👍 95
09/17 08:00
The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost of long-context computation, prefill remains computationally expensive, and large KV caches continue to strain HBM and SSD capacity and data-transf
中文介绍 长任务智能体使输入侧负载激增,prefill 计算与 KV cache 占用成为瓶颈。DeepSeek-V4.1-Flash 针对 KV cache 压缩,在 HBM 与 SSD 容量受限下进一步提升长上下文效率,缓解显存与存储压力。
👍 93
09/16 08:00
Recent Omni-Modal Generative Models (Omni-Models) have advanced content generation toward unified modeling of text, images, video, and audio. MiniMax-H3 exemplifies this transition by combining multimodal context understanding with joint audio-visual generation in a shared latent framework. Its unif
中文介绍 评测全能多模态生成模型 MiniMax-H3 是否具备物理世界推理能力。该模型在共享隐空间内统一文本、图像、视频与音频的理解与音视频联合生成,论文据此考察其在物理常识与因果推理上的表现与局限。
👍 76
09/17 08:00
We study length inflation in on-policy distillation (OPD), where student responses can become excessively long and even exhaust the generation budget. We identify termination-token mismatch between base students and post-trained teachers as an important source of this behavior. Across Qwen3, Llama,
中文介绍 研究 on-policy distillation 中学生回复过长乃至耗尽生成预算的长度膨胀现象,指出基座学生与后训练教师的终止 token(EOS)不一致是重要成因,并在 Qwen3、Llama 等模型上验证该失配的影响。
👍 74
09/16 08:00
Scientific code repositories encode decades of human knowledge in executable models, methods, and tools. Yet fragmented toolchains, implicit domain conventions, and specialized correctness criteria make this knowledge difficult to convert into reliable learning experience-a challenge we call the sci
中文介绍 科学代码库蕴含大量可执行模型与工具,但工具链碎片化、领域约定隐式、正确性标准特殊,难以转为智能体学习经验。ScienceIDE 将其构建为可学习环境,让智能体在真实科学代码中训练可验证能力。
👍 69
09/17 08:00
As coding agents move from supervised code completion to unattended, around-the-clock exploration, their work expands from isolated predictions into long trajectories of reasoning, tool use, and feedback. Token efficiency therefore becomes important for scaling recursive self-improvement. We take an
中文介绍 面向无人值守的编码智能体,长程推理与工具调用使 token 效率成为递归自我改进的瓶颈。SoL-Pi 通过递归扩展 auto-research 循环来优化 agent harness,在提升探索效率的同时降低单位进展的 token 消耗。
👍 55
09/17 08:00
Coding harnesses shape how autonomous coding agents translate model capabilities into long-horizon software-engineering performance, yet existing work typically evaluates harnesses as monolithic systems, leaving the effectiveness of individual components unclear. To enable component-level comparison
中文介绍 编码 harness 决定自主智能体长程软件工程表现,但以往多把 harness 当整体评估,各组件作用不明。该实证研究拆解 harness 各组件,开展组件级对比,厘清哪些设计真正影响编码智能体性能。
👍 54
09/15 08:00
Reliable confidence estimation is increasingly central to the trustworthy deployment of language models: a calibrated estimate of the probability that an output is correct decides what to ship, what to escalate, and what to retry. Existing confidence estimators, however, share one design premise: th
中文介绍 可靠置信度估计对语言模型部署至关重要,决定输出交付、升级或重试。现有估计器共享同一设计前提,本文提出从推理到智能体的经验式置信度估计,利用模型自身的经验信号获得更校准的正确性概率。
👍 48
09/16 08:00
Coding agents are typically evaluated with desired behavior specified through issues or instructions. In practical web development, however, agents may need to infer behavior from working software and implement it in an incomplete application. We introduce ProgramDistill, a benchmark evaluating codi
中文介绍 现有编码智能体多依赖 issue 或指令指定目标行为,而真实 Web 开发需从可用软件反推行为并在不完整应用中实现。ProgramDistill 构建从交互式 Web 应用到可验证参考引导 SWE 任务的基准,考察该能力。
👍 43
09/16 08:00
Autonomous research loops such as AutoResearch show that one coding agent can improve a training setup unattended. Run several of them and each session starts from scratch, so more agents tend to mean more duplicated search rather than more discovery. Agora is a shared memory for such agents: resear
中文介绍 多个 AutoResearch 智能体各自从零开始,导致重复搜索而非更多发现。Agora 以 Git 作为共享记忆,让研究智能体复用彼此的代码、实验与结论,从而在集体自动研究中减少冗余、提升发现效率。
👍 40
09/17 08:00
World modeling enables intelligence to anticipate consequences, guide interventions, and learn from interaction. Yet predictive models remain domain-specific: can a common learning principle support world modeling across radically different systems? We introduce JEPA-Anything, a domain-agnostic fram
中文介绍 世界模型可预测后果并指导干预,但现有预测模型多为领域专用。JEPA-Anything 提出领域无关框架,用统一的联合嵌入预测学习原则,在差异极大的系统上训练预测模型,探索跨世界通用世界建模的可行性。
👍 33
09/15 08:00
LLM assistants are widely used for daily social advice, yet evaluating their social reasoning in such consultation settings remains challenging since (i) it requires setups where the assistant learns about social situations from subjective user narratives, and (ii) social properties, such as others'
中文介绍 LLM 助手常用于日常社交建议,但从主观用户叙述中推断社交情境、并处理他人隐私等社会属性,使评测困难。本文提出可验证的社交推理评测设置,检验助手在咨询场景下的社交推理是否可核查、可信。
👍 32
09/15 08:00
Large language model (LLM) trading agents can combine market data, news, and executable analysis, but their behavior is often controlled by static hand-written tool-use policies that are fixed before deployment. This limits their ability to adapt how they gather evidence, invoke tools, verify signal
中文介绍 LLM 交易智能体的工具使用策略多为部署前手写固定,难以自适应调整取证、调用工具与验证信号的方式。EvolveTrade 用经验驱动策略精炼,让交易智能体在运行中自我演化决策策略,提升适应性与表现。
👍 29
09/15 08:00
Platform abuse campaigns conceal redirection instructions with emojis, homophones, character decomposition, and redundant symbols, then route users through disguised links to services associated with pornography, fraud, gambling, or illicit transactions. Existing benchmarks evaluate obfuscated text
中文介绍 平台滥用活动用 emoji、谐音、拆字与冗余符号隐藏跳转指令,经伪装链接导向色情、诈骗、赌博等服务。RiskChainBench 面向混淆平台消息还原与基于证据的 Web 调查,提供可评测该链路能力的基准。
👍 26
09/17 08:00
Information retrieval is increasingly important as LLM agents tackle complex tasks involving diverse information needs. Because retrieval relies on an index that represents each document through index keys, retrieval quality depends heavily on how effectively these keys expose the knowledge containe
中文介绍 检索质量高度依赖索引键能否有效暴露文档知识,而 LLM 智能体的信息需求日趋多样。本文提出自演化检索索引,让索引键随任务与语料动态优化,从而在复杂检索场景中提升召回与效果。
👍 26
09/17 08:00
Multi-turn agents trained with reinforcement learning (RL) receive a single scalar reward per trajectory, which motivates self on-policy distillation (OPD) to supply dense token-level supervision from a self-teacher with privileged task skills, letting a skill-free student internalize them. This rec
中文介绍 多轮智能体 RL 每轨迹仅得单一标量奖励,self on-policy distillation 用具备特权技能的自教师提供 token 级稠密监督。RetireOPD 让这种蒸馏随学生能力提升自我退役,避免长期依赖教师,兼顾稠密监督与自主探索。
👍 26
09/14 08:00
Scientific agents contribute to hypothesis discovery by synthesizing evidence, assessing proposals, and developing new explanations. Recent systems combine scientific agents with evolutionary search through critique, comparison, and revision. However, how different forms of agent collaboration affec
中文介绍 科学智能体结合演化搜索,通过批评、比较与修订发现假说,但不同智能体协作形式的影响尚不明确。HypoEvolve 用遗传算法组织多智能体 LLM 协作,系统比较协作结构对科学假说发现质量的作用。
👍 25
09/15 08:00
GUI agents execute long-horizon tasks on dynamic graphical user interfaces, where pop-ups, delayed loads, and relocated widgets routinely invalidate plans fixed before execution. Recent agent-skill frameworks encapsulate reusable procedural knowledge to mitigate this, yet existing skill designs are
中文介绍 GUI 智能体在弹窗、延迟加载与控件移位下,执行前固定的计划极易失效。Reflect, Revise, Reuse 提出免训练的技能演化方法,让智能体反思失败、修订并复用可迁移操作知识,提升长程 GUI 任务的鲁棒性。
👍 23
09/17 08:00
Document parsing converts document images into structured content and requires reliable performance across diverse layouts and acquisition conditions. Yet training corpora are biased toward common document types and clean digital pages, while expanding coverage alone does not specify how to address
中文介绍 文档解析训练语料偏向常见类型与干净数字页,仅扩大覆盖不足以应对多样版式与采集条件。WeVisDoc 从覆盖转向能力,构建面向端到端文档解析的鲁棒方案,提升复杂版式与低质图像下的结构化效果。
👍 22
09/16 08:00
Direct preference alignment methods are widely used to align large language models (LLMs) with human preferences because of their computational and memory efficiency. However, likelihood displacement motivates alternative ways to extract information from preference pairs with small likelihood margin
中文介绍 直接偏好对齐方法因计算与内存高效而被广泛使用,但似然位移促使人们寻找从偏好对中提取信息的替代方式。本文提出零阶范式,利用零阶优化进行 LLM 偏好对齐,规避似然位移带来的问题。
👍 20
09/17 08:00
Spatial intelligence requires more than describing object locations. Under incomplete observation, models must identify and acquire missing evidence, interpret it in a common spatial frame, and act on it. We introduce VA-Bench to evaluate the complete observe-reason-act-revise loop. General-purpose
中文介绍 空间智能不止于描述物体位置,还需在不完整观测下识别并获取缺失证据、在统一空间框架中理解并行动。VA-Bench 通过视觉演示、主动感知与度量控制,评测完整的观察—推理—行动—修正闭环。