👍 381
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 StateM 通过 harness 扩展提升长程智能体执行系统,在 Terminal-Bench 2.1 上达到95.3%原始准确率,并实现约15美元的超低运行成本。该方法聚焦于解决长程任务中状态跟踪失败、程序中断等执行层问题,展示了系统级优化对智能体性能的巨大潜力。
👍 147
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 技能(Skills)被视为推理时增强 LLM 智能体的有效手段,但现有评估多关注整体成功率,忽略了其失效条件。本文深入剖析技能有效与失效的机制,为理解技能在何种场景下起作用提供更细致的视角。
👍 93
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 针对长程智能体推理中 RL 训练存在反向传播开销大、难以扩展的问题,提出 Agentic ESOpt,一种低 GPU 需求的微调方法。该方法避免重计算,适配稀疏奖励与分支交互场景,为长程智能体训练提供更高效的路径。
👍 61
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 FreeToken 是面向边缘设备的 MoE 服务系统,将个人机器视为统一弹性推理平台,而非小型 GPU。通过协同设计带宽自适应执行,FreeToken 在边缘环境下高效运行 MoE 模型,解决前沿开源模型在非数据中心场景的部署难题。
👍 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 ASI-Bench 基准旨在评估 AI 迈向超级智能的能力,超越掌握既有知识,探索未知、创造新知并将想法转化为可验证成果。该基准衡量 AI 在开放探索与知识创新方面的潜力,为人工智能超级智能研究提供评估框架。
👍 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 Embodied-Navigator 针对大型视觉语言模型在具身导航中与2D预训练先验不对齐、推理僵硬的问题,提出点选、思考、记忆与对齐的导航框架,使 VLM 在自然动作空间中高效导航,提升导航效率与泛化能力。
👍 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 AVA-Encoder 提出面向智能体的视频表示学习方法,从高质量人类电影中学习结构化表征,兼顾内容真实性与智能体推理可操作性。该表示利于智能体生成电影级视频,弥补创意智能体缺乏有效学习视频表示的不足。
👍 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 HarmProfile 将前沿 LLM 的有害生成视为分析对象而非攻击结果,通过构建大规模高质量有害输出集合,刻画模型有害输出的分布特征,深化对模型不安全行为的理解,为安全评估提供新视角。
👍 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 Agent Lightning v1.0 提出受约束的智能体 RL 框架,通过分离式架构将任意智能体经 LLM 端点代理连接至 RL 训练,强化 harness 在智能体系统中的核心作用,实现工具、上下文与控制流的高效集成训练。
👍 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 DiSCO 通过分布引导的对比提示优化,防御文本到图像生成中的不安全内容(如暴力、裸露)及红队对抗攻击。该方法无需白盒假设,以分布引导方式优化提示,提升生成安全性的同时保持图像质量。
👍 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 GRNEdit 从生成精炼网络的新视角,提出二值证据高效视频编辑方法,避免资源密集的条件分支或源拼接。通过建模编辑意图,实现多样编辑操作统一于单一界面,显著降低计算成本。
👍 12
08/18 08:00
Reinforcement learning for coding agents increasingly relies on long-running agent harnesses to manage tool integration, repository contexts, and execution feedback. However, the native execution environments of these harnesses are inherently misaligned with policy-gradient training: environmental c
中文介绍 LEGO-RL 面向编程智能体的强化学习,解决训练环境与策略梯度不匹配问题。通过将 harness 原生环境与 RL 对齐,实现工具集成与执行反馈的高效利用,提升编程智能体在真实环境中的学习效果。
👍 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 MoE-ViE 采用混合专家架构扩展视觉编码器,在不显著增加计算成本的前提下提升图像与视频理解能力。通过稀疏激活机制,在保持高效推理的同时增强视觉表征容量,为视觉语言模型提供高效扩展方案。
👍 12
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 本文对记忆智能体中的不同记忆底物进行受控 harness 评估,探究不同运行机制下记忆表示与存储介质的适用性。通过系统分析,为长程 LLM 智能体选择合适记忆系统提供实证指导。
👍 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 面向通用图像生成,提出能力中心的数据设计方法,从语料库到共同演进能力。该方法不孤立优化任务特定数据,而是组织异质语料以协同提升多种生成能力,为大规模图像生成的数据设计提供新范式。
👍 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 StartupBench 基准评估通用智能体在市场验证的端到端工作流中的表现,超越研究者自选任务,聚焦真实用户需求。通过真实场景评估,揭示 LLM 智能体在实际工作中的能力与局限。
👍 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 HarnessRisk 提出面向智能体 harness 安全的全生命周期基准,覆盖工具、扩展、持久状态、权限与外部动作等层面。相比聚焦单一攻击机制的基准,该基准提供更全面的安全评估框架,便于比较不同配置下的安全性。
👍 7
08/18 08:00
Compute-optimal scaling laws guide the training of frontier language models yet remain largely unexplored for visual generation. We present a systematic scaling law study for text-to-image diffusion models using Abra, a controlled family of flow-matching transformers trained across three orders of m
中文介绍 Abra 对文本到图像扩散模型进行系统性缩放定律研究,基于流匹配 Transformer 家族,跨越三个数量级训练规模。该研究为视觉生成领域的计算最优训练提供指导,弥补语言模型缩放定律在视觉生成中的空白。
👍 6
08/14 19:51
Autoformalization is commonly framed as translating natural-language mathematical statements into machine-verifiable formal languages such as Lean 4. However, faithful formalization requires more than translation. Models must map mathematical concepts to the complex hierarchy of types and definition
中文介绍 MathForm 通过知识检索与验证引导的细化,提升数学自动形式化能力。不仅翻译自然语言数学语句,更映射到复杂类型与定义层次,实现机器可验证的 Lean 4 形式化,提高形式化质量与可靠性。
👍 6
08/17 08:00
Reliable uncertainty quantification (UQ) is essential for deploying large language model (LLM) agents in complex interactive environments. Existing UQ methods largely rely on local signals, such as token probabilities, predictive entropy, or per-step confidence, and therefore overlook the long-range
中文介绍 针对 LLM 智能体在交互环境中的可靠性,提出从序列到结构的关联不确定性传播方法。现有 UQ 方法依赖局部信号,忽略长程依赖,该方法通过传播不确定性至结构层面,提升复杂任务下的不确定性量化准确性。