👍 164
07/14 08:00
The capability of a modern AI agent depends not only on its foundation model but also on its harness, which constructs prompts, manages state, invokes tools, and coordinates execution. As models, APIs, environments, and requirements evolve, the harness must be continually modified. Before such a cha
中文介绍 提出 Harness Handbook,使不断演化的 AI agent 框架(harness)可读、可导航、可编辑。通过结构化表示 prompt 构建、状态管理、工具调用和协调执行,支持 models、APIs 和环境的持续修改,提升 agent 系统的可维护性。
👍 107
07/14 08:00
We introduce Boogu-Image-0.1, an open-source unified multimodal understanding and generation model family, comprising Base, Turbo, Edit, and Edit-Turbo variants. It delivers competitive performance in high-quality text-to-image generation, fast inference, instruction-based editing, and bilingual (Ch
中文介绍 发布 Boogu-Image-0.1,开源统一多模态理解与生成模型族,含 Base、Turbo、Edit 和 Edit-Turbo 变体。在高质量文生图、快速推理、指令编辑和中英双语理解/生成上表现具有竞争力。
👍 90
07/14 08:00
Coding agents must integrate external tool returns into ongoing reasoning - a capability that standard left-to-right pretraining on code exposes only in its forward direction. We observe that the action-observation-continuation loop of a coding agent is structurally isomorphic to a function call sit
中文介绍 提出 Function-Aware Fill-in-the-Middle 作为 coding agent 基础模型的中间训练方法。利用编码 agent 的 action-observation-continuation 循环与函数调用结构的同构性,预训练模型学习整合外部工具返回,提升推理中的上下文融合能力。
👍 79
07/14 08:00
Reinforcement learning with verifiable rewards without human-annotated data, often referred to as zero RL, has emerged as a powerful paradigm for eliciting chain-of-thought reasoning. However, due to computational constraints, existing studies are largely restricted to small models, leaving the trai
中文介绍 将零强化学习(zero RL)扩展到万亿参数规模,验证大模型在可验证奖励信号下的涌现推理能力。突破现有小模型限制,展示了大规模 RL 训练在 chain-of-thought 推理上的可扩展性。
👍 72
07/09 08:00
Visual generators excel at rendering, but they confidently fabricate what they do not know. User requests are unbounded, evolving, and deeply long-tailed: new characters, trending entities, post-cutoff events, and more. This world-knowledge bottleneck is structural: generators are trained on fixed c
中文介绍 针对视觉生成模型对未知实体(新角色、热点事件等)的编造问题,提出在 agentic 视觉生成中通过搜索演化知识边界,突破静态训练集的世界知识瓶颈,实现按需动态扩展生成能力。
👍 70
07/13 08:00
In this paper, we propose SpectraReward, a training-free reward function that turns pretrained MLLMs into off-the-shelf reward models for image-generation reinforcement learning. Instead of asking the MLLM to judge a generated image or answer decomposed verification questions, SpectraReward measures
中文介绍 提出 SpectraReward,一个无需训练的奖励函数,将预训练 MLLM 转化为即用型奖励模型,用于文生图强化学习。通过测量生成图像与文本在语义空间中的对齐程度实现零样本奖励建模。
👍 63
07/11 08:00
Vision language models (VLMs) have achieved strong performance on visual document understanding benchmarks such as DocVQA, ChartQA, and MMLongBench-Doc. However, real-world documents combine multiple factors such as length, layout complexity, modality, and question difficulty, which makes it difficu
中文介绍 构建 SynthDocBench,一个可控长上下文视觉文档理解基准。通过组合文档长度、布局复杂度、模态和问题难度等因素,系统评估 VLM 在真实多因素场景下的能力,弥补现有基准的盲区。
👍 44
07/15 08:00
OpenClaw has emerged as a leading agent framework for complex task automation, yet it faces insufficient cross-platform GUI interaction support and a well-built self-evolution mechanism. These flaws limit its adaptation to diverse device ecosystems and prevent performance improvements through contin
中文介绍 提出 KnowAct-GUIClaw,基于 OpenClaw 框架改进跨平台 GUI 交互和自进化机制。通过深度理解用户意图和自我进化记忆与技能,实现个性化 GUI 助手,提升复杂任务自动化的适应性和持续学习能力。
👍 30
07/07 15:04
Image guardrails are typically trained and evaluated under a fixed safety policy, implicitly treating safety as an intrinsic property of an image. Real deployments are different: the same image may be allowed in one product, restricted in another, and newly disallowed when a policy boundary changes.
中文介绍 提出 PolicyShiftGuard,用于评估和改进图像安全防护策略的自适应能力。构建基准测试衡量同一图像在不同安全策略下的容忍度变化,并设计方法使防护模型能随策略边界调整而更新。
👍 22
07/13 08:00
Current visual generation models are capable of producing high-quality content, yet they lack a coherent perception of the spatial structure. Existing generative novel view synthesis methods typically introduce explicit geometry priors, which enforce spatial consistency but inherently restrict gener
中文介绍 提出 MetaView,单张图片新视角合成方法。引入尺度感知的隐式几何先验,在保持空间一致性的同时不限制生成多样性,相比显式几何先验方法在质量与灵活性上取得更好平衡。
👍 21
07/15 08:00
World Action Models (WAMs) improve robot policy learning by jointly modeling actions and future visual observations, using future scene evolution as dense supervision for physically grounded action generation. However, a common design in existing WAMs is to explicitly generate future videos at infer
中文介绍 推出 GigaWorld-Policy-0.5,基于 AutoResearch 加速的 World Action Model(WAM)。通过隐式未来预测替代显式视频生成,在保持物理接地动作生成的同时显著提升推理速度,适用于机器人策略学习。
👍 19
07/14 01:58
Metacognition is a foundational component of intelligence critical to effective learning, problem solving, decision-making, communication, and more. In recent years, it has become increasingly recognized as a cornerstone of capable, transparent AI systems. Yet while LLMs have made significant progre
中文介绍 综述 LLM 中的元认知:为基础、进展和机遇提供系统性梳理。元认知是智能体的核心能力,对学习、问题解决和决策至关重要。论文探讨如何将元认知集成到 LLM 中以构建更透明、更强大的 AI 系统。
👍 13
07/14 08:00
Self-improving autonomous agents are moving from research prototypes to deployed systems. The primary goal is controllable evolution, or adaptation, from experience with minimal or even no human input. This survey frames modern self-improving agents as adaptive systems that convert experience into a
中文介绍 综述现代 agent 系统的自我改进能力。将自改进 agent 视为将经验转化为可控演化的自适应系统,无需或极少需要人类输入。总结当前从原型到部署的进展、核心设计范式及未来方向。
👍 13
07/13 20:56
Post-training is essential for refining the domain-specific capabilities of large language models (LLMs), yet existing reward optimization and distribution matching methods tightly couple policy exploration with distribution alignment. This coupling forces expensive exploration directly on the polic
中文介绍 提出 Proxy Exploration and Reusable Guidance,一种模块化 LLM 后训练范式。通过代理探索和复用引导信号,解耦策略探索与分布对齐,降低直接在策略模型上进行代价高昂探索的需求,提升训练效率。
👍 12
07/12 08:00
Large language model (LLM) agents are beginning to automate machine learning engineering (MLE) by coupling planning, code execution, debugging, and empirical feedback. Translating this capability to medical imaging remains difficult because each task imposes modality-specific experimentation and str
中文介绍 探索 LLM agent 自动化医疗影像机器学习工程(MLE),将规划、代码执行、调试和实证反馈耦合到自主开发流程中。针对模态特定实验和结构化报告要求,提出可审计的自动化开发范式。
👍 11
07/15 08:00
While recent advances in 3D generation have enabled impressive visual synthesis, existing methods often rely on 2D diffusion supervision without explicit mechanisms for geometric consistency, leading to spatial hallucinations such as duplicated structures and misaligned geometry. These issues become
中文介绍 提出 Hallo4D,多模态幻觉缓解方法用于一致时空生成。针对 2D 扩散监督导致的空间幻觉(如重复结构、几何错位),引入显式几何一致性机制,在 3D 生成中减少多模态不一致问题。
👍 11
07/14 08:00
Structured pruning is a hardware-friendly way to compress LLMs, but it is mostly validated on multiple-choice recognition tasks, while the same compressed checkpoints can collapse on the free-form generation that deployment actually requires. Two observations trace this gap. First, greedy pass@1 nea
中文介绍 提出 ShortOPD(短到长在线策略蒸馏),恢复结构化剪枝后的 LLM 在自由生成任务上的性能。发现贪婪 pass@1 近似不足,通过从短到长的在线策略蒸馏有效缓解剪枝导致的生成坍塌,保持压缩模型实用性。
👍 11
07/13 16:34
Exploration is essential for reliable autonomy in multi-agent systems, yet it remains unclear whether large language model (LLM) agents can explore effectively when interacting with one another. We show that modern LLM agents fail to do so, often exhibiting myopic and polarized interaction patterns
中文介绍 揭示多 agent LLM 系统在交互中缺乏探索能力。实验表明现代 LLM agent 呈现短视和极化互动模式,无法有效探索彼此策略空间,限制自主多智能体系统的可靠性和协作性能。
👍 9
07/15 08:00
As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical. However, current evaluation pipelines remain highly fragmented and tightly coupled, hindering reproducibility and causing redundant engineering. To address this, we introdu
中文介绍 提出 AgentCompass,统一评测基础设施,解决当前 LLM agent 评估流程碎片化、耦合严重的问题。提供标准化框架支持可复现、可扩展的 agent 能力评估,减少冗余工程工作。
👍 9
07/13 21:09
When should an intelligent assistant speak up without being asked? Continuous egocentric video offers rich, evolving context that enables a new form of assistance: one that is proactive rather than merely reactive. Yet existing approaches either wait passively for user queries or treat every detecte
中文介绍 提出 Vinci2,在连续第一人称视频中提供主动辅助。利用丰富的上下文信息,在无需用户明确查询时主动判断何时提供帮助,超越被动响应和基于固定检测的触发策略,实现更自然的智能助手交互。