👍 80
09/24 08:00
While Large Language Models (LLMs) rely on highly non-linear components, in this work we demonstrate that they exhibit fundamental linearity: when inputs from distinct text streams are linearly combined, the model outputs a superposition of the individual next-token distributions. We term this the S
中文介绍 研究揭示 LLM 存在基础线性特性:当两段不同文本流的输入被线性组合时,模型输出为各自下一 token 分布的叠加,作者称之为「线性叠加」。该现象说明高度非线性的 Transformer 内部仍保有对多路语义的线性可加表征,为理解与控制 LLM 的并行思维提供依据。
👍 45
09/22 08:00
Prefill and decode reward different approaches to quantization: low-precision arithmetic accelerates prompt processing, while compact weights reduce memory traffic during generation. We propose "disaggregated quantization" (DQ), which specializes computation formats, weights and storage placement to
中文介绍 提出「分离式量化」(DQ),针对 LLM 推理中 prefill 与 decode 阶段的不同瓶颈分别优化:低精度算术加速 prompt 处理,紧凑权重降低生成阶段访存。方法对计算格式、权重与存储位置做阶段专用化设计,从而提升整体推理效率。
👍 24
09/20 08:00
Humans can effortlessly localize the direction of a sound source and integrate it with visual cues for reasoning, yet this remains challenging for embodied agents. In particular, it is still unclear how to effectively evaluate and model spatial audio understanding in embodied settings. To address th
中文介绍 针对具身智能体难以像人类那样定位声源方向并与视觉线索融合推理的问题,提出 OmniEcho,用于空间音频理解。工作建立面向具身场景的评估与建模框架,使智能体能够利用空间音频信息进行感知与决策。
👍 21
09/24 08:00
Rufus-Air is an open and reproducible post-training recipe on GLM-4.5-Air-Base (106B-A12B), organized as a serial pipeline of eight stages: SFT, Reasoning RL, Coding RL, Instruction-Following RL, General Agent, Coding Agent, Search Agent, and RLHF. We document the data, reward design, infrastructure
中文介绍 Rufus-Air 是在 GLM-4.5-Air-Base(106B-A12B)上开源可复现的后训练方案,由 SFT、推理 RL、代码 RL、指令遵循 RL、通用 Agent、编码 Agent、搜索 Agent 与 RLHF 八个阶段串行组成,并公开数据、奖励设计与基础设施细节,为 LLM 后训练提供可复用配方。
👍 20
09/25 08:00
Scaling language models to long contexts is limited by the quadratic cost of self-attention. Block sparse attention offers an efficient alternative, but selecting the retained blocks remains a bottleneck. Conventional block selection requires scoring all query-block pairs and therefore remains quadr
中文介绍 针对长上下文自注意力二次复杂度与块稀疏选择需评分全部 query-block 对、仍为二次的瓶颈,提出对数线性复杂度的块稀疏注意力方法,在保留关键块的同时将选择开销降至与序列长度呈对数线性增长,提升长文本建模效率。
👍 13
09/23 08:00
Recently, Large Language Models (LLMs) have been increasingly able to solve advanced mathematical problems, including many that have been open for decades. This opens the door to expansion of mathematical knowledge at unprecedented scale. Yet, while LLMs may be able to conjecture and prove more and
中文介绍 LLM 已能解决诸多高难甚至长期悬而未决的数学问题,但如何让其提出「有趣」的猜想与课题仍不明确。论文研究用学习方式引导模型发现值得探索的数学方向,从而在自动猜想与证明之外,推动数学知识以更大规模扩展。
👍 11
09/24 08:00
Task and motion planning (TAMP) problems remain difficult even with full observability and object-centric states because discrete decisions are tightly coupled to geometric, kinematic, and dynamic constraints. Generalized TAMP addresses this difficulty by exploiting regularities across problem insta
中文介绍 任务与运动规划(TAMP)中离散决策与几何、运动学、动力学约束紧耦合,即便完全可观测也难求解。论文面向利用跨问题实例规律性的泛化 TAMP,采用编码 Agent 方法生成求解方案,缓解离散与连续约束的耦合难题。
👍 11
09/24 08:00
We introduce PUBG Ally, an embodied agent for PUBG: BATTLEGROUNDS that can reason, act autonomously, and play alongside players as a voice-enabled teammate. Building such a teammate requires combining two difficult capabilities: it must perceive and respond to a constantly changing game world under
中文介绍 提出 PUBG Ally,一个可用于《绝地求生》的具身智能体,能以语音队友身份自主推理、行动并与玩家并肩作战。系统需在动态游戏世界中持续感知并实时响应,融合具身感知与对话交互能力,实现协作式游戏陪玩。
👍 10
09/24 08:00
Detectors of alignment failures screen deployed language models and score alignment benchmarks. Most are generative judges that spend a decoding pass on every criterion, and classifiers that read token probabilities, such as Llama Guard, still score one fixed label per call. Jev, a model trained wit
中文介绍 Jev 是以强化学习训练、输出校准决策(选项、二值判断与评分)的模型,被用作零样本对齐失效检测器。相比每次判据都需解码的生成式评判器与固定标签分类器(如 Llama Guard),Jev 以更快、更低成本的方式对模型对齐失败进行筛查与基准打分。
👍 9
09/24 08:00
Deep search requires LLM agents to decompose complex queries, search for evidence, and synthesize grounded answers, yet existing ReAct-style agents suffer from two limitations: role coupling, where one policy must handle planning, evidence use, and synthesis; and context accumulation, where growing
中文介绍 针对深度搜索中 ReAct 式 Agent 的角色耦合(单一策略兼顾规划、证据使用与综合)和上下文累积问题,提出 IterSynth,通过角色解耦的迭代合成拆解查询、检索证据并生成有依据的答案,提升复杂查询分解与答案合成的可靠性。
👍 8
09/24 08:00
Jev is a fast, low-cost decision model that answers natural-language questions with choices, binary judgments, and scores. As its public ecosystem grows rapidly, it remains unclear how Jev is used across applications and how public attention relates to project distribution. To answer these questions
中文介绍 对快速低成本决策模型 Jev 的生态做数据驱动分析:Jev 可用选项、二值判断和评分回答自然语言问题。论文考察其在实际应用中的使用方式,以及公众关注度与项目分布之间的关系,揭示该模型的真实应用面貌。
👍 8
09/24 08:00
In this paper, we propose RGBD20K, a novel dataset for facilitating the development of more robust and general RGB-D semantic segmentation by encompassing abundant categories and high-quality annotations. RGBD20K possesses several attractive properties: (1) Expanded Semantic Space. In particular, it
中文介绍 提出 RGB-D 语义分割数据集 RGBD20K,旨在提升分割的鲁棒性与泛化性。该数据集具备类别丰富、标注质量高等特点,拥有扩展的语义空间,为 RGB-D 语义分割模型的训练与评测提供大规模基准。
👍 8
09/19 08:00
Modern Transformer design and compression both reduce to allocating capacity under a budget. The standard scalars for these decisions, #Params and #FLOPs, capture size and compute but not architectural structure: two architectures with identical parameter budgets but different depth-width, head, or
中文介绍 Transformer 设计与压缩本质是在预算内分配容量,但参数量与 FLOPs 只反映规模与计算、不刻画架构结构。论文提出神经谱容量指标,仅凭网络规格即可度量并指导架构设计,区分参数量相同而深度宽度、注意力头等结构不同的模型。
👍 7
09/25 08:00
Existing multi-agent benchmarks primarily test in competitive settings, short-horizon interactions under 20 steps, or simply aggregate individual performance, failing to isolate and highlight genuine collaboration capabilities of LLM-based agents. We introduce AgentWorld, a benchmark of 100 human-an
中文介绍 现有多智能体基准多测竞争场景、20 步以内短程交互或仅汇总个体表现,难以隔离真实协作能力。提出 AgentWorld,包含 100 个人工设计任务,用于评测基于 LLM 的多智能体长程协作,突出协作而非单点能力的衡量。
👍 7
09/24 08:00
Tool-calling agents produce heterogeneous outputs, interleaving structured tool invocations with user-facing natural language summaries. This output heterogeneity presents a structural failure mode in standard on-policy Reinforcement Learning (RL): algorithms like GRPO indiscriminately broadcast a h
中文介绍 工具调用 Agent 的输出混合结构化工具调用与自然语言总结,标准 on-policy RL(如 GRPO)会不加区分地广播优势,导致跨片段信用错配。提出 SLCA-GRPO,针对不同输出片段做分段信用分配,缓解该结构性失效,提升工具调用 RL 训练效果。
👍 7
08/29 08:00
Modern AI agents routinely cross trust boundaries: they ingest untrusted content, combine it with privileged instructions, persist intermediate beliefs in long-term memory, and invoke privileged tools. This creates an attack surface in which malicious payloads can enter through model inputs and caus
中文介绍 现代 AI Agent 频繁跨越信任边界:摄入不可信内容、与特权指令混合、将中间信念写入长期记忆并调用特权工具,形成可被模型输入注入攻击的面的。提出 AgentKernel,一个以信任为核心原语的 Agent 操作系统,从系统层控制此类风险。
👍 7
09/24 08:00
General-purpose vision-language models (VLMs) bring broad knowledge and spatial reasoning to robot manipulation, yet existing systems either use them indirectly, to predict constraints or write programs, or give them a view of the scene rather than a world in which to act. We present World Action Ag
中文介绍 现有系统多让 VLM 间接预测约束或写程序,或只给其场景视图而非可行动的世界。提出 World Action Agent,通过「世界动作排练」让通用 VLM 直接驱动机器人操作,使其在可交互世界中推演动作,发挥其广泛知识与空间推理能力。
👍 6
09/24 08:00
Long-horizon reasoning remains a central challenge for large language models (LLMs) under sparse-reward regimes. We argue that this brittleness arises from two biases induced by complex reasoning spaces: an exploration bias, where models are drawn toward locally plausible but structurally unstable b
中文介绍 稀疏奖励下 LLM 长程推理脆弱,源于复杂推理空间带来的探索偏差(偏向局部看似合理但结构不稳的路径)。提出 SAGE,通过拓扑引导缓解长程推理偏差,引导模型在推理空间中走向结构更稳健的路径,提升长程任务表现。
👍 5
09/23 08:00
Time series agents answer analytical questions by calling external tools, and which tools they carry is decided by people before the agent runs. However, we identify two failures in this setup. Human-Agent Tool Misalignment: a library of 21 expert-curated tools helps on some tasks and hurts on other
中文介绍 时间序列 Agent 依赖人工预先选定工具,存在人机工具错配与工具迁移失败两类问题:21 个专家精选工具在某些任务上有益、在另一些上反而有害。提出 TimeEvo,以失败为驱动让 Agent 自演化其工具集,按任务需要动态调整调用工具。
👍 5
09/23 08:00
World-action models (WAMs) transfer visual and motion priors from pretrained video generators to robot control by jointly modeling visual dynamics and actions. Existing WAMs, however, predict dense future frames during training, repeatedly modeling largely unchanged content and coupling action-condi
中文介绍 世界动作模型(WAM)从预训练视频生成器迁移视觉与运动先验以控制机器人,但现有方法训练时预测稠密未来帧,反复建模几乎不变的内容并使动作条件耦合。提出 DeltaWAM(Delta 世界动作模型),面向双臂操作,改为建模帧间变化量以减少冗余并解耦动作条件。