👍 285
07/30 08:00
Chemistry literature synthesis often requires assembling specific findings scattered across many publications, yet existing literature-search systems primarily return ranked document lists. As a result, scientists and AI agents need to locate relevant information, verify their provenance, and assemb
中文介绍 面向化学文献综合,提出以声明为中心的基础设施,将散落多篇文献的具体发现组装为可验证的综合结果,帮助科学家和AI代理定位信息、核查来源,弥补传统检索只返回文档列表的不足。
👍 264
07/30 08:00
GUI agents have the potential to become a general purpose executor over existing digital devices. To advance them toward real-world use, we envision agents that operate reliably on real devices, execute workflows across platforms, combine GUI interaction with CLI execution, complete long-horizon tas
中文介绍 提出面向真实世界的下一代 GUI 智能体 Qwen-UI-Agent,目标在真实设备上可靠操作、跨平台执行工作流、结合 GUI 交互与 CLI 执行,完成 long-horizon 任务,推动 GUI 智能体从通用执行器走向实际应用。
👍 240
07/29 08:00
Recent advances in AI agents have increasingly internalized native capabilities into their underlying foundation models, giving rise to multimodal foundation models and large reasoning models. However, agent memory is still primarily implemented through external modules, leaving the native memory ca
中文介绍 提出记忆基础模型 Metis,将智能体记忆从外部模块内化为模型原生能力,解决现有 AI 智能体主要依赖外部记忆模块、缺乏原生记忆能力的问题,为多模态基础模型和大推理模型提供统一记忆机制。
👍 150
07/30 08:00
We introduce PhiZero, a physical world model built around physical language, a compact discrete representation of world-state transitions. Existing physical world models typically predict future videos directly in pixel space, leaving the underlying world dynamics implicit within high-dimensional vi
中文介绍 提出基于物理语言(physical language)的世界模型 PhiZero,用紧凑的离散世界状态转移表示替代直接在像素空间预测未来视频,使物理世界模型显式建模底层动态,避免高维视频中隐式推断的局限。
👍 143
07/30 08:00
Recursive self-improvement (RSI) requires AI systems that improve the process of building AI (i.e., AI4AI); machine learning engineering (MLE) offers a concrete, executable testbed for studying this capability. We introduce OpenMLE, an open full-stack system for RSI research in MLE, spanning verifia
中文介绍 面向机器学习工程的递归自我改进(RSI),提出 OpenMLE 全栈系统和 Frontis-MA1 模型,覆盖可验证的 AI4AI 训练流程,让 AI 改进构建 AI 的过程,在机器学习工程这一可执行测试床上研究递归自我改进。
👍 87
07/29 08:00
Existing autoregressive video distillation methods commonly adopt a Distribution Matching Distillation (DMD)-based multi-stage pipeline. However, they typically decouple the initialization and DMD stages -- which then pursue different target distributions -- and judge the intermediate student mainly
中文介绍 提出 DistillAlign,协调自回归视频蒸馏中模式覆盖(mode covering)与模式寻求(mode seeking)的冲突,改进 DMD 多阶段流程中初始化与蒸馏阶段目标分布不一致的问题,提升中间学生模型的训练质量。
👍 61
07/29 08:00
Text-to-video models have achieved remarkable visual quality, yet they still struggle to generate physically consistent dynamics because the temporal evolution of a scene must be inferred implicitly from a highly compressed text prompt. Existing chain-of-thought approaches introduce intermediate pla
中文介绍 提出 VideoCoCo,以代码作为思维链(Code-as-CoT)的智能体双引擎系统,为文生视频生成显式物理一致的计划,解决文本提示压缩导致场景时间演化推断困难、物理动态不一致的问题。
👍 45
07/30 08:00
Decoder-only language models entangle long-term memory and reasoning in a single parameter set, making it difficult to scale memory capacity independently. Memory Decoder introduces a parametric long-term memory module but only studies it at a relatively small scale. In this work, we present Memory
中文介绍 将参数化长期记忆模块 Memory Decoder 扩展到更大规模,解决 decoder-only 语言模型中长期记忆与推理纠缠在同一参数集、记忆容量难以独立扩展的问题,实现可预训练、可扩展的长期记忆。
👍 44
07/30 08:00
The fundamental goal of agentic visual reasoning is to improve the success rate of multimodal large language models (MLLMs) on complex tasks, rather than merely equipping them with a sophisticated yet inefficient reasoning paradigm. In this work, we rethink agentic visual reasoning through two key d
中文介绍 提出 Beacon,聚焦多模态大模型(MLLM)智能体视觉推理的时机与方式,目标是提升复杂任务成功率而非堆砌低效推理范式,通过判断何时调用外部工具、如何执行视觉推理来优化 agentic reasoning。
👍 42
07/28 08:00
Real-world tasks often require models to learn from task-specific context rather than relying only on pre-trained knowledge. While recent work has highlighted this capability as context learning, existing evaluations mainly focus on textual contexts. In many practical settings, however, the context
中文介绍 提出 CLBench-V,从 grounding 到知识获取系统评估多模态上下文学习,覆盖真实任务中需要从任务特定上下文学习的场景,弥补现有评测主要关注文本上下文、忽略多模态上下文的不足。
👍 38
07/30 08:00
Retrieval-augmented generation (RAG) spans lexical and dense retrieval, graph-based indexing, and agentic search, but these paradigms are usually evaluated on different benchmarks at one corpus size, leaving their accuracy-cost scaling unclear. To bridge this gap, we present a controlled study that
中文介绍 对词法检索、稠密检索、图索引和智能体搜索等 RAG 范式进行受控规模研究,比较不同语料规模下的准确率与成本,发现 BM25 在扩展时仍有竞争力,为 RAG 范式选型提供依据。
👍 34
07/30 08:00
Embodied intelligence faces a fundamental data bottleneck. Models must capture how first-person perception, whole-body motion, dexterous manipulation, object state, sound, and touch evolve together as humans pursue goals over time. Existing datasets fragment this experience across viewpoints, modali
中文介绍 提出以人为中心的环绕采集方案 ACE-Data-0,作为具身智能数据引擎,同步捕获第一人称视觉、全身运动、灵巧操作、物体状态、声音与触觉,解决现有数据集模态割裂、难以反映人类目标导向行为的问题。
👍 30
07/30 08:00
Role-playing agents (RPAs) have become one of the most important consumer applications of large language models. Users engage in multi-turn conversations with RPAs for experiences such as emotional comfort, making reliable evaluation essential for measuring capability, comparing systems, and guiding
中文介绍 提出面向角色扮演智能体(RPA)的交互式评测方法,通过人设对齐的用户模拟生成真实多轮对话,用于衡量情感陪伴等体验能力,解决借用他人历史或模板对话导致评测不可靠的问题。
👍 24
07/30 08:00
Existing video captioning models generate natural descriptions of video content but cannot explicitly ground local visual elements to multiple reference images. We introduce multi-reference image-grounded video captioning, a new task requiring factual video descriptions with phrase-level reference g
中文介绍 提出多参考图像引导的视频描述任务(multi-reference image-grounded video captioning),要求模型在生成事实性视频描述的同时,将局部视觉元素与多张参考图像做短语级对齐,并开发 RefCaptioner 实现该能力。
👍 21
07/29 08:00
Multimodal large language models increasingly use sketches, annotations, tools, and intermediate images during reasoning, but it remains unclear whether they truly rely on these visual states. Existing benchmarks are limited both by task collections with narrow coverage or partially text-solvable sa
中文介绍 构建 See2Think 评测基准,检验多模态大模型在推理中是否真正依赖中间视觉状态(草图、标注、工具输出等),弥补现有基准覆盖窄或可被文本解算的缺陷,揭示模型实际使用视觉信息的程度。
👍 21
07/30 08:00
Vision-language models (VLMs) are increasingly used in embodied agents to interpret visual inputs, reason about spatial relationships, and make task-level decisions based on that reasoning. However, a fundamental capability mismatch remains: general VLMs can reason about the overall task but often m
中文介绍 提出 SpatialCLI,先让视觉语言模型(VLM)借助空间工具(如坐标、深度)进行空间推理,再逐步脱离工具,解决 VLM 在具身智能中能理解总体任务但缺乏精细空间关系推理能力的问题。
👍 21
07/29 08:00
Coding agents have made substantial progress on software engineering tasks that modify existing codebases, including bug fixing and feature implementation. However, constructing a complete program from scratch remains a major challenge: even the frontier models evaluated on ProgramBench fully resolv
中文介绍 提出 MindForge,通过无源码程序合成训练小语言模型掌握全生命周期软件工程,针对从零构建完整程序这一挑战(即使前沿模型在 ProgramBench 上也难以完全解决),提升代码智能体的编程能力。
👍 18
07/29 08:00
LLM-based agents excel at software engineering tasks where an existing codebase provides context, but constructing a program from scratch remains fundamentally harder. Recent benchmarks such as ProgramBench quantify this gap: given only natural-language documentation and an execute-only binary as a
中文介绍 提出 SpecFirst,将行为规格获取作为基于智能体从零程序合成的第一等步骤,针对仅有自然语言文档和可执行二进制的 ProgramBench 任务,先澄清行为再生成代码,提升 LLM 智能体从零构建程序的成功率。
👍 16
07/30 08:00
We present ShadowDancer, a novel approach to any-action, frame-level control of interactive video world models. The obstacle is representational: existing interfaces either encode an action loosely, leaving how it unfolds for the model to improvise, or encode it exactly through structured signals th
中文介绍 提出 ShadowDancer,从视频及对应 shadow 中学习统一动态表示,实现视频世界模型上任意动作的帧级控制,解决现有接口要么动作编码松散、要么依赖结构化信号而导致表示不统一的问题。
👍 15
07/30 08:00
On-policy self-distillation (OPSD) is a promising approach to improve reasoning language models, but it remains brittle in practice: making it work reliably often requires substantial engineering effort. We identify a structural source of this difficulty: vanilla OPSD is precisely the β=1 member of
中文介绍 提出 β-OPSD,将 on-policy self-distillation 推广为带系数 β 的统一框架,通过策略优化推导、自蒸馏训练,解决原生 OPSD(β=1)训练脆弱、工程调参困难的问题,提升推理语言模型的稳定性。