👍 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 针对化学文献综述中信息分散、现有检索仅返回文档排序的问题,提出以声明为中心的基础设施 AskChem,将零散研究发现组织为可定位、可核验的声明,帮助科学家和 AI 代理完成证据溯源与文献综合。
👍 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基础 GUI 智能体 Qwen-UI-Agent,目标是在真实设备上稳定操作、跨平台执行工作流,结合 GUI 交互与 CLI 执行,完成长程任务;技术报告详述系统设计、训练方法与能力评测。
👍 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 针对智能体记忆仍依赖外部模块、缺乏原生记忆能力的问题,提出记忆基础模型 Metis,将记忆能力内化到基础模型之中,实现长期记忆与推理的原生整合,推动智能体记忆从外挂走向模型原生能力。
👍 149
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 提出以物理语言为核心的物理世界模型 PhiZero,用紧凑离散的世界状态转移表示替代像素空间视频预测,使世界动态显式化,从而提升物理世界建模的准确性与可控性。
👍 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 面向机器学习工程中的递归自我改进,提出 AI4AI 模型 Frontis-MA1,并开源全栈系统 OpenMLE,覆盖可验证的训练环境等关键环节,用于训练和改进 AI 构建 AI 的能力。
👍 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 针对自回归视频蒸馏中初始化与 DMD 阶段目标分布不一致的问题,提出 DistillAlign,协调 mode covering 与 mode seeking 两种模式,使各阶段对齐,提升蒸馏学生模型的视频生成质量。
👍 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 针对文生视频物理一致性不足的问题,提出 VideoCoCo,以代码作为思维链显式建模场景动态,并通过智能体双引擎系统生成符合物理规律的视频,缓解时序演化只能隐式推断的困难。
👍 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 针对解码器语言模型将长期记忆与推理耦合、难以独立扩展记忆容量的问题,将参数化长期记忆模块 Memory Decoder 扩展到更大规模,验证其作为预训练参数化长期记忆的扩展性与有效性。
👍 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 重新思考智能体式视觉推理,提出 Beacon,让多模态大模型学会判断何时需要以及如何进行智能体推理,避免低效推理范式,从而提升复杂视觉任务的成功率。
👍 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 针对现有上下文学习评测偏重文本、未覆盖多模态场景的问题,提出 CLBench-V,从 grounding 到知识获取系统评估多模态上下文学习能力,涵盖真实世界中的多模态任务上下文。
👍 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 通过受控实验系统比较词法检索、稠密检索、图索引与智能体搜索等 RAG 范式的准确率与成本扩展规律,发现随规模扩大 BM25 表现最优,为大规模检索增强生成选型提供依据。
👍 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 针对具身智能数据瓶颈,提出以人为中心的环境采集方案 ACE-Data-0,同步记录第一视角感知、全身运动、灵巧操作、物体状态、声音与触觉,为具身模型提供多模态协同演化的数据引擎。
👍 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 针对角色扮演智能体评测依赖借用历史数据、缺乏个性化交互的问题,提出人设对齐的用户模拟方法,用于多轮交互式评测,提升对情感陪伴等场景下智能体能力的评估可靠性。
👍 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新任务,要求模型在生成事实性视频描述时将局部视觉元素显式关联到多张参考图像;并构建数据集与模型 RefCaptioner,实现短语级引用定位。
👍 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 针对多模态大模型在推理中是否真正依赖草稿、标注等中间视觉状态的问题,提出 See2Think 基准与分析方法,通过更难文本求解的任务检验模型是否实质使用中间视觉信息。
👍 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 针对视觉语言模型在空间推理上的能力短板,提出 SpatialCLI,先学习使用空间工具进行推理,再逐步脱离工具,从而提升具身智能体的空间关系理解与任务决策能力。
👍 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 针对从零构建完整程序仍具挑战的问题,提出 MindForge,通过无源码程序合成训练小语言模型完成全生命周期软件工程任务,使其在没有现有代码库的情况下也能编写完整程序。
👍 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 针对智能体从零编程困难的问题,提出 SpecFirst,将行为规格 elicitation 作为一等步骤,在生成代码前先明确需求行为,以缩小仅凭文档与可执行二进制编程时的性能差距。
👍 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,通过从视频及其“影子”中学习统一动态表示,实现任意动作的帧级控制交互式视频世界模型,解决现有动作编码松散或过于结构化的问题。
👍 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 针对在线自我蒸馏训练推理语言模型不稳定的问题,将其一般化为 β-OPSD 框架,结合策略优化推导与自蒸馏训练,缓解结构脆弱性,提升推理模型优化的可靠性。