👍 171
08/15 08:00
Long-horizon agents can fail even when their underlying models can solve the constituent steps. They may lose track of mutable state, fail to reactivate lessons from earlier executions, skip known procedures, or stop prematurely. We bet on harness scaling to improve the execution system around an ag
中文介绍 提出 StateM,通过 harness 扩展强化长程智能体执行系统,缓解状态追踪丢失、流程跳过等问题,在 Terminal-Bench 2.1 上以约 15 美元成本达到 95.3% 原始准确率,实现前沿级性能。
👍 114
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,将世界模型评测智能体化,不只输出分数,还给出解释分数合理性的推理,帮助判断物理、因果与世界状态演化是否正确,提升评测可信度。
👍 55
08/17 08:00
Reinforcement learning (RL) with group-relative advantages has become the de facto standard for post-training language model reasoners. However, when optimizing multiple reward objectives, existing methods typically scalarize the reward vector with a fixed weighted sum before group-wise standardizat
中文介绍 针对多奖励强化学习中固定加权标量化导致已掌握目标过度优化的局限,提出饱和感知优势重加权方法,依据各奖励饱和程度动态调整权重,避免浪费训练信号,提升多目标策略优化表现。
👍 52
08/16 08:00
Scientific discovery often involves optimising expensive-to-evaluate objectives over vast, structured, and open-ended hypothesis spaces, such as molecules, protein sequences, and computer programs. Generative models such as large language models (LLMs) provide expressive priors over such spaces, but
中文介绍 提出大规模发现模型,将经验数据与模型驱动的开放式搜索结合,利用生成模型对分子、蛋白质序列等结构化假设空间的先验,高效优化昂贵目标,推进科学发现。
👍 51
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 提出 VibeWorlding,构建多模态智能体从用户查询端到端搭建交互式 3D 开放世界的评估体系,分析意图理解、3D 工具使用及文本/视觉推理能力,弥补现有简单理想化查询评测的不足。
👍 39
08/15 08:00
We present MOSS-VL, an open vision-language model family that treats real-time interaction -- perceiving while it speaks -- as a first-class capability. It is co-designed across the stack: the language decoder attends to vision only through gated cross-attention, so the model can naturally see incom
中文介绍 提出开源视觉语言模型族 MOSS-VL,将实时交互(边感知边说话)作为一等能力;语言解码器仅通过门控交叉注意力获取视觉信息,支持边看边生成的交互范式。
👍 38
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,用上下文示例增强开源权重 GUI 智能体,缓解训练数据稀缺与偏差、指令歧义和执行不可靠问题,提升复杂数字任务的自动化能力。
👍 35
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 提出 ClawGym II,探索通过黑盒强化学习训练 agent harness,克服长程任务中奖励稀疏与训练扩展的挑战,为智能体工具链的端到端学习提供新路径。
👍 23
08/14 08:00
AI has long assisted scientific research, but the rapid advance of LLMs and agentic scaffolds is reshaping the landscape; a single system can now carry whole-stage research from an initial hypothesis all the way to final published paper, which is a paradigm now referred to as AutoResearch. Existing
中文介绍 对 100 项真实前沿科研任务进行端到端诊断评测,系统分析 AutoResearch 智能体从假设提出到论文发表的完整流程中的失败模式,为改进科研智能体提供依据。
👍 20
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,为智能体图像生成提供统一工作流路由,根据用户复杂意图动态调度生成流程,避免固定管线,在提升生成质量的同时降低计算开销。
👍 17
08/12 08:00
Creative agents still lack an effective way to learn from high-quality human films, limiting their ability to produce cinematic-grade videos. A key challenge is the absence of a structured video representation that is both faithful to film content and directly usable for agentic reasoning and manipu
中文介绍 提出 AVA-Encoder,面向智能体视频理解学习结构化的视频表示,既忠实于电影内容又可直接用于智能体推理与操作,弥补创意智能体缺乏高质量影像学习能力的短板。
👍 13
08/17 08:00
Instruction-based general video editing seeks to unify diverse editing operations within a single, intuitive interface. Existing approaches often rely on resource-intensive conditioning, using either heavyweight branches or costly source concatenation. Is there any efficient way to model editing int
中文介绍 提出 GRNEdit,从二元证据视角在生成精炼网络中实现高效通用视频编辑,避免重型分支或源拼接等高成本条件建模,降低资源需求的同时统一多种编辑指令。
👍 12
08/17 08:00
As large language models become increasingly widespread, third-party providers that deploy open-weight models have become an important part of the ecosystem. Auditing the quality of their inference APIs is therefore an open problem. We formalize hosted model routing as a stochastic process and propo
中文介绍 提出 Ventor-QTest,基于威胁模型对第三方托管的 LLM API 进行验证:将模型路由建模为随机过程,审计 open-weight 模型推理质量,解决开放权重模型部署生态中的审计难题。
👍 11
06/11 08:00
Frontier large language models (LLMs) safety evaluation has largely treated harmful generation as an attack outcome rather than as an object of analysis. Consequently, little is known about the harmful outputs produced during model misbehavior, partly because large-scale, high-quality collections of
中文介绍 提出 HarmProfile,系统刻画前沿 LLM 的有害输出分布,超越将有害生成仅视为攻击结果的做法,构建大规模高质量有害样本集,为安全评估与风险分析提供分析对象。
👍 11
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 在共享预算约束下的资源理性推理,校准同模型在独立任务预算上的表现,揭示模型在预算分配与价值最大化方面的不足。
👍 10
08/17 08:00
The current best bounds on the matrix multiplication exponent ω are obtained through a refinement of the laser method called combination loss analysis (Duan et al., 2022; Williams et al., 2024; Alman et al., 2025). In this note, we address the optimization problem at the core of this approach and pr
中文介绍 使用现代优化方法与 AlphaEvolve 求解组合损失分析的核心优化问题,改进矩阵乘法指数 ω 的上界,推进激光法变体的最优界分析。
👍 10
08/14 08:00
Self-evolving agents improve future behavior from interaction experience, yet existing evaluations typically optimize under fixed execution conditions and do not test recovery after those conditions change. To address this gap, we introduce PACE-Bench (Physics Adaptation via Code Evolution), a simul
中文介绍 提出 PACE-Bench,通过代码演化模拟动态环境下的物理适配,测试自进化智能体在执行条件变化后的恢复能力,弥补现有评测只在固定条件下优化的不足。
👍 9
08/18 08:00
Reinforcement Learning (RL) has been promising in single-turn LLM fine-tuning. However, long-horizon agentic reasoning introduces increasingly branching interactions and sparse rewards, exposing several limitations of RL: its heavyweight backpropagation-based training stack makes it impractical to f
中文介绍 提出 Agentic ESOpt,面向长程 LLM 智能体的轻量微调方法,规避 RL 中高开销反向传播训练,在极小 GPU 需求下处理分支交互与稀疏奖励,提升智能体策略优化效率。
👍 9
08/17 08:00
Despite recent advances in unified multimodal models for multi-reference image generation, existing benchmarks remain organized around predefined task types (e.g., "subject composition"), which are ill-suited to this combinatorial setting and lead to fragmented coverage, uncontrolled complexity, and
中文介绍 提出 TRACE-Bench,对多参考图像生成进行分解与诊断,摆脱预定义任务类型的局限,系统性控制组合复杂度,为统一多模态模型的多参考生成提供细粒度评测。
👍 9
08/12 08:00
Video understanding is a fundamental task for evaluating the capabilities of multimodal large language models (MLLMs). However, existing leading models have already achieved approximately 90% accuracy on the Video-MME leaderboard, suggesting that conventional single-turn video understanding tasks ar
中文介绍 提出 VideoGAIA,面向通用 AI 助手的智能体视频理解基准,突破传统单轮视频理解任务已饱和(如 Video-MME 约 90% 准确率)的现状,评测智能体在真实交互中的视频理解能力。