👍 90
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 研究 Omni-Modal 生成模型 MiniMax-H3 在物理世界推理上的能力。该模型在共享潜空间中联合建模文本、图像、视频与音频,并进行多模态上下文理解与音画联合生成。文章系统评测其物理规律遵循与场景一致性表现,指出当前通用生成模型在物理因果推理上仍存在不足。
👍 82
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 场景下 prefill 开销大、KV cache 占用 HBM/SSD 及带宽的问题,提出 DeepSeek-V4.1-Flash 的 KV cache 压缩方案。通过更激进的压缩与重构策略降低长上下文推理的存储与传输成本,为超长输入推理提供更高效部署基础。
👍 73
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 学习的可执行环境。针对工具链碎片化、领域约定隐式化与正确性标准专门化所导致的知识转化难题,构建包含可运行代码、验证标准与领域知识的环境,使 Agent 获得可靠的科学计算学习体验。
👍 70
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) 中学生回复过长乃至耗尽生成预算的 length inflation 问题。识别出 base student 与 post-trained teacher 之间 EOS/termination token 不一致是关键诱因,在 Qwen3、Llama 等模型族上分析并讨论缓解该长度膨胀的机制与做法。
👍 65
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,将自动化研究循环递归扩展用于构建高效的 Agent harness。面向无人值守的长轨迹代码探索,关注推理、工具调用与反馈中的 token 效率,以提升递归自改进的扩展性,使 Agent 在更长的探索轨迹中保持成本可控。
👍 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 提出从推理到 Agent 的经验式置信度估计方法。针对现有置信度估计器共享同一设计前提而难以适应复杂部署的问题,利用 Agent 积累的经验对输出正确概率进行校准,用于决策发布、升级或重试,提升模型部署的可信度。
👍 52
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 对 coding agent 的 harness 设计进行实证研究。针对现有工作常把 harness 当作整体系统评估、难以厘清各组件有效性的问题,设计可组件级对比的评测框架,拆解分析各 harness 要素如何影响长程软件工程表现,为设计选择提供依据。
👍 46
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,将交互式 Web 应用转化为可验证的、参考引导的 SWE 任务。区别于仅以 issue 或指令描述期望行为的评测方式,要求 Agent 从可运行的软件中推断行为并在不完整应用中复现,更贴近真实 Web 开发需求。
👍 41
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,将 Git 作为共享内存用于集体 AutoResearch。针对多 Agent 并行运行导致重复搜索而非更多发现、各会话从零开始的问题,Agora 让 Agent 在共享 Git 上下文中协作、复用探索成果,提升自主研究循环的整体效率。
👍 36
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
👍 31
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,用经验驱动策略精细化让 LLM 交易 Agent 自我演化。针对静态手写工具调用策略在部署前固定、难以适应证据收集、工具调用与信号验证方式的问题,依据交易经验动态调整策略,提升 Agent 适应能力。
👍 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,评测混淆平台消息还原与基于证据的 Web 调查能力。针对滥用活动用 emoji、同音字、字符拆分、冗余符号隐藏重定向并诱导用户进入色情、诈骗、赌博或非法交易服务的问题,评测从还原文本到追踪关联证据的能力。
👍 27
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方法。针对日常社交建议场景中社交推理难以评估的问题,设置 Assistant 从主观用户叙述中学习社交情境、并验证关于他人动机与意图等主观属性的推理过程,提升社交建议的可验证性。
👍 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 强化学习的自淘汰 on-policy distillation。针对多轮 Agent 用 RL 训练时每轨迹仅有单一标量奖励的问题,借助拥有特权技能的 self-teacher 提供密集 token 级监督,并设计机制让 student 在掌握技能后主动淘汰该蒸馏信号。
👍 25
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,用遗传算法使多 Agent LLM 发现科学假设。针对现有系统将科学 Agent 与演化搜索结合但不同协作方式影响不清晰的问题,研究通过 critique、比较与修订等不同 Agent 协作形式如何影响假设发现质量。
👍 24
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 Reflect, Revise, Reuse,一种免训练的 GUI Agent 技能演化方法。针对弹窗、延迟加载、控件位移等动态界面使执行前固定计划失效的问题,让 Agent 通过反思、修订与复用演化可重用过程性知识,无需额外训练即提升长程任务适应力。
👍 22
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,从覆盖度转向能力以构建稳健的端到端文档解析。针对训练语料偏向常见文档类型与干净数字页、且单纯扩大覆盖无法说明如何针对能力加以解决的问题,从能力维度设计解析方案,提升跨版式与拍摄条件下的鲁棒性。
👍 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 提出用于 LLM 偏好对齐的零阶优化范式。针对直接偏好对齐方法虽在算力与显存上高效,却因 likelihood displacement 难以从小似然边距偏好对中提取信息的问题,提出以零阶方式替代似然梯度以提取偏好信号进行对齐。
👍 19
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,面向 LLM Agent 的自演化检索索引。针对复杂任务中检索质量严重依赖 index key 能否有效暴露文档所含知识的问题,使索引能够随使用自演化、动态优化 key 表示,从而提升多样化信息需求下的检索效果。
👍 19
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。针对空间智能不仅是描述物体位置、还要求模型在不完整观测下主动获取缺失证据、在统一空间框架内解释并据此行动的问题,评测完整的 observe-reason-act-revise 闭环。