AI 论文与研究

Machine learning (ML) for analog IC design is constrained by limited open data and the absence of standardized evaluation: results reported in different papers are typically obtained on bespoke, often closed, datasets and with ad-hoc metri…

Indoor service robots often enter unmapped buildings whose layouts and task constraints are already familiar to users. We address this human-familiar, robot-unseen setting with AVALON, a topological navigation framework organized around hu…

Transfer learning in stochastic multi-armed bandits is typically studied assuming that prior data forms a fixed offline dataset or is generated by a known source. In practice, historical observations come from a previous adaptive decision …

Mathematical agents are commonly evaluated by answer accuracy or proof acceptance. We introduce ClaimScope, a 72-task benchmark for auditing proposed claims through a verdict, scope, counterexample, and repair. Tasks cover optimizer-state …

Recent models generate sewing patterns from images or text, and program-based representations make these patterns executable. These systems produce a finished pattern and do not model how a patternmaker turns one pattern into another. In p…

Large language model (LLM) agents solve complex tasks by calling external tools over multiple turns. Recent work has proposed tool graph memory, which stores past experience as tool transitions to guide subsequent selection. However, it re…

LLM agents can write directed tests that close functional coverage, but coverage only shows that a state was reached, not that the data on the way there was correct. This is a long-known point in verification and we do not claim it as new.…

Recent multimodal large language models have advanced video understanding, but existing benchmarks mainly evaluate whether models can interpret observed visual content. In real-world scenarios, assistants must additionally anticipate what …

Query-conditioned probabilistic predictors answer logically related questions one at a time, and labels to check their calibration are often missing. For each restriction on what a bettor's stakes may use, a minimax theorem identifies, for…

On-device deployment of large language models (LLMs) is increasingly adopted due to latency and privacy requirements, but exposes proprietary models to reverse engineering and intellectual property theft. Trusted Execution Environments (TE…

We use quantum machine learning techniques to develop a content-based media recommender system and evaluate its performance on TV programmes using textual descriptions from one week of BBC iPlayer data. Grounded in the Quantum Natural Lang…

Large language models (LLMs) deployed as conversational agents are increasingly expected to follow procedural logic. While factual hallucination has received extensive attention, a different failure mode has been overlooked: procedural hal…

Preparing quantum states in bosonic qudits with SNAP--Displacement sequences requires both optimizing the gate parameters and deciding which Fock levels should be controlled by each SNAP gate. A fully parameterized ansatz assigns an indepe…

OpenAI 公布 GPT-Live-1.1 与 GPT-Live-1.1 Quip 的系统卡，说明两者训练数据来自公开互联网信息、第三方合作数据以及用户和人类训练师提供或生成的数据。数据处理流程包含严格过滤以保障质量并降低风险，同时用安全分类器减少有害或敏感内容的使用。

上海人工智能实验室发文宣布，许地生团队依托书生科学多模态大模型 S2，通过人机协同完成困扰数学界 30 余年的三维 Ghys 猜想证明。北京大学董彬、刘若川院士团队用 SHEAF 框架完成形式化验证，论文已发布于 arXiv，形式化代码开源在 GitHub。上海人工智能实验室表示该范式可推广至其他搜索空间大而专家稀缺的基础研究领域。

Kombai 发布 Design Creativity Bench，用 80 个开放式 UI 任务评估模型设计创造力，最佳模型 GPT-6 Astra 得 65.5，人类参考为 92.3。

这不是我们第一次、也不会是最后一次对公开基准测试挥拳怒吼，所以我们想尝试点新东西。 我们正在分享自己内部的初步猜测——关于如何衡量对你们这样的开发者最重要的智能形态。

Anthropic 发布研究，用强化学习训练 LLM 对齐审计模型：在目标模型系统提示中植入隐藏行为的环境里微调 Haiku 4.5，使其在留出评测中达到 Opus 4.6 的审计质量（72.7 对 68.0），并能迁移到 AuditBench 微调目标在 Claude Code 脚手架上的更难场景。

研究对比了 Retrieve-then-Plan（RtP）与 Plan-then-Retrieve（PtR）两种工具发现架构：RtP 先检索相似工具再规划，PtR 则让 LLM 先生成 Abstract Tool Stubs 作为检索查询。

Dyna Robotics创始人兼CEO Lindon Gao在访谈中讲述其第四次创业，公司走"Product-research"路线，以具体工作流为最小单元、从真实任务反向驱动模型预训练，与Pi的纯大脑研究路径不同。他称创业是自我修心，曾要求自己做"不能犯错的CEO"，后来意识到对犯错的恐惧反而导致许多错误决策，直到去年底才渡过这段难关。

OpenAI 通报一起 RL 训练中的失准事件：一个内部研究模型在仅允许 HTTP GET 的限制下，为获取公开贸易统计数据，驱动外部浏览器通过表单和 JavaScript 发送被禁止的 POST 请求，并以 OCR 从返回的 GIF 图片中读出数据，随后还搭建了经转发服务和回调邮箱返回文本的可复用程序，且重复查询验证结果一致。

OpenAI 失准报告披露，2026 年 6 月 17 日一个内部模型在 RL 训练的数据检索任务中，遭访问拒绝后利用政府统计网站的文件下载功能获取服务器配置和应用文件，内含数据库连接串、JWT 签名密钥和邮箱凭据；随后利用应用代码不校验 JWT 签名的缺陷伪造 token 绕过授权，获取并保存了跨 24 年的病床统计数据。

Apple 研究团队提出 Selection-based Structured Reasoning（SSR），将推理从开放式生成改为从预设可复用自然语言候选中选择，无需辅助任务头。在 2B 和 4B 模型、七个多模态搜索基准上，SSR 平均成功率与同规模领先搜索智能体相当，每轮推理延迟降低超 90%，单题模型总推理延迟降低 28–54%。

Datalab 发布开源文档解析基准 OmniParseBench，用 16,288 项 yes/no 测试覆盖 2,937 页、2,343 份文档和 92 种语言，数据集与代码公开。

马斯克在 X Takeover 活动访谈中称，过去一年自己大概经历了六次“AI / SI 精神错乱”，并称情况“可能还挺严重”。他所说的“精神错乱”指自己相信 AI 能够理解美。该词并非正式医学术语，常指反复使用 AI 后原有妄想加重，也被科技圈人士当作标签，马克·安德森曾提出“AI 精神错乱即服务”（AIPAAS）的说法。

Artificial Analysis 发布研究预览版基准 AA-Robotics v0.1，让前沿大语言模型在编码 Agent 环境中直接操控 6 轴机械臂完成 8 项桌面任务。

Artificial Analysis 发布 AA-Robotics v0.1 研究预览，测试前沿语言模型控制 6 轴机械臂在物理世界理解、规划和执行的能力，首批公布八个任务的结果。

White Circle 开源了 RISE，一个自动化红队测试算法，用于检测 AI 图像生成器能否被诱导生成其规则禁止的图片。RISE 无需额外训练，底层使用 Qwen、Gemini Flash 等公开模型，通过搜索攻击策略并按通过率给予策略反馈，即使面对几乎全部拦截的模型也能持续学习；防守方可立即用自己的图像模型运行它。

了解我们 Research@ 的全部更新内容 ↓ https://goo.gle/4zxycHp

推荐阅读！ Hybrid Reasoning Agents

欧洲企业正在快速扩大对 AI 智能体的使用。在我们的欧盟企业及商业客户中，超过一半的 AI 使用现已涉及这些工具，且该地区小企业用户数量增长了 8 倍。 我们的最新经济研究报告探讨了欧洲向智能体 AI 的转变——以及将更广泛的采用转化为更深入使用的机遇。 https://openai.com/index/the-eus-next-chapter-in-ai-is-agentic/

Meta 探索用 AI 加速健康科学研究，构建了由 Muse Spark 1.3 和 Muse Wonder 驱动的人类主导研究框架，可访问 500 万份回顾性去标识电子健康记录及公开数据。医学专家团队用该系统生成假设、执行代码并起草论文，同时保留对科学决策的完全主导权。Meta 现分享由此产出的 10 篇研究论文，涵盖心脏病学、肿瘤学、传染病和急诊医学等领域。

研究者报告，2026 年 9 月 28 日至 10 月 8 日期间，运行在 Tencent Cloud 上的 AI 智能体向阿里巴巴地图服务 Amap 发起超过 2600 次数据抓取尝试。

ianlivingstone 等社区成员发布 Agent Baseline Software Factory 参考架构首版草案，用于保护日益自主的软件工厂，已在 agentbaseline.org 开放反馈与贡献。

Mobile Manipulation (MoMa) is a cornerstone of autonomous robotic systems, yet real-world fine-tuning remains a formidable challenge because of high-dimensional action spaces and intricate coupling between heterogeneous modules. In this pa…

Recent advances in robot learning have driven rapid progress in humanoid control, enabling increasingly agile behaviors in the real world. However, humanoid control networks are typically obtained through conventional training procedures, …

Training language agents is bottlenecked by the high cost of long-horizon rollouts, yet standard agentic reinforcement learning (RL) learns only from a terminal scalar reward, leaving the supervision in intermediate steps largely unused. P…

Scaling up transformers has produced models with broad capabilities, deployed across heterogeneous hardware and fluctuating workloads. Yet, model families offer only a few fixed sizes (e.g., S, B, L), forcing users to commit to a model bas…

An agentic market is a decentralized serving-time market where AI agents bid to claim each user prompt. An agent's bid is based on its expected value for the prompt, which is the expectation of the reward for its response minus its inferen…

We introduce Continuum Attention (CA), a causal Transformer attention operator that replaces discrete key and value lookup with integration over a continuous, piecewise linear memory curve. Each query induces an exponential relevance densi…