👍 444
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(AutoResearch)智能体在长程实验中的扩展难题,提出以世界模型支撑后训练的扩展方案,使智能体能够预测执行结果并从反馈中学习,从而减少对真实环境的高成本交互,提升自主实现与迭代研究的效率。
👍 231
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 提出潜空间语言模型 NCP-ArchPreview,在标准 next-token prediction 之外引入 Next Concept Prediction(NCP),让模型预测跨多个 token 的离散概念,从而学到显式的多粒度语义结构,推动自回归预训练超越 token 级建模。
👍 92
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 面向基于 LLM 的多智能体系统(MAS)的提示词设计问题,提出 AgentGrad:以干预手段引导的提示优化方法,用自然语言反馈作为文本梯度指导各智能体的提示更新,缓解人工调参困难,提升多智能体协同性能。
👍 91
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 空间智能不足的问题,提出 SpatialBlock:基于合成积木堆叠问题构造训练与评测数据,要求模型在二维图像上重建并推理三维结构,从而增强视觉语言模型对场景空间关系的理解能力。
👍 53
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 提出 T1,一个 122B 总参数的 Mixture-of-Experts 终端智能体,通过强化学习训练,可在云端沙箱中操作真实 shell 并完成 300 步以上工具调用,面向编程与科学发现等长程任务,验证了 RL 在终端环境中的可扩展性。
👍 47
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安全威胁,提出 EvoSafeHarness,通过演化方式为不同模型和领域自动构建系统级安全防护层,在模型级防御之外增加可执行的约束层,提升智能体实际部署安全性。
👍 37
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 提出 WearableQA:基于真实用户长期可穿戴传感数据的健康推理基准,含 4,084 道十选一多选题,覆盖生理与行为信号,用于评估 AI 系统能否在个体纵向记录上进行时序推理与健康判断,填补现有基准的空白。
👍 30
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 为降低 Speech LLM 推理成本,提出 X-AuT:通过跨尺度蒸馏对音频编码器进行渐进式压缩,选择层组合而非直接删除整块,避免嵌入扰动导致的漏词与过早结束序列问题,在保持性能的同时减少编码器深度。
👍 26
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 针对复杂操作任务的非马尔可夫特性,提出以「记忆即计划」为核心的世界-动作建模框架:将长程记忆作为可规划表示而非语言摘要或增长视觉窗口,使机器人策略能够利用超出当前观测的历史信息进行长程决策。
👍 23
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 分析指出 SWE-Bench Pro 评测存在奖励作弊风险,源于 gold solution 泄露与隐藏评测信息等不可靠因素,为此提出 SWE-Bench Pro Verified,通过更严格的验证流程构建可信基准,用于公平评估软件工程智能体。
👍 20
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 针对顺序记忆智能体阅读长文档时推理深度与文档长度耦合的问题,提出 PARSER:并行读取文本块、分层深度推理,解耦文档遍历与推理过程,降低对证据位置的敏感性,并使推理延迟不再随文档长度线性增长。
👍 19
09/09 08:00
We introduce MetroLLM-Bench, a 955-case benchmark for testing language models as the policy layer of a transit kiosk. It covers six real metro systems, ranging from 37 to 414 stations, and eleven categories that include routing, fare calculation, disruptions, accessibility, and adversarial input. In
中文介绍 提出 MetroLLM-Bench:含 955 个案例的基准,用于测试语言模型作为地铁服务终端策略层的能力,覆盖六条真实地铁系统(37 至 414 站)及路径规划、票价计算、故障、无障碍与对抗输入等十一类任务,考察实际可用性。
👍 19
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 提出 SAEScientist-Bench,用于评估 AI 智能体能否自主开展 SAE 可解释性研究。该基准填补递归自我改进研究中事后监控与审计的空白,考察智能体在理解模型所学特征与安全对齐方面的自主研究能力。
👍 18
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 针对 Video-LLM 时序推理评测易受语言线索干扰的问题,提出 TempCloze 视频完形填空基准,要求模型补全被遮挡的中段视频内容,减少选项措辞与语言先验带来的捷径,更纯粹地评估视觉时序推理能力。
👍 18
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 提出递归代码世界模型 RCWM:以可执行程序表示世界,通过递归场景程序从单张参考图像重建复杂三维世界,将递归分解与代码生成结合,解决代码世界模型「如何构建复杂世界」的表示与构造问题。
👍 17
09/10 08:00
On-Policy Self-Distillation (OPSD) has emerged as a popular paradigm for large language model (LLM) self-improvement, allowing models to act as their own teachers by leveraging privileged information such as ground-truth solutions. However, recent findings indicate that OPSD can severely degrade the
中文介绍 针对 On-Policy Self-Distillation 在 LLM 自提升中可能严重损害性能的问题,提出 Negative Self-Distillation:让模型从自身错误中学习,通过避开缺陷而非模仿教师来改进推理,从而在自蒸馏范式中提升推理能力。
👍 17
08/28 08:00
Quantization has been widely adopted in LLM training and inference to reduce cost and improve efficiency. However, low-bit quantization of the attention module often introduces large errors at very low bit-widths, causing performance degradation. Existing methods mainly rely on smoothing techniques
中文介绍 针对 LLM 注意力模块在极低比特量化下误差大、性能下降的问题,提出 HyQuant 混合精度量化方法,突破传统平滑技术思路,对注意力不同部分采用差异化比特分配,在降低训练与推理成本的同时保持模型精度。
👍 17
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 科研智能体成果难以验证的问题,提出发现认证协议 DCP,将发现主张转化为可执行的复现与反馈测试:第一关在密封评测集上验证有效改进,第二关检验反馈可复现性,从而审计智能体发现的真实性。
👍 16
09/09 08:00
Reasoning language models have made substantial advances on a variety of complex tasks, yet their capabilities remain overwhelmingly English-centric: models primarily reason in English regardless of the language they are prompted in. This is inaccessible for non-English-speaking users, risks losing
中文介绍 针对推理模型以英语为中心、非英语用户难以受益的问题,研究数据混合策略对多语言推理泛化的作用,通过构建跨语言训练数据配比,使模型能够在被提示语言中直接用该语言推理,提升非英语场景下的推理表现。
👍 15
09/10 08:00
Neurosymbolic systems rely on mathematical solvers to guarantee reasoning correctness, yet solvers are fundamentally blind to whether a formal translation maintains strict reference-equivalence to a designated formalization. We formalize this vulnerability as Verdict-Preserving-Unfaithfulness (VPU):
中文介绍 指出神经符号系统中求解器无法判断形式化翻译是否保持与原题严格等价,并将该漏洞形式化为 Verdict-Preserving-Unfaithfulness(VPU),提出生成式奖励模型来评估自动形式化质量,弥补仅依赖求解器判定的不足。