👍 64
06/01 08:00
We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-transformers architecture. By supporting highly flexible input-output configurations, Cosmos 3 seamlessly unifies critica
中文介绍 Cosmos 3是一族全模态世界模型,采用统一的mixture-of-transformers架构,联合处理与生成语言、图像、视频、音频和动作序列。支持灵活输入输出配置,统一关键模态,为物理AI提供基础。
👍 44
06/01 08:00
Deep-research agents solve tasks through long trajectories of search, tool use, evidence inspection, and answer synthesis. Evaluation based on final answers shows whether an agent succeeds, but not which parts of the trajectory make the answer unreliable. We study span-level error localization for d
中文介绍 针对深度研究代理长轨迹中的错误定位,提出span级错误定位方法。通过分析搜索、工具使用、证据检查等环节,定位导致答案不可靠的具体片段,超越仅基于最终答案的评估。
👍 35
06/03 08:00
Rubric-based reinforcement learning (RL) uses an LLM-as-a-Judge (LaaJ) to score model outputs according to rubrics as rewards. However, policy models may exploit latent biases in the judge, leading to reward hacking and ineffective or unsafe training outcomes. In real-world rubric-based RL, such hac
中文介绍 在基于评分的强化学习中,策略模型可能利用LLM评判器的潜在偏差导致奖励破解。本文重现并分析了此类破解,提出检测方法以保障训练安全性,避免无效或不安全结果。
👍 28
06/02 08:00
Multimodal agents in robotics, AR, and autonomous driving must reason about places and layouts from continuous egocentric streams, often using evidence outside the current view. Existing benchmarks either evaluate offline over full videos or target events rather than spatial structure. We introduce
中文介绍 提出OVO-S-Bench,用于评估多模态大模型在连续自中心视频流中的空间推理能力。通过层次化基准测试,考察模型对超出当前视野的空间布局和场所的理解,面向机器人、AR和自动驾驶。
👍 24
06/02 19:21
Large Reasoning Models (LRMs) have achieved remarkable progress thanks to Reinforcement Learning with Verifiable Rewards (RLVR) on Chain-of-Thoughts (CoTs). However, since long CoTs naturally contain trial and errors and mainstream RLVR approaches choose outcome-correct CoT trajectories for memoriza
中文介绍 大推理模型通过可验证奖励的强化学习在思维链上取得进展。但长CoT包含试错,主流RLVR只选择结果正确的轨迹。提出ThoughtFold,通过内省偏好学习折叠推理链,改进训练效率。
👍 24
06/03 08:00
As multi-modal models advance towards long-form video understanding, memory emerges as a critical capability. Despite substantial efforts in developing video datasets and benchmarks, existing works primarily focus on perception and reasoning, without systematically evaluating memory: what models ret
中文介绍 多模态模型长视频理解中记忆能力至关重要。现有基准聚焦感知与推理,缺乏系统记忆评估。提出M^3Eval,通过认知启发的视频任务评估模型记忆能力。
👍 22
06/03 08:00
Multi-agent reasoning systems adopt a "generate-then-transfer" paradigm that forces end-to-end latency to scale linearly with pipeline depth. We introduce StreamMA, a multi-agent reasoning system that streams each reasoning step to downstream agents as soon as it is generated, pipelining adjacent ag
中文介绍 多智能体推理系统采用「生成-传递」范式导致延迟随流水线深度线性增长。提出StreamMA,将每一步推理结果流式传输给下游智能体,实现相邻智能体流水线化,降低端到端延迟。
👍 20
05/26 08:00
LLM agents are rapidly evolving from coding assistants into autonomous software engineering systems. However, existing evaluation methodologies remain largely centered on static, isolated, and short-horizon benchmarks that fail to capture the dynamic complexity of real-world production workflows. As
中文介绍 LLM代理从代码助手演进为自主软件工程系统,现有静态基准无法捕捉生产动态复杂性。提出RAMP,用于运行时评估代理模型在生产系统中的表现,弥补基准不足。
👍 13
06/02 08:00
Memory is an indispensable capability for long-horizon LLM agents, enabling them to preserve and utilize information accumulated across extended interactions. Existing memory-agent approaches are typically trained end-to-end with reinforcement learning on downstream tasks. However, collecting high-q
中文介绍 长时LLM代理需要记忆能力。现有方法需在下游任务上用RL端到端训练,但高质量长期交互数据难收集。提出MemTrain,通过自监督方式训练上下文记忆,无需下游任务标签。
👍 12
06/01 17:50
Abundant procedural knowledge on the Web holds great potential for helping agents solve long-horizon tasks. However, such knowledge is often multimodal, heterogeneous, noisy, and implicitly assumes human executors, making it difficult to use directly as the skills required by agents. To bridge the g
中文介绍 Web上丰富的程序知识是异构、多模态、带噪音的,且隐含人类执行者假设。提出MMG2Skill,让代理从野外指南中蒸馏出可自我进化的技能,弥合知识与代理所需技能之间的鸿沟。
👍 11
06/01 08:00
On-Policy distillation (OPD) in large language models is shifting from full-trace KL supervision toward more selective training paradigms. Recent OPD methods increasingly focus on selecting which trajectories to learn from, which tokens are most informative, and which supervision signals are most re
中文介绍 在线策略蒸馏正向选择性训练范式转变。本文重新思考优化粒度,提出先过滤再重加权的方法,选择更有信息的轨迹、令牌和监督信号进行学习,提升蒸馏效率。
👍 11
06/03 08:00
Lane-level maps are critical infrastructure for autonomous driving and lane-level navigation, yet constructing and maintaining standardized lane networks for hundreds of cities remains highly labor-intensive. Recent end-to-end vectorized mapping methods can predict lane geometry and topology directl
中文介绍 车道级地图是自动驾驶的关键基础设施,但人工构建数百城市的标准车道网络劳动密集。提出MapAgent,一个工业级代理框架,通过智能体协作自动生成车道几何与拓扑。
👍 10
06/03 08:00
Feed-forward 3D Gaussian Splatting methods reconstruct a scene from posed or pose-free images in a single forward pass, yet current approaches predict one Gaussian per input pixel, tying the representation budget to camera resolution rather than scene complexity. A flat wall and a richly textured ob
中文介绍 前馈3D高斯泼溅方法每像素预测一个高斯,表示预算受限于相机分辨率而非场景复杂度。提出ZipSplat,以更少高斯实现更好的泼溅效果,根据场景复杂度自适应分配表示资源。
👍 10
06/02 23:28
High-quality pretraining data is a central ingredient in modern language models, but German-language resources remain far less developed than their English counterparts: they are often smaller, less carefully curated, weakly documented, and rarely validated through controlled training experiments. W
中文介绍 高质量预训练数据对语言模型至关重要,但德语资源远少于英语:规模小、筛选差、文档弱。提出KletterMix,旨在系统构建高质量德语预训练数据,并通过受控训练实验验证。
👍 9
06/03 08:00
Scientific and engineering progress is fundamentally a long-horizon iterative process: proposing changes, running experiments, measuring outcomes, and continuously refining artifacts. Yet existing benchmarks for frontier models primarily evaluate either single-turn responses or short-horizon agent t
中文介绍 科学和工程进步本质是长周期迭代过程:提出变更、运行实验、测量结果、优化工件。现有基准评估单轮或短周期任务。提出AutoLab,评估前沿模型在自动科研和工程长周期任务上的能力。
👍 8
06/02 14:29
Existing benchmarks for MLLM-generated web artifacts assess interaction through local evidence and miss the requirement-induced states and transitions that determine whether a page works. We introduce WebRISE, which compiles task requirements into Interaction Contract Graphs (ICGs) of observable sta
中文介绍 现有MLLM生成网页工件基准通过局部证据评估交互,忽略需求诱导的状态和转换。提出WebRISE,编译任务需求为交互合约图(ICG),对可观测状态和转换进行验证。
👍 6
06/02 08:00
Structured financial audit verification is difficult for language-model agents because correctness depends on structured evidence rather than text alone. A model must link reported facts to taxonomy concepts, traverse calculation or dimensional relations, and recompute expected values before applyin
中文介绍 结构化财务审计验证对语言模型代理困难,因为正确性依赖于结构化证据而非文本。提出AUDITFLOW,构建可执行符号环境,让代理链接报告事实与分类概念、遍历计算关系并重新计算预期值。
👍 5
06/01 08:00
How can a population of agents self-orchestrate and self-adapt into stronger collective intelligence without centralized control? Inspired by Friedrich Hayek's economic theory of decentralized coordination in markets, we study this question through an agent economy in which agents compete via auctio
中文介绍 受哈耶克经济理论启发,研究去中心化市场中智能体如何通过拍卖竞争自我组织为更强的集体智能。提出智能体经济,无需中心控制,通过经济交互涌现集体智慧。
👍 5
06/02 08:00
Computer-use agents extend language models from text generation to sustained interaction with files, terminals, browsers, and external tools. This shift creates safety risks that are difficult to detect from isolated prompts or final responses, because harm often emerges only through multi-step exec
中文介绍 计算机使用代理在文件、终端、浏览器等持续交互中引入安全风险,且危害常通过多步操作显现。提出BraveGuard,防范开放世界威胁,保障代理安全。
👍 4
05/28 08:00
Weight-space model merging is usually formulated as an algebraic operation on checkpoints, yet at LLM scale the limiting resource is often the set of expert weights that must be read. We introduce MergePipe, a budget-aware execution layer that casts LLM merging as an expert access-set problem: given
中文介绍 权重空间模型合并通常视为代数操作,但在LLM规模下限制因素是专家权重读取集。提出MergePipe,一种预算感知的执行层,将LLM合并建模为专家访问集问题,优化读取预算以实现可扩展合并。