👍 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 针对长上下文Agent工作负载中KV Cache严重占用HBM与SSD的问题,提出DeepSeek-V4.1-Flash,通过更激进的KV Cache压缩降低prefill与存储瓶颈,提升长序列推理效率。
👍 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 评测MiniMax-H3这一Omni-Modal生成模型在物理世界推理上的能力,考察其在统一文本、图像、视频与音频表征框架下的多模态生成与物理常识一致性表现。
👍 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中学生回答过长(length inflation)的问题,发现基础学生与后训练教师之间终止token不一致是重要成因,并在Qwen3、Llama等模型上验证。
👍 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 提出SoL-Pi,通过递归扩展自动研究循环来提升编码Agent的token效率,使Agent在长期无人值守探索中实现递归自我改进。
👍 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 提出ScienceIDE,将全球科学代码库转化为Agent可学习的执行环境,解决领域约定隐性、正确性标准特殊等「科学转换难题」,提升Agent对科研代码的掌握能力。
👍 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 实证研究编码Agent中harness设计的组件级影响,将harness拆解为独立部件进行对比,揭示各组件对长程软件工程表现的贡献,弥补以往整体评估的不足。
👍 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 提出JEPA-Anything,一种领域无关的世界建模框架,使单一学习原则能够支撑跨不同系统的预测建模,提升智能体对未来状态与环境后果的预判能力。
👍 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 提出基于经验的语言模型置信度估计方法,从推理与Agent场景学习历史经验来校准输出正确概率,辅助部署决策、升级与重试判断。
👍 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 提出ProgramDistill基准,从可运行Web应用逆向生成带可验证参考的SWE任务,评估编码Agent在未完成任务中推断并复现行为的能力。
👍 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 提出面向LLM助手的可验证社交推理评测,要求助手从用户主观叙述中学习社交情境,并对他人意图、情绪等社交属性进行有据可查的推理。
👍 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 提出Self-Evolving Search Index,让检索索引的关键字表示随查询与Agent任务需求动态演进,从而提升LLM Agent在复杂信息检索任务中的检索质量。
👍 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 提出Agora,以Git作为共享记忆供多个AutoResearch Agent协作,避免各Agent从零开始重复搜索,实现集体自动研究的累积式知识共享与协同改进。
👍 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 提出RiskChainBench,评测模型对社交平台伪装消息(表情、谐音、拆字等)的还原能力,以及基于证据的网页调查能力,覆盖色情、诈骗、赌博等滥用链路。
👍 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 提出RetireOPD,一种用于Agentic RL的自淘汰on-policy蒸馏方法,通过淘汰低价值样本提供稠密token级监督,帮助无技能学生内化具有特权的自教师任务技能。
👍 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 提出无需训练的GUI Agent技能进化方法,通过Reflect-Revise-Reuse流程让Agent在动态界面对弹窗、延迟加载等扰动进行自适应技能更新与复用。
👍 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 提出WeVisDoc端到端文档解析方法,从覆盖面扩展转向能力构建,提升模型在多样布局与复杂采集条件下的结构化文档解析鲁棒性。
👍 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 提出EvolveTrade,通过经验驱动的策略精炼使LLM交易Agent自演进,动态调整证据收集、工具调用与信号验证方式,突破静态手写工具策略的局限。
👍 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 提出VA-Bench,通过视觉演示、主动感知与度量控制评测具身空间智能,要求模型在观测不完整时完成观察-推理-行动-修正的完整闭环。
👍 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 提出When2Think,为大推理模型学习难度感知的长度控制,避免简单题过度思考与难题思考不足,替代统一长度惩罚或硬性路由的效率折损方案。
👍 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 研究特权信息对on-policy自蒸馏的贡献,隔离教师看到答案或解题过程这一额外信息本身的作用,量化其相对于蒸馏机制带来的额外学习增益。