👍 135
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 针对长程 Agent 带来的输入密集型负载,prefill 计算与 KV cache 占用 HBM/SSD 的问题,DeepSeek-V4.1-Flash 推进 KV cache 压缩极限,在长上下文场景下降低显存与存储压力,兼顾推理效率与模型能力。
👍 114
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 评估全模态生成模型 MiniMax-H3 对物理世界的推理能力。该模型在共享 latent 框架内统一文本、图像、视频与音频理解与联合音视频生成,论文考察其统一建模是否真正带来物理常识与因果推理,而非仅停留在内容生成层面。
👍 94
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(OPD)中学生回复过长甚至耗尽生成预算的长度膨胀问题,发现根源之一是基座学生与后训练教师的终止 token(EOS)不一致。作者在 Qwen3、Llama 等模型上分析该错配,为抑制长度膨胀提供依据。
👍 94
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、全天候探索的编码 Agent,其推理、工具调用与反馈构成长轨迹,token 效率成为递归自我改进的瓶颈。SoL-Pi 通过递归扩展自动研究循环来构建高效 Agent Harness,降低单位改进的 token 开销。
👍 79
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 科学代码库蕴含数十年可执行知识,但工具链碎片化、领域约定隐式和正确性标准特殊,难以转化为可靠学习经验。ScienceIDE 将全球科学代码库改造成 Agent 可学习环境,解决科学知识难以用于训练可靠 Agent 的问题。
👍 71
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 Agent 的 harness 决定模型能力如何转化为长程软件工程表现,但已有工作多把 harness 当整体评估,各组件作用不明。本文对 harness 设计做实证研究,实现组件级对比,厘清哪些设计真正有效。
👍 56
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 世界模型能让智能体预测后果、指导干预并从交互中学习,但现有预测模型多局限于单一领域。JEPA-Anything 提出领域无关框架,用统一学习原则跨差异极大的系统训练预测模型,验证通用世界建模的可行性。
👍 56
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 LLM 部署至关重要,决定哪些输出发布、升级或重试。本文提出「置信度来自经验」的思路,从推理到 Agent 场景利用经验进行置信度估计,突破现有估计器依赖固定设计前提的局限。
👍 50
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 实际 web 开发中,Agent 常需从可运行软件推断行为并在不完整应用中实现,而现有评测多依赖 issue 或指令描述目标行为。ProgramDistill 提出从交互式 Web 应用生成可验证、带参考的 SWE 任务的基准,评测编码 Agent 的这类能力。
👍 48
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,但其社交推理难以评估:需从主观用户叙述中理解情境,且他人隐私等社交属性不可直接观测。本文提出可验证的社交推理评测方法,面向咨询场景检验 LLM 助手的社交推理能力。
👍 45
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 LLM Agent 面对多样信息需求时检索愈发关键,而检索质量取决于索引键能否充分暴露文档知识。本文提出自我演化搜索索引,让索引随任务与语料迭代更新,从而提升检索对复杂信息需求的有效性。
👍 45
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 AutoResearch 等自主研究循环可由单个编码 Agent 无人值守改进训练配置,但多个会话各自从零开始,Agent 越多重复搜索越多。Agora 以 Git 作为共享记忆,让多个自主研究 Agent 复用研究历史,减少重复探索、增加发现。
👍 42
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 平台滥用活动常用 emoji、谐音、拆字与冗余符号隐藏跳转指令,再经伪装链接导向色情、诈骗、赌博等非法服务,而现有基准多只评测混淆文本。RiskChainBench 面向混淆平台消息还原与基于证据的 Web 调查,覆盖完整链路。
👍 39
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 Agent 的 RL 训练每条轨迹仅得单一标量奖励,self on-policy distillation(OPD)借助具特权技能的自教师提供 token 级稠密监督。但持续蒸馏会带来依赖,RetireOPD 提出自退役式 OPD,让学生内化技能后退出蒸馏。
👍 37
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 Agent 在动态界面上执行长程任务,弹窗、延迟加载与控件移位常使预先固定的计划失效。现有 Agent 技能框架封装可复用过程知识,但技能设计仍不灵活。本文提出「反思、修正、复用」的训练无关技能演化方法。
👍 35
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 文档解析需将文档图像转为结构化内容,并在多样版式与采集条件下保持可靠,但训练语料偏向常见文档类型与干净数字页,仅扩大覆盖不足以解决鲁棒性。WeVisDoc 从覆盖转向能力,提升端到端文档解析鲁棒性。
👍 34
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 Agent 可融合行情、新闻与可执行分析,但其行为常由部署前写死的工具使用策略控制,难以自适应地收集证据、调用工具与验证信号。EvolveTrade 提出经验驱动的策略精炼,使交易 Agent 能在运行中自我演化。
👍 33
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:在观测不完整时,模型需识别并获取缺失证据、在统一空间框架中解读并据此行动。VA-Bench 评测「观察—推理—行动—修正」完整闭环,通过视觉演示、主动感知与度量控制考察具身空间智能。
👍 32
09/17 08:00
Large Reasoning Models (LRMs) achieve strong performance on complex tasks but exhibit systematic inefficiency: they often overthink easy problems and underthink hard ones. Existing approaches based on uniform length penalties or rigid routing incur an efficiency tax, trading reduced computation on e
中文介绍 大型推理模型存在系统性低效:简单题过度思考、难题思考不足。现有统一长度惩罚或刚性路由方案会带来效率税。When2Think 提出难度感知的长度控制,让混合推理模型按题目难度分配思考长度,兼顾效率与性能。
👍 30
09/17 08:00
On-policy self-distillation (OPSD) lets a language model learn from a frozen copy of itself that sees an answer or a worked solution. Giving the teacher this extra information seems to offer the student more to learn, but how much does it add beyond distillation itself? To isolate that contribution,
中文介绍 On-policy self-distillation(OPSD)让模型向看到答案或解题过程的冻结自身副本学习。本文隔离出特权信息本身的贡献,考察在蒸馏之外,教师多出的答案信息究竟给学生带来多少额外收益,厘清该方法的真实增益来源。