👍 56
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 介绍 MiMo-V2.6 全模态模型系列,旨在通过扩展强化学习算力推动大模型实现自我改进。模型在强化学习前结合了中期训练(mid-training),系统探索了扩展 RL 计算规模对提升跨模态智能极限与自我优化能力的效果。
👍 43
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 针对现有第一视角世界模型局限于单智能体且动作粗糙的问题,提出支持细粒度具身交互的多智能体第一视角世界模型。该模型能够在多智能体共享环境中,基于各自的高保真具身交互动作准确预测第一人称视角观测。
👍 30
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(LLM)的基础核心概念,涵盖预训练、生成模型、Prompting 提示工程等六大核心章节。内容深入剖析基础理论与通用机制,为理解和构建现代大模型提供了全面的基础理论参考。
👍 29
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 针对大模型难以判断何时该拒答或求助的信任难题,提出 U-Space 框架,系统探究大模型在决策时产生不确定性的时机与根因。该方法有助于刻画模型置信度边界,提升模型在高风险场景下的可靠性与可解释性。
👍 28
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 针对当前代码测试评估依赖单一参考实现而易高估测试质量的问题,提出 TestPrism 评估框架。该方法打破单基准局限,综合考虑多种有效实现变体,从而更全面、真实地评估 LLM 编程智能体生成测试用例的质量。
👍 24
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难以根据新证据更新世界模型的问题,提出 Memento 3 框架。该方法通过反思性规则手册(Reflective Rulebooks)实现基于模型的递归自我改进,动态修正对环境的认知以消除预测歧义。
👍 24
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 针对文生图后训练中单一奖励信号无法覆盖复杂人类偏好的问题,提出一种组合偏好奖励与细则奖励(Rubric Rewards)的后训练配方。通过多维奖励协同优化,显著提升了前沿开源文生图模型的综合生成质量与指令遵循能力。
👍 16
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 提出统一的多模态深度研究智能体 OneSearch-VL,适用于单图、多图与视频场景。该框架统一了视觉定位、外部检索与事实整合工作流,有效保持视觉锚点、实体关系与事实之间的长程依赖,提升复杂多模态推理能力。
👍 13
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 智能体中 KV 缓存与注意力计算开销过大的问题,提出稀疏优先推理引擎 SparseEngine。该系统通过定制的稀疏服务抽象与底层执行协同设计,解决了异构缓存表示难以集成的问题,显著提升长程推理效率。
👍 11
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 针对多模态大模型视听生成评测指标单一、难以反映真实能力边界的问题,构建了深度结构化评测框架 OmniCapBench。该基准聚焦细粒度视听字幕生成任务,从多层次维度全面解耦并诊断模型的视听联合推理能力。
👍 10
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 提出将具身世界视作具身图灵机(Embodied Turing Machines)的全新范式,打破传统策略中模型必须常驻控制回路的限制。通过将物理环境与状态转化为可执行代码逻辑,为机器人系统的递归自我改进提供了灵活高效的新途径。
👍 8
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)中经验复用导致策略偏移与符号依赖型梯度饥饿的问题,提出重塑序列策略优化算法 ReSPO。该方法重构了裁剪策略的梯度机制,有效缓解离线学习中的梯度不足,提升样本利用率与训练稳定性。
👍 8
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 针对 LLM 是否真正理解潜在序列结构还是仅依赖表面动作频率进行了受控实验研究。通过在推断与生成任务中的严格对比测试,揭示了当前大语言模型在行为模拟和时序因果推理上的能力边界与认知局限。
👍 7
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 针对长流程编程智能体中反馈评价容易误判且缺乏后续追踪的问题,提出言语评论家框架 Opera。该方法不仅实时提供错误修正反馈,还主动追踪反馈执行后的状态轨迹变化,大幅提升了智能体在复杂软件工程任务中的纠错成功率。
👍 4
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 探讨 AI 智能体自主进行开放式科学发现的能力。研究构建了开放世界环境 Station,评估多智能体在没有预设狭义指标的情况下探索未知、提出假设并验证科学规律的自主能力,为开放式 AI 科学研究提供了实证参考。
👍 4
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,系统评测了结合任务语义、工具调用与反馈决策的 LLM 智能体优化能力。构建了专门基准深入对比智能体方法与传统优化算法的优劣势,揭示了 LLM 在黑盒优化中的潜力与局限。
👍 4
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 针对长程智能体单纯依赖文本摘要会导致关键信息丢失的问题,提出残差记忆网络 REMORY。该方法在文本摘要基础上补充有界的软记忆标记(soft memory tokens),以紧凑的隐空间表征弥补上下文压缩损失,提升后续决策表现。
👍 3
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 针对企业级智能体因合规与权限限制缺乏真实训练数据的问题,提出基于仿真的数据合成框架。通过大规模智能体与系统的交互模拟生成连贯的企业级表格与调用数据,有效解决了企业应用场景下高质量训练与评估数据稀缺的难题。
👍 3
10/08 08:00
In this work, we show that a single Transformer block, applied recurrently, can match the accuracy of a full-depth vision encoder at comparable inference FLOPs without intermediate feature distillation. reViT restores depth-specific transformations by representing the FFN at each recurrent depth as
中文介绍 提出 reViT,通过循环应用单个 Transformer 块即可匹敌完整深度视觉编码器的精度,且无需特征蒸馏。模型将各循环深度的 FFN 建模为深度可编程专家,在保持相当计算量(FLOPs)的同时大幅减少参数量。
👍 3
10/08 08:00
We propose BrickBench, a benchmark for agentic text-conditioned LEGO-set design. Given a prompt, an agent is tasked with producing an assembly that not only satisfies semantic and design criteria, but that can also be physically built. To do so, it must select parts from a discrete library and reaso
中文介绍 提出文本驱动的乐高积木设计智能体评测基准 BrickBench。该基准要求智能体根据提示从离散组件库中选件并推理物理搭建约束,生成兼具语义保真度与物理可建造性的拼接方案,全面评估具身设计与装配推理能力。