👍 374
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 针对长程智能体在底层模型可解时仍因状态丢失、未复用经验、过早停止而失败的问题,StateM 采用 harness scaling 扩展智能体执行系统,而非微调模型。在 Terminal-Bench 2.1 上取得 95.3% 原始准确率,单次运行成本约 15 美元。
👍 142
08/14 08:00
Skills have emerged as a practical and effective approach for enhancing LLM agents at inference time through structured packages of knowledge. However, existing evaluations largely measure whether skills improve aggregated task success, leaving a more fundamental question underexplored: \textbf{When
中文介绍 系统研究 agent skills(结构化知识包)在推理时增强 LLM 智能体的生效条件,指出现有评测只看总体成功率,掩盖了技能何时有效、何时失效的问题。通过分解任务与技能的交互揭示失效的机制性原因,为技能的设计与使用提供指导。
👍 92
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 针对 RL 在长程智能体微调中因反向传播训练栈过重、难以处理分支交互和稀疏奖励的问题,提出 Agentic ESOpt,采用免反向传播的进化策略优化替代 RL 后端,在极低 GPU 资源下实现长程智能体的策略微调。
👍 60
08/17 08:00
Frontier open-weight models are increasingly available, but serving them still largely assumes datacenter infrastructure. We present FreeToken, an edge-native MoE serving system that treats a personal machine not as a small GPU, but as a unified, elastic inference platform. FreeToken co-designs the
中文介绍 将个人电脑视为统一弹性推理平台,提出边缘原生 MoE 服务系统 FreeToken,采用带宽自适应执行机制,协同设计调度、显存与通信,使开放权重 MoE 模型在边缘设备上高效运行,摆脱对数据中心基础设施的依赖。
👍 58
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 面向分子、蛋白质序列、程序等开放假设空间中的高代价目标优化,提出 Large Discovery Models,以经验数据为基础进行模型驱动的开放式搜索,利用生成式先验引导探索,提升科学发现的样本效率与开放性。
👍 52
08/18 08:00
Artificial superintelligence (ASI) requires AI to move beyond mastering existing knowledge toward exploring the unknown, creating new knowledge, and turning new ideas into verifiable results. However, the capabilities of today's AI systems are still largely built on learning, compressing, and applyi
中文介绍 提出 ASI-Bench,面向人工智能超级智能能力的评测基准,衡量 AI 从掌握已知知识走向探索未知、创造新知识并将想法转化为可验证结果的能力,弥补现有模型仅靠学习、压缩和应用知识进行评测的局限。
👍 44
08/18 08:00
Although Large Vision-Language Models (VLMs) have significantly advanced embodied navigation, their direct deployment remains challenging, as existing methods often force VLMs into unnatural action spaces that misalign with their 2D pre-training priors, compounded by rigid reasoning schedules and in
中文介绍 针对 VLM 直接用于具身导航时被强制映射到与 2D 预训练先验不符的动作空间、且推理调度僵硬的问题,提出 Embodied-Navigator,通过指认、思考、记忆和对齐四个环节,使 VLM 在自然动作空间中完成高效导航。
👍 42
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家族 MOSS-VL,将边看边说的实时交互作为一等能力。语言解码器仅通过门控交叉注意力访问视觉信息,从架构到训练全栈协同设计,支持流式视觉输入与语音生成同步,实现低延迟多模态交互。
👍 39
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 面向智能体学习高质量人类影片的需求,提出 AVA-Encoder,学习一种既忠实于影片内容、又可直接用于智能体推理与操作的视频表征,弥补现有视频表示在结构化程度和可操作性上的缺失,为电影级视频生成与创作智能体提供基础。
👍 26
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 针对 AutoResearch(从假设到成稿的全流程自动科研)缺少细粒度诊断的问题,在 100 个真实前沿研究任务上进行端到端评测,系统分析智能体在科研各阶段的失败模式,为自动科研系统的能力边界和改进方向提供实证依据。
👍 19
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 把有害生成本身当作分析对象,提出 HarmProfile 刻画前沿 LLM 在越狱或异常行为中产生的有害输出分布。通过大规模高质量有害样本与统计分析,揭示模型不当行为的内容结构与模式,为安全评估从攻击结果转向有害分布建模提供工具。
👍 16
08/18 08:00
Modern agents operate inside agent harnesses that manage tools, context, and control flow, making the harness a critical part of the agent system. Our original Agent Lightning introduced a disaggregated architecture that connects arbitrary agents to RL training through an LLM endpoint proxy, an appr
中文介绍 提出 Agent Lightning v1.0,在原有解耦架构上推进 harnessed agentic RL:通过 LLM endpoint proxy 将任意智能体接入 RL 训练,使工具、上下文和控制流组成的外部 harness 成为可训练系统的一部分,统一优化智能体与环境交互。
👍 15
08/17 08:00
As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) content such as violence and nudity, further exacerbated by red-teaming adversarial attacks. Existing defenses predominantly operate under white-box assumptions, r
中文介绍 针对文生图模型被红队对抗攻击生成 NSFW 内容的问题,提出 DiSCO,通过分布引导的对比提示优化进行防御,不依赖白盒假设,在保持生成质量的同时抑制暴力、色情等不安全内容的产生。
👍 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 从二值证据视角出发,在生成式精修网络中建模编辑指令与源视频的关系,提出 GRNEdit,避免重型条件分支或高成本源拼接,实现高效、统一的通用视频编辑,兼顾编辑质量与计算开销。
👍 12
08/18 08:00
Vision encoders are a critical component of vision-language models, and scaling their capacity effectively improves performance. However, dense scaling increases compute cost and inference latency. Mixture-of-Experts (MoE) architectures offer a compelling alternative, having enabled efficient scalin
中文介绍 针对视觉编码器密集缩放带来的高计算量和推理延迟,提出 MoE 视觉编码器 MoE-ViE,用混合专家架构高效扩展视觉编码能力,在图像与视频理解任务中以较低成本获得接近稠密缩放的效果。
👍 11
08/15 08:00
Memory is becoming core infrastructure for long-horizon LLM agents, yet existing evaluations offer limited guidance on which memory substrate, namely the underlying medium in which memory is represented and stored, should be used under different operating regimes. We present a controlled harness eva
中文介绍 系统评估记忆智能体中不同 memory substrate(记忆表示与存储的底层介质)在多种运行机制下的适用性。通过受控 harness 评测,比较不同记忆介质对长程 LLM 智能体任务表现的影响,为选择记忆底层提供实证指导。
👍 10
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 针对多参考图像生成现有基准按预定义任务类型组织、难以覆盖组合空间的不足,提出 TRACE-Bench,将多参考生成能力分解为可组合的原子维度进行细粒度诊断评测,以受控复杂度评估统一多模态模型的真实能力与失败模式。
👍 9
08/18 08:00
Large-scale image generation has benefited from advances in data scale, quality, rebalancing, and recaptioning, yet conventional pipelines typically optimize task-specific datasets in isolation. A central challenge is not only how to curate each task-specific corpus, but also how to organize heterog
中文介绍 提出以能力为中心的数据设计范式,将异构任务语料组织为协同演化的能力集合,而非孤立优化各任务数据集,用于通用图像生成。在规模、质量、重平衡与重标注基础上,强调跨任务数据相互促进,提升生成器综合泛化能力。
👍 8
08/18 08:00
Recent advances in Large Language Models(LLMs) and agents have substantially improved the ability of AI systems to execute complex tasks. Yet existing benchmarks largely rely on researcher-selected tasks, leaving uncertain whether such progress extends to the work that real-world users actually dema
中文介绍 针对现有基准使用研究者自选任务、难以反映真实用户需求的问题,提出 StartupBench,基于市场验证的端到端工作流评测通用智能体,检验 LLM agent 在真实业务场景中的复杂任务执行能力。
👍 7
08/18 08:00
Large language models are increasingly deployed through agent harnesses that manage tools, extensions, persistent state, permissions, and external actions. Existing safety benchmarks mainly target individual attack mechanisms or a limited subset of operational settings, making it difficult to compar
中文介绍 面向 agent harness 全生命周期提出安全评测基准 HarnessRisk,覆盖工具、扩展、持久状态、权限与外部动作等环节,系统评估各类安全风险,弥补现有基准只关注单一攻击机制或局部设置的不足,支持不同 harness 的安全比较。