👍 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 为提升自动研究(AutoResearch)智能体的可扩展性,本文提出基于世界模型(World Models)的框架。其核心是利用世界模型预测实验环境的演化,从而使智能体能在模拟环境中高效筛选、验证科研方案,减少对昂贵真实执行的依赖。该方法有望让现代LLM在独立完成实验与从结果中学习方面实现更大规模、更自动化的科研自动化。
👍 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 本文提出NCP-ArchPreview,一种潜空间语言模型,将自回归预训练从标准的Next Token Prediction(NTP)扩展到Next Concept Prediction(NCP)。模型在NTP基础上额外学习预测跨越多个token的离散概念,引入显式的多粒度概念结构。此举将表示能力从token级提升到概念级,推动语言模型迈向真正的潜空间建模。
👍 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 针对LLM多智能体系统(MAS)性能高度依赖各智能体提示词设计的问题,本文提出AgentGrad,一种干预引导的提示优化方法。该方法将文本梯度方法引入MAS场景,利用自然语言反馈指导各智能体提示词的定向更新,并通过干预实验定位瓶颈智能体,从而系统性提升多智能体协作的整体表现。
👍 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 针对大型视觉语言模型(LVLM)在2D图像上重建与推理3D场景结构的空间智能不足问题,本文提出SpatialBlock方法。其思路是构造合成的积木堆叠问题作为训练数据,让模型通过反复的空间推理任务学习3D结构理解。该方法旨在提升LVLM的空间智能能力,弥补现有方法的不足。
👍 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
👍 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智能体面临的间接提示注入与直接有害请求威胁,本文提出EvoSafeHarness,一种可进化的模型与领域特定安全外壳(harness)框架。该框架在系统层加入强制约束层,弥补纯模型级防御的不足。现有harness通常是静态设计的,而EvoSafeHarness能通过演化机制自适应特定模型与领域,提升智能体安全性。
👍 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 针对可穿戴传感器可连续监测生理与行为信号但缺乏相应评估基准的问题,本文提出WearableQA,包含4084道10选项多选题,评估AI系统对真实用户纵向可穿戴记录的推理能力。该基准聚焦健康推理场景,推动AI在可穿戴健康数据上的真实世界应用能力。
👍 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 为降低语音大语言模型(Speech LLM)的推理成本,需压缩音频编码器深度,但直接删除整层块会扰动解码器嵌入并引发删除与提前EOS错误。本文提出X-AuT,一种渐进式框架,通过跨尺度蒸馏筛选层组合,逐步压缩编码器,在减少层数的同时保持嵌入一致性,避免性能退化。
👍 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 主流机器人策略多采用马尔可夫假设,但复杂操作任务本质上是非马尔可夫的,需要超越当前观测的长程记忆。本文提出Memory as Plans框架,将记忆用于接地规划(memory-grounded planning),而非依赖语言摘要或扩展视觉窗口。该方法通过世界-动作建模将记忆显式转化为计划,提升长程操作任务的鲁棒性。
👍 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 SWE-Bench Pro已成为评估软件工程智能体的标准基准,但本文分析发现其评估受两种不可靠因素困扰:金解决方案或隐藏评估的泄漏导致reward hacking。为此,本文提出SWE-Bench Pro Verified,通过修复这些可靠性缺陷,提供更可信的软件工程智能体评测基准。
👍 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 顺序记忆型长文档智能体逐块阅读并维护紧凑记忆,使文档遍历与推理深度耦合,导致对证据位置敏感且延迟与文档长度线性相关。本文提出PARSER,实现并行阅读与深度推理解耦,提升长上下文智能体的鲁棒性与推理效率。
👍 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 本文提出MetroLLM-Bench,一个含955个用例的基准,测试语言模型作为轨道交通自助机策略层的能力。覆盖6个真实地铁系统(37至414个车站)和11类任务,包括路线规划、票价计算、故障应对、无障碍出行及对抗输入。
👍 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 递归自我改进(RSI)研究多聚焦于自动化模型训练,但可靠自主发展还缺少事后监控与审计环节。本文提出SAEScientist-Bench,评估AI智能体能否自主开展稀疏自编码器(SAE)可解释性研究,包括发现、分析SAE特征并进行审计,为可靠自主AI开发提供关键支柱。
👍 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 现有视频LLM时序推理基准多依赖语言中介,存在语言捷径风险。本文提出TempCloze,一个视频完形填空基准,通过遮蔽中间片段评估视觉时序推理,降低选项措辞、答案相关性等语言先验的干扰,更纯粹地测试模型的视觉时序理解能力。
👍 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 代码世界模型将世界表示为可执行程序,但单张参考图像如何重建复杂3D世界仍是挑战。本文提出Recursive Code World Models(RCWM),通过递归场景程序框架,结合递归生成机制,从单张参考图像逐步构建复杂3D世界的代码表示,推动代码世界模型向复杂场景扩展。
👍 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 在策略自蒸馏(OPSD)作为LLM自改进范式日益流行的背景下,近期研究发现OPSD可严重退化模型推理能力。本文提出Negative Self-Distillation,通过引导模型避免缺陷推理路径而非模仿正确路径来学习推理,从而缓解OPSD的退化问题,提升LLM推理可靠性。
👍 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 量化被广泛用于降低LLM训练与推理成本,但注意力模块在极低位宽下量化误差较大,导致性能下降。现有方法主要依赖平滑技术。本文提出HyQuant,一种混合精度量化方法,针对性地优化LLM注意力模块的量化,在极低位宽下减少性能损失,提升推理效率。
👍 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 AI研究智能体结合先验知识、公开来源与实验反馈产生有用结果,但仅凭分数无法证明真正的发现。本文提出发现认证协议(Discovery Certification Protocol, DCP),将声明转化为可执行的恢复与反馈测试:Gate 1在密封评估上验证有用改进,Gate 2验证可恢复性,为AI研究智能体的结果审计提供标准化框架。
👍 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 推理语言模型在复杂任务上取得进展,但能力高度英语中心化:无论提示语言如何,模型主要在英语下推理。本文提出以数据混合作为跨语言泛化的关键支柱,通过优化训练数据的多语言混合策略,提升模型在语言内推理(in-language reasoning)上的泛化能力,使非英语用户也能获得同等推理能力。
👍 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 神经符号系统依赖数学求解器保证推理正确性,但求解器无法判断形式化翻译是否严格保持了参考等价性。本文将该漏洞形式化为Verdict-Preserving-Unfaithfulness(VPU),并提出Generative Reward Models用于自动形式化,超越传统求解器判据,从生成式奖励角度评估形式化的忠实度。