👍 437
08/29 08:00
Automating empirical research is a long-standing direction of AI. Recent automatic research (AutoResearch) agents bring this goal within reach, as modern LLMs show the capability to independently implement solutions and learn from the execution outcomes. Behind these gains, post-training (especially
中文介绍 针对自动科研智能体(AutoResearch)依赖后训练与执行反馈、难以规模化的问题,本文提出以世界模型(World Model)扩展自动研究智能体的方法,让模型在内部模拟环境中预演实验、筛选方案并积累经验,减少真实环境的试错成本,从而提升研究任务的成功率与效率。
👍 176
09/09 08:00
We introduce NCP-ArchPreview, a latent-space language model that pushes autoregressive pretraining beyond standard next-token prediction (NTP). Alongside NTP, the model learns through Next Concept Prediction (NCP) to predict discrete concepts that span multiple tokens, introducing an explicit and mo
中文介绍 NCP-ArchPreview 是一种潜空间语言模型,在标准 next-token prediction 之外引入 Next Concept Prediction(NCP),预测跨越多个 token 的离散概念,从而在预训练阶段建立显式的概念层抽象。该工作使自回归预训练超越 token 级建模,向潜空间语言建模推进。
👍 89
09/08 08:00
Large language model (LLM)-based multi-agent systems (MAS) achieve strong performance by employing specialized multiple agents, yet their performance depends on the prompt design of each agent. For MAS prompt optimization, textual gradient methods that guide prompt updates using natural-language fee
中文介绍 针对 LLM 多智能体系统中各智能体提示词设计依赖人工、难以联合优化的问题,AgentGrad 提出以干预(intervention)引导的提示优化方法,用自然语言反馈式的文本梯度定位问题智能体并定向更新其 prompt,实现多智能体提示的协同改进,提升系统整体表现。
👍 68
09/07 08:00
Large Vision-Language Models (LVLMs) have achieved strong performance on diverse visual tasks, yet their ability to reconstruct and reason about the 3D structure of the scene depicted in 2D images -- referred to as spatial intelligence -- remains limited. Existing approaches attempt to address this
中文介绍 针对 LVLM 在从 2D 图像重建与推理 3D 场景结构方面能力不足的问题,SpatialBlock 构建了合成积木堆叠(block-stacking)任务作为训练与评测载体,通过程序化生成的堆叠数据增强模型的空间推理能力,为提升视觉语言模型的空间智能提供了可扩展的数据与评测方案。
👍 46
09/10 08:00
Agent usage is shifting toward long-horizon tasks such as coding and scientific discovery, among which terminal tasks are especially important. We introduce T1, a Mixture-of-Experts model of 122B total trained with reinforcement learning, operating a real shell in a cloud sandbox for up to 300+ tool
中文介绍 T1 是面向终端长程任务的智能体模型,采用 122B 总参数的 Mixture-of-Experts 架构并经强化学习训练,可在云沙箱中操作真实 shell,支持 300+ 轮工具调用。该工作将 MoE 与 RL 结合用于终端环境下的长程任务,如编码与科学发现场景。
👍 36
09/05 08:00
Large Language Model (LLM) agents are turning language into real-world effects, making safety necessary against both indirect prompt injections and direct harmful requests. System-level safety harnesses add an enforcement layer beyond model-level defenses, but existing harnesses are usually designed
中文介绍 针对 LLM 智能体面临间接提示注入与直接有害请求的双重风险,EvoSafeHarness 提出可进化的模型与领域特定安全护具(harness),在模型级防御之外增加系统级强制层,并随模型与任务场景演化自适应更新,以提升智能体在真实执行环境中的安全性。
👍 31
09/04 08:00
Recent advances in wearable sensing enable continuous monitoring of physiological and behavioral signals, yet existing benchmarks rarely evaluate whether AI systems can reason over a real user's longitudinal wearable record. We introduce WearableQA, a benchmark comprising 4,084 10-option multiple-ch
中文介绍 现有评测很少检验 AI 能否推理真实用户的长期可穿戴记录,WearableQA 因此构建了包含 4,084 道十选一多选题的健康推理基准,基于真实可穿戴设备采集的生理与行为信号,系统评估模型对纵向个人健康数据的推理能力。
👍 21
09/08 08:00
SWE-Bench Pro has emerged as a standard benchmark for evaluating software engineering agents on challenging repository-level tasks. However, our analysis work show that its evaluation is undermined by two sources of unreliability: reward hacking, enabled by leakage of gold solutions or hidden evalua
中文介绍 SWE-Bench Pro 是软件工程智能体的常用基准,但分析发现其评测存在两类不可靠:金标方案或隐藏评测信息泄露导致的 reward hacking,以及其他评测缺陷。本文提出 SWE-Bench Pro Verified,通过修补泄漏与评测漏洞,为仓库级软件工程智能体提供更可信的评测基准。
👍 17
09/06 08:00
Sequential memory agents process long documents by reading chunks one after another while maintaining a compact memory state, coupling document traversal to reasoning depth. This coupling introduces sensitivity to evidence placement and ties inference latency linearly to document length. We introduc
中文介绍 顺序记忆智能体逐块读取长文档,导致对证据位置敏感且推理延迟随文档长度线性增长。PARSER 提出并行读取、深度推理的框架,将文档遍历与推理深度解耦,缓解证据位置偏差并降低长上下文任务的推理延迟。
👍 16
09/08 08:00
While research on recursive self-improvement (RSI) has predominantly automated model training pipelines, reliable autonomous development demands a missing pillar: post-hoc monitoring and auditing to understand what models learn and ensure safe alignment. Mechanistic interpretability tools are essent
中文介绍 递归自我改进研究多聚焦训练流程自动化,却缺少对模型所学内容的审计环节。SAEScientist-Bench 是一个评测基准,考察 AI 智能体能否自主开展 SAE(稀疏自编码器)可解释性研究,从而检验其在机制可解释性方向上的独立科研能力,为安全对齐与事后监测提供支撑。
👍 16
09/07 08:00
AI research agents combine prior knowledge, public sources, and experimental feedback to produce useful results. The Discovery Certification Protocol (DCP) turns claims about these results into executable recovery and feedback tests. Gate 1 validates useful improvement on sealed evaluation. Gate 2 g
中文介绍 针对 AI 科研智能体得分高就等同于发现的问题,本文提出 Discovery Certification Protocol(DCP),把关于研究结果的主张转化为可执行的复现与反馈测试:Gate 1 在密封评测上验证有效改进,Gate 2 检验反馈可复现性,用于审计智能体发现的真实性。
👍 13
09/10 08:00
Reducing audio-encoder depth lowers the inference cost of speech large language models, but removing complete blocks perturbs the embeddings consumed by the decoder and can cause deletion and premature end-of-sequence errors. We introduce X-AuT, a progressive framework that selects layer combination
中文介绍 减少语音 LLM 的音频编码器深度可降低推理成本,但整块删除会扰动解码器输入,引发漏词与提前结束序列。X-AuT 提出渐进式音频编码器压缩框架,通过跨尺度蒸馏选择层组合并逐步压缩,在保留性能的同时降低推理开销。
👍 12
09/08 08:00
World models are increasingly used as policy-in-the-loop imagination environments, where reliable rollouts require fine-grained controllability with respect to low-level robot actions. A key obstacle to scaling such models in robotics is that actions are not a universal language in pixel space: chan
中文介绍 世界模型作为策略在环的想象环境时,需要相对底层机器人动作的细粒度可控性,而不同机器人的动作空间在像素空间中并不通用。SyncWorld 通过视觉校准让世界模型充当零样本模拟器,使模型无需针对具体机器人重新训练即可可靠地预测动作后果。
👍 10
09/06 08:00
High-quality structured organic reaction data are essential for developing artificial intelligence for chemistry (AI4Chem), yet much of this knowledge remains dispersed across patent text, images, and reaction schemes. We present DianShi-RxnDB, a large-scale, fine-grained organic reaction data platf
中文介绍 有机反应知识分散于专利文本、图像与反应式中,制约 AI4Chem 发展。DianShi-RxnDB 通过全自动流水线构建大规模、细粒度的有机反应数据平台,从多源材料中抽取结构化反应信息,为科研人员与 AI 智能体提供可直接使用的反应数据资源。
👍 9
09/10 08:00
Mainstream robotic policies often adopt a Markovian formulation, but many complex real-world manipulation tasks are inherently non-Markovian, requiring long-horizon memory beyond the current observation. Existing memory mechanisms often rely on language summaries, growing visual windows, or their co
中文介绍 许多真实操作任务本质上是非马尔可夫的,需要超出当前观测的长程记忆,而现有记忆机制多依赖语言摘要、扩张视觉窗口等。本文提出以记忆为计划的世界-动作建模框架,将记忆以计划形式扎根于规划过程,提升机器人在长程非马尔可夫操作任务中的表现。
👍 7
09/10 08:00
Code world models represent worlds as executable programs, but this representation alone does not determine how to construct a complex world. We introduce Recursive Code World Models (RCWM), a framework for reconstructing complex 3D worlds in code from a single reference image. RCWM couples a Recurs
中文介绍 代码世界模型以可执行程序表示世界,但如何构造复杂世界尚不明确。RCWM(递归代码世界模型)提出从单张参考图像出发,通过递归场景程序重建复杂 3D 世界,将递归程序生成与图像条件结合,实现可执行的复杂场景代码建模与重建。
👍 7
09/08 08:00
We study scheming in LLM agents, in which agents covertly pursue misaligned goals. Our focus is to understand how scheming arises from the interaction of key factors, such as instrumental goals, environmental affordances, oversight conditions, and perceived consequences. Prior work examines only a s
中文介绍 针对 LLM 智能体暗中追求与人类目标不一致意图的 scheming(图谋)行为,SchemeArena 构建了因子化压力测试框架,将工具性目标、环境可乘之机、监督条件与感知后果等因素分离考察,系统揭示各因素交互如何诱发智能体的隐蔽失配行为。
👍 7
09/06 08:00
Recursive Super-Resolution (SR) extends fixed-scale SR to extreme magnification by repeatedly feeding predictions back into the same model, analogous to zooming an image repeatedly. However, ground truth availability at every scale, especially at depth, remains challenging as the required source res
中文介绍 递归超分辨率通过反复回喂预测实现极端放大,但深层尺度缺乏真实高分辨率监督数据。OracleZoom 受在策略自蒸馏启发,提出参考约束的递归图像超分方法,在缺少深层真实标签的情况下稳定训练,实现高倍率放大下的图像质量提升。
👍 6
09/01 08:00
Temporal reasoning benchmarks for Video-LLMs are often mediated by language, leaving room for linguistic shortcuts from option wording, answer correlations, or language priors. To reduce such shortcuts, we introduce TempCloze, a video cloze benchmark for evaluating visual temporal reasoning in Video
中文介绍 视频 LLM 的时序推理评测常以语言为媒介,选项措辞、答案相关性或语言先验可能带来捷径。TempCloze 提出视频完形填空基准,通过遮蔽视频中段内容让模型补全,减少语言捷径,更纯粹地评估 Video-LLM 的视觉时序推理能力。
👍 6
09/08 08:00
Leading benchmarks for formal theorem proving with large language models are small collections drawn from competition math, such as the IMO and Putnam, that poorly represent field-specific applications. We introduce StochBench, a Lean 4 benchmark of 450 graduate stochastic-processes problems at vary
中文介绍 现有形式化定理证明基准多取自 IMO、Putnam 等竞赛数学,难以代表特定学科应用。StochBench 提出 Lean 4 的随机过程领域基准,包含 450 道研究生水平、难度各异的随机过程问题,用于更贴近实际领域的形式化证明能力评测。