👍 68
10/08 08:00
Reinforcement learning (RL) is the central training paradigm for advancing large foundation models towards self-improvement. This report introduces the MiMo-V2.6 series, an omni-modal family that pushes the frontier of model intelligence by scaling RL compute. Prior to RL, we conduct mid-training on
中文介绍 针对全模态基础模型自我进化难题,MiMo-V2.6 系列通过扩展强化学习(RL)计算量推动模型智能前沿。模型在 RL 之前进行特定 mid-training,系统化探索扩展 RL 算力对提升全模态推理与自我改进能力的有效范式。
👍 51
10/08 08:00
Egocentric world models predict first-person observations conditioned on an agent's actions, but most focus on a single agent. Real embodied settings often involve multiple agents that act and interact within a shared environment. Existing multi-agent world models rely on coarse actions like locomot
中文介绍 针对现有第一人称世界模型局限于单智能体或仅支持粗粒度动作的问题,提出支持细粒度具身交互的多智能体第一人称世界模型。该模型能够基于智能体动作预测共享环境下的第一人称视角观测,建模多智能体间的复杂交互动态。
👍 41
10/08 08:00
This is a book about large language models. As indicated by the title, it primarily focuses on foundational concepts rather than comprehensive coverage of all cutting-edge technologies. The book is structured into six main chapters, each exploring a key area: pre-training, generative models, prompti
中文介绍 聚焦大语言模型(LLM)底层核心机制与基础理论,全书系统划分为预训练、生成模型、Prompting 等六大核心章节,梳理技术演进逻辑,为研究者构建系统化的 LLM 基础概念体系与技术全景。
👍 37
10/06 08:00
Large language models are informing decisions with ever-higher stakes. As the consequences of their errors grow, a central question becomes harder to ignore: how much can we trust an individual answer? Yet recognizing when to defer remains difficult because language models can present incorrect conc
中文介绍 针对高风险决策中大语言模型(LLM)难以判别何时应暂停输出的问题,提出 U-Space 分析框架。该研究深入解构 LLM 中不确定性产生的时机与根因,帮助量化单次回答的置信度,提升模型输出的可靠性与可解释性。
👍 34
10/08 08:00
Large language model (LLM) coding agents have advanced test generation across diverse programming tasks. However, the common practice of evaluating tests against a single reference solution overlooks alternative valid implementations and can overstate test quality. We introduce TestPrism, comprising
中文介绍 针对现有代码智能体测试评估仅依赖单一参考解导致高估测试质量的缺陷,提出 TestPrism 评估框架。该方法跳出单一基准限制,综合考量多种有效实现方式,更精准全面地评估 LLM 智能体生成的代码测试用例质量。
👍 32
10/08 08:00
Learning to act in unfamiliar environments requires agents to infer how the world works and revise that understanding as new evidence arrives. Yet limited observations can support multiple world models that explain past interactions but predict different outcomes in unseen states. We introduce Memen
中文介绍 针对未知环境中智能体基于有限观测推断世界模型的歧义性问题,提出 Memento 3 架构。通过反思性规则手册(Reflective Rulebooks)实现基于模型的递归自我改进,动态修正对环境规律的理解,增强在未见状态下的决策表现。
👍 29
10/02 08:00
Recent text-to-image generation models have achieved remarkable visual quality, but improving them through post-training remains challenging because no single reward signal captures the full range of human preference. In this work, we develop a simple and effective post-training recipe for open-doma
中文介绍 针对单一奖励信号无法涵盖人类复杂偏好的问题,提出一种组合偏好与细则(Rubric)奖励的文生图后训练方案。该方法为开域文生图模型设计了简洁高效的奖励聚合机制,显著优化生成图像的人类偏好对齐与综合视觉质量。
👍 24
10/08 08:00
Single-image, multi-image, and video deep research require different visual operations but share a workflow of visual grounding, external retrieval, and fact composition. A key challenge is to preserve the dependencies linking localized visual anchors, entity relations, source-supported facts, and a
中文介绍 针对跨单图、多图与视频的深度研究工作流,提出统一多模态智能体 OneSearch-VL。该方法打通视觉定位、外部检索与事实整合,有效保持局部视觉锚点、实体关系与支撑事实之间的依赖链条,实现高精度的多模态深度推理。
👍 18
10/08 08:00
Most robot policies keep a model in the control loop: a VLA maps observations to actions, and an Agent Harness, such as Agent-as-Policy or Harness VLA queries a VLM for decision making at run time. We propose a different view: the embodied world is an Embodied Turing Machine, whose tape is the robot
中文介绍 挑战将视觉-语言-动作(VLA)模型置于闭环运行的传统范式,提出具身图灵机(Embodied Turing Machines)框架。将具身物理环境抽象为图灵机纸带,利用有状态代码实现机器人策略的递归自我改进与运行时精准决策。
👍 17
09/30 08:00
Long-context LLM agents accumulate interaction histories that strain KV-cache memory and attention computation. Although sparse attention reduces these costs, heterogeneous cache representations and workflows hinder integration with existing inference engines, while prior sparse-serving abstractions
中文介绍 针对长上下文 LLM 智能体交互历史带来的 KV 缓存和注意力计算瓶颈,提出稀疏优先的推理引擎 SparseEngine。该引擎解决异构缓存表示与工作流整合难题,系统化优化稀疏服务抽象,大幅降低显存占用并加速长文本推理。
👍 15
10/08 08:00
Multimodal large language models (MLLMs) are rapidly evolving toward continuous audio--visual reasoning, creating an urgent need for evaluations that expose their capability limits. Audio--visual captioning is an ideal diagnostic task, yet current benchmarks face a coupled trade-off: whole-caption s
中文介绍 针对多模态大模型在连续音视频推理上的评估局限,提出深层结构化基准 OmniCapBench。该框架聚焦细粒度音视频描述任务,打破整句评估的传统权衡限制,系统诊断模型在跨模态信息捕捉、细粒度时空感知与推理能力的边界。
👍 14
10/08 08:00
Long-horizon coding agents need timely corrections, yet feedback can be ineffective or even harmful when it misjudges ongoing work or fails to address the underlying problem. Existing critics focus on evaluating trajectories and generating feedback, but rarely track what happens after feedback is de
中文介绍 针对长程代码智能体中反馈评价误判或未解决根本问题导致反向受损的缺陷,提出语言化批判框架 Opera。不仅评估执行轨迹并生成反馈,更深入追踪反馈交付后的代码演变与调整效果,实现动态闭环的精准修正指导。
👍 14
10/04 08:00
Large language models (LLMs) are increasingly used as interactive agents and simulators, yet it remains unclear whether they can recover latent sequential structure beyond surface action frequencies. This distinction is critical for behavioral simulation, where actions are often shaped by prior cont
中文介绍 探究 LLM 能否在表层动作频率之外理解潜在的时序结构。通过推断与生成的受控实验,评估 LLM 是否真正掌握依赖历史上下文的潜在转移规律,为利用 LLM 进行高保真行为模拟与交互智能体开发提供关键理论依据。
👍 12
09/28 08:00
Reinforcement learning from verifiable rewards (RLVR) frequently reuses rollouts across multiple policy updates, increasing the mismatch between the current policy and the data-generating policy. We identify a sign-dependent gradient starvation problem in clipped policy optimization: clipping suppre
中文介绍 针对可验证奖励强化学习(RLVR)中轨迹复用引发的数据分布失配,指出现有裁剪策略优化中存在的符号相关「梯度饥饿」问题。提出序列重塑策略优化算法 ReSPO,有效缓解截断导致的梯度消失,提升离线策略学习的收敛稳定性与效率。
👍 9
10/08 08:00
Long-horizon agents compact their history to continue within a finite context window, but a textual summary alone may not support every subsequent decision. We introduce REMORY, a neural memory network that supplements the summary with a bounded sequence of soft memory tokens. Given the history and
中文介绍 针对长程智能体纯文本摘要压缩历史容易遗失决策关键信息的问题,提出神经记忆网络 REMORY。该方法引入有界软记忆(soft memory)Token 序列补充文本摘要,形成残差记忆机制,有效保留细粒度历史信息并赋能后续决策。
👍 8
10/06 08:00
Recent AI systems have made rapid progress in scientific discovery when given well-defined metrics, but whether they can autonomously undertake open-ended scientific discovery remains unclear. We investigate AI's ability to tackle open-ended tasks in Station, an open-world environment in which multi
中文介绍 探究 AI 智能体在缺乏明确预设指标时进行开放式科学发现的能力。研究构建开放世界环境 Station,评估多智能体在探索未定义问题、构建假设和验证理论的自主科研能力,揭示了当前系统应对真正开放性科学探索的潜力与局限。
👍 8
10/08 08:00
Black-box optimization (BBO) arises in many scientific and engineering problems where objective evaluations are expensive and limited. Recent large language model (LLM) agents offer a new way to approach BBO by combining task semantics, computation, optimization tools, and feedback-driven decision m
中文介绍 针对高成本黑盒优化(BBO)问题,系统评测基于 LLM 智能体的新型求解范式。该基准结合任务语义理解、计算能力与外部优化工具,量化评估智能体在反馈驱动的序列决策机制下的优化性能,为智能体驱动的科学工程优化建立统一评价标准。
👍 8
10/08 08:00
When retrieved evidence contradicts an agent's prior beliefs, does it revise its answer, acknowledge uncertainty, or persist with an incorrect conclusion? Existing evaluations of agentic systems focus primarily on task success, offering limited insight into how agents handle such conflicts. We propo
中文介绍 针对知识冲突时 LLM 智能体过度自信或执迷错误答案的问题,提出认知谦逊度(Epistemic Humility)评估体系。打破仅关注任务成功率的局限,深入分析智能体在检索证据与先验信念冲突时承认不确定性及修正结论的动态行为。
👍 8
10/08 08:00
Spatial reasoning remains a persistent weakness of vision-language models (VLMs), because RGB inputs do not directly provide geometric evidence. Existing remedies either inject 3D into the model at inference, paying architecture and latency costs, or train with outcome rewards that supervise only th
中文介绍 针对 VLM 仅依靠 RGB 输入难以获取几何先验导致空间推理薄弱的问题,提出路由 3D 特权蒸馏方法。该方法避免在推理阶段引入 3D 架构与额外延迟开销,通过中间路由监督将 3D 几何特征有效蒸馏至纯视觉模型,大幅提升空间推理精度。
👍 7
09/24 08:00
Tool-calling agents have become central to enterprise AI, yet training and evaluating them at scale remains severely constrained due to business and legal restrictions on enterprise systems, data, and database schemas. Tabular data synthesis offers a natural alternative, but its effectiveness is fun
中文介绍 针对企业由于合规与隐私限制导致工具调用智能体缺乏训练评测数据的问题,提出基于仿真交互的数据合成方法。通过可扩展的智能体-系统模拟交互生成连贯的企业表格与日志数据,为企业级智能体规模化训练与评测提供高真实度的数据支撑。