👍 74
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 聚焦基础大模型的自我改进,发布全模态模型家族 MiMo-V2.6。该工作通过扩展强化学习(RL)计算规模推动模型智能前沿,并在执行大规模 RL 之前引入中期训练(mid-training),显著增强模型在多模态理解与自主迭代上的能力。
👍 52
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、且多智能体交互动作过于粗粒度的问题,提出支持细粒度具身交互的多智能体第一人称世界模型。该模型能在共享环境中基于多体动作联合预测第一视角观测,刻画复杂多智能体协作与交互动态。
👍 42
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 系统梳理大语言模型的基础理论与核心概念。全书结构分为预训练、生成模型、提示工程等六大核心章节,摒弃碎片化追踪前沿技术的做法,专注提炼大模型底层原理与核心知识体系,为深入理解和研发大语言模型提供系统化的理论基石。
👍 39
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 针对大语言模型在高风险决策中难以判断单次回答可靠性的难题,提出 U-Space 框架以揭示模型何时及为何产生不确定性。该方法剖析模型置信度与错误成因,帮助系统精准识别何时该推迟决策或拒绝回答,提升模型应用的安全性与可信度。
👍 38
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 针对长跨度智能体仅依靠文本摘要压缩历史容易丢失关键决策信息的问题,提出神经记忆网络 REMORY。该网络利用有界软记忆(soft memory)Token 序列补充文本摘要,保留历史关键残差信息,在有限上下文窗口内大幅提升长程决策质量。
👍 35
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 针对智能体在未知环境中因观测有限导致世界模型存在歧义的问题,提出 Memento 3 框架。该方法基于反思规则手册(Reflective Rulebooks)实现模型的递归自我改进,动态检验并修订对环境运作机制的认知,提高在未见状态下的预测与决策鲁棒性。
👍 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 针对代码大模型生成测试用例时依赖单一参考实现容易高估测试质量、忽略多种有效实现的问题,提出评估框架 TestPrism。该框架跳出单参考局限,综合考虑多种有效实现方案,提供更严谨、更具泛化性的代码测试质量评估标准。
👍 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全面捕捉人类审美与需求偏好的难题,提出结合偏好奖励与评价细则奖励(Rubric Rewards)的文生图模型后训练方案。该方法通过组合多种细粒度奖励信号,对开放域前沿文生图模型进行对齐优化,显著提升生成图像的综合质感与指令遵循度。
👍 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 针对图像与视频深度研究中视觉操作割裂的问题,提出统一多模态研究智能体 OneSearch-VL。该系统统一了视觉定位、外部检索和事实整合的工作流,有效维持视觉定位锚点、实体关系与检索事实间的依赖链路,实现了跨图文与视频的端到端深度分析能力。
👍 19
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 突破传统机器人在控制循环中频繁调用 VLA/VLM 的范式,提出具身图灵机(Embodied Turing Machines)。该方案将物理世界与机器人状态形式化为图灵机磁带与有状态代码,通过程序化状态维护实现机器人的递归自我改进与高效长期执行。
👍 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 智能体导致 KV 缓存爆炸和注意力计算受阻的痛点,构建稀疏优先的推理引擎 SparseEngine。该引擎解决了稀疏注意力机制中缓存表征异构与现有推理架构难以整合的问题,显著降低长程交互下的显存占用并提升吞吐效率。
👍 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 为突破多模态大模型连续音视频推理能力的评测局限,提出深度结构化评测框架 OmniCapBench。该基准针对音视频字幕生成任务,打破了传统整句评分的粗粒度权衡,通过细粒度语义结构分解,精准诊断模型在跨模态时序感知与细节描述上的极限。
👍 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评价反馈容易出现误判、甚至无法解决根本问题而导致反向干扰的困境,提出语言评判框架 Opera。该框架超越单纯生成轨迹反馈,主动跟踪反馈落地后的执行效果与纠错闭环,有效提升长周期编程任务的纠错成功率与稳定性。
👍 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 探究大语言模型在交互模拟中是否真正理解潜在的时序结构,而非仅仅拟合表面动作频率。通过对推理与生成行为的受控对比实验,揭示模型在结合上下文依赖进行时序决策时的能力边界,为将 LLM 用作行为仿真器提供了关键的机理洞察。
👍 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)在多轮策略更新复用采样时,截断策略优化引发的符号依赖型梯度饥饿问题,提出重塑序列策略优化算法 ReSPO。该算法重新调整序列层面的优化更新,缓解策略漂移带来的梯度抑制,提升离策略学习的训练稳定性。
👍 8
10/07 08:00
Text-to-music systems produce increasingly convincing audio, yet evaluation reveals little about whether the result matches user intent. A global text-audio relevance score can overlook the implicit intent in underspecified prompts and mask failures in specific requirements, such as instrumentation,
中文介绍 针对文本生成音乐中全局相关性评分难以反映用户隐式意图、易忽略乐器编排等具体需求的弊端,提出音乐意图细化智能体 MIRA。该方法通过交互式意图细化与约束分解,精细对齐文本提示与音乐特征,显著改善音乐生成对复杂需求的遵从度。
👍 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 智能体在脱离明确指标时能否自主进行开放式科学发现。研究构建了多智能体开放世界环境 Station,系统评估智能体在开放式假设提出、实验设计及知识演化中的表现,实证揭示了当前自主智能体在开放科学探索中的潜力与核心瓶颈。
👍 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 针对昂贵目标函数下的黑盒优化(BBO)问题,提出面向大模型智能体的系统化基准 Agentic BBO。该基准全面评估智能体融合领域语义、计算工具及反馈驱动决策的能力,深入量化分析了 LLM 智能体在复杂科学与工程黑盒寻优中的表现与策略优势。
👍 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 探讨大语言模型智能体在检索证据与先验信念冲突时的认知谦逊(Epistemic Humility)表现。针对传统评测仅侧重任务成功率的缺陷,提出专门的冲突评估框架,深入探究智能体在面对矛盾信息时是修正认知、表达不确定性还是盲目固执。
👍 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 针对纯 RGB 输入缺乏几何信息导致视觉语言模型(VLM)空间推理能力不足的问题,提出路由式 3D 特权蒸馏方法。该方法在训练时引入 3D 专家知识并路由蒸馏至模型中,无需在推理时增加 3D 模态架构与延迟开销,显著提升复杂空间推理表现。