面向机器人装箱这一长时程序贯决策任务,提出综合框架 PackLab,用于开发、训练与评估多模态大模型;突破依赖手工几何启发式或强化学习的局限,提供数据、训练与评测一体化方案,推动 MLLM 在装箱场景的应用。
👍 11
09/23 08:00
Vision-Language-Action models provide a strong foundation for general-purpose robot control, yet a vast majority of policies do not preserve and leverage episode-level information beyond the current observation. This limitation is consequential in history-dependent manipulation tasks that depend on
中文介绍 多数 Vision-Language-Action 策略仅依赖当前观测,难以应对依赖历史信息的操作任务。本文提出 MemBodied,为 VLA 模型引入循环式联想记忆,保存并利用 episode 级信息,提升历史依赖型操作任务的表现。
👍 10
09/24 08:00
The rapid progression of large language models is extending AI from passive content generation into the active workflows of engineering and scientific discovery. This shift raises a compelling question: can AI be both the object of development and an active participant in building next-generation AI
中文介绍 提出闭环 AI-for-AI 框架 Qwen-Planner-Agent,将 LLM 同时作为开发对象与开发参与者,用于真实移动端 planner agent 的构建与迭代,通过自动评估与反馈闭环持续优化规划能力,探索 AI 自主参与下一代 AI 系统研发的可行路径。
👍 10
09/23 08:00
We present Hunyuan-A13B, an open-source large language model based on a Mixture-of-Experts architecture. It contains 80 billion total parameters but activates only 13 billion during inference, balancing model capability, computational efficiency, and deployment cost. The model is pretrained on a rig
中文介绍 开源大模型 Hunyuan-A13B,采用 MoE 架构,总参数 800 亿、推理仅激活 130 亿,兼顾能力、计算效率与部署成本。基于大规模语料预训练并给出完整技术细节,在多项基准上验证了稀疏激活设计的性能与效率平衡。
👍 9
09/24 08:00
Task and motion planning (TAMP) problems remain difficult even with full observability and object-centric states because discrete decisions are tightly coupled to geometric, kinematic, and dynamic constraints. Generalized TAMP addresses this difficulty by exploiting regularities across problem insta
中文介绍 任务与运动规划(TAMP)中离散决策与几何、运动学、动力学约束紧密耦合,即使状态全观测仍难求解。本文提出面向广义 TAMP 的编码智能体(coding agents),利用问题实例间的规律性自动生成求解方案,提升跨实例泛化能力。
👍 9
09/24 08:00
Deep search requires LLM agents to decompose complex queries, search for evidence, and synthesize grounded answers, yet existing ReAct-style agents suffer from two limitations: role coupling, where one policy must handle planning, evidence use, and synthesis; and context accumulation, where growing
中文介绍 针对深度搜索 agent 中角色耦合(单一策略兼做规划、证据利用与综合)和上下文累积问题,提出 IterSynth,通过角色解耦的迭代式合成方法分离各职能并控制上下文,提升复杂查询分解、证据检索与有据回答的效果。
👍 9
09/23 08:00
AI research agents need reliable knowledge of how their experiments change outcomes. We introduce WhatWorkedBench to measure experimental understanding, the accuracy of predictions about component changes after budgeted experimentation. Agents inspect code, select measurements, and submit a response
中文介绍 提出 WhatWorkedBench,用于衡量 AI 科研智能体对实验的理解能力,即在其查看代码、选择度量并进行有限预算实验后,预测组件改动所带来结果的准确性,为评估智能体的实验性理解提供可量化基准。