👍 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 针对长上下文 Agent 工作负载中 KV Cache 占用大量 HBM/SSD 与数据传输开销的问题,提出 DeepSeek-V4.1-Flash 以更强压缩减少 KV Cache 规模。重点改善长输入场景下 prefill 阶段的计算与显存瓶颈,实现推理部署上的容量与吞吐扩展。
👍 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 评估 Omni-Modal 生成模型 MiniMax-H3 对物理世界的推理能力。MiniMax-H3 在同一潜在框架内联合建模文本、图像、视频、音频并支持视听生成,论文通过系统评测考察其多模态理解与物理世界认知是否达到可靠水平。
👍 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 中出现的 student 回复过长甚至耗尽生成预算的 length inflation 现象。论文指出该问题的重要来源是 base student 与 post-trained teacher 的终止 token 不一致,并在 Qwen3、Llama 等模型上进行分析与验证。
👍 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 提出 ScienceIDE,将世界科学代码库转化为可供 Agent 学习的环境。针对科学仓库中存在碎片化工具链、隐式领域惯例和特殊正确性标准的问题,ScienceIDE 把可执行代码、方法与工具整理为可靠的学习体验,便于 Agent 吸收科学计算知识。
👍 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 提出 SoL-Pi,面向自主编码 Agent 的递归 auto-research loop 框架。针对长时间推理、工具调用与反馈累积导致 token 消耗激增的问题,SoL-Pi 通过更高效的 agent harness 设计提升递归自我改进中的 token 利用效率。
👍 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
👍 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 提出 Experiential Confidence Estimation,将经验信息用于语言模型输出可靠性的置信度估计。论文认为现有置信度方法依赖静态设计前提,难以判断输出是否应放行、升级或重试,并给出从 reasoning 到 Agents 的更可信赖估计方案。
👍 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 提出 ProgramDistill,一个用于评估 coding agent 的基准。与传统基于 issue 或指令描述目标行为不同,ProgramDistill 要求 agent 从已运行的 web app 中推断行为,并在不完整应用中实现,形成可验证的参考引导式 SWE 任务。
👍 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 提出 Agora,为 AutoResearch 等自主研究循环提供基于 Git 的共享记忆。多个 coding agent 并行运行时,以往每个会话都从零开始,导致重复搜索而非共同发现;Agora 让不同会话共享研究成果,减少冗余并促进集体自动研究。
👍 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 提出 JEPA-Anything,一种跨不同世界学习的 domain-agnostic 预测建模框架。针对现有 world model 多局限于单一领域的问题,JEPA-Anything 用统一学习原则支持不同系统下的预测建模,使模型能够理解交互后果并指导干预。
👍 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 研究 LLM 助手在日常生活社交咨询中的可验证社会推理。论文指出该场景评测困难,因为助手需从主观用户叙述中学习情境,并处理他人意图等社会属性,随后提出针对该设置的可验证社会推理评价方法。
👍 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 提出 EvolveTrade,面向 LLM 交易 Agent 的经验驱动策略自进化。针对静态手写工具使用策略难以随市场数据、新闻与可执行分析动态调整的问题,EvolveTrade 让 Agent 从经验中不断细化证据收集、工具调用和信号验证策略。
👍 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 提出 RiskChainBench,用于平台滥用消息恢复与证据支持的网络调查评测。现有基准只覆盖伪装文本本身,而本文纳入 emoji、谐音、拆字等混淆手段还原,并考察 Agent 沿伪装链接还原证据链、关联欺诈或非法服务的能力。
👍 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 提出 Self-Evolving Search Index,用于提升 LLM Agent 的信息检索质量。论文指出检索效果高度依赖 index keys 对文档知识的暴露程度,并设计一种可自我演化的索引机制,使索引随任务需求持续更新,适配多样化信息需求。
👍 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 提出 RetireOPD,用于 agentic reinforcement learning 的自淘汰 on-policy distillation。针对多轮 Agent RL 中轨迹只给一个标量奖励、缺乏密集监督的问题,RetireOPD 利用具备任务技能的 self-teacher 提供 token 级指导,帮助 student 内化能力。
👍 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 提出 HypoEvolve,用遗传算法增强多智能体 LLM 的科学假设发现。论文研究不同形式的 agent 协作如何影响 critique、comparison 与 revision 等进化搜索过程,并让科学智能体在证据综合、提案评估与新解释生成中持续迭代。
👍 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 提出面向 GUI agents 的 training-free skill evolution 方法。针对动态界面上弹框、延迟加载、控件迁移等导致固定计划失效的问题,该方法通过 reflect、revise、reuse 循环持续演化和复用技能,无需额外训练即可提高长程任务成功率。
👍 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 提出 WeVisDoc,面向端到端文档解析的鲁棒评测体系。论文指出训练语料偏向常见文档类型和干净数字页面,单纯扩大覆盖并不能规范能力要求;WeVisDoc 从 coverage 扩展到 capability,评估复杂布局与采集条件下的稳定解析能力。
👍 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 提出面向 LLM preference alignment 的 zeroth-order 范式。直接偏好对齐方法计算与显存效率较好,但 likelihood displacement 限制了从偏好对中提取信息,本文研究在 likelihood margin 较小时如何通过零阶方法更有效地完成对齐。
👍 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 提出 VA-Bench,用于衡量具身空间智能。任务要求模型在观测不完整时主动获取缺失证据,并在统一空间框架中解释后采取行动与修正。评测覆盖观察、推理、行动和修订的完整闭环,并包含视觉示范与度量控制。