AI 论文与研究

TS2Vec is one of the strongest self-supervised methods for time series classification, introduced in 2022. It is designed to be trained anew on every dataset: self-supervised pre-training followed by linear probing. Since then, the field h…

Computer-use agents are limited by the interfaces humans give them. An agent may type a spreadsheet cell by cell when a single line of code would do, or print all two thousand lines of a file through a shell to find one function. Both the …

Neural implicit representations provide memory-efficient signed distance field (SDF) parameterizations for large-scale LiDAR scenes, but the geometry recovered from sparse and often noisy inputs carries jitter and artifacts. Classical geom…

RTL repair benchmarks should measure whether a model can fix broken RTL given a specification. To this end, we construct and hash-bind RTLRepairDataset, 5,784 (broken, fixed) training pairs; RTLRepairBench, 459 held-out cases with 374 synt…

Neuro-spectral architectures (NeuSA) are a class of physics-informed neural networks (PINNs) that have proven effective for solving time-dependent PDEs in the frequency domain. However, training and inference often rely on costly numerical…

Camera-trap imagery presents substantial variation in animal appearance, illumination, background clutter, and visual degradation. Masked autoencoders (MAEs) offer a label-free framework for modeling such data through reconstruction of ran…

We prove that every $\operatorname{ReLU}$ network can be exactly simulated by a $\operatorname{ReLU}$ network with two hidden layers. We present two proofs: one that is based on fillings in homological algebra and a direct combinatorial pr…

OpenAI 在 g-mean² 之外新增两个互补指标：pair-matched 指标只在多个模型共同支持的实例上比较可监控性，post-control 指标则模拟监控器拒绝输出后的部署风险。原文指出，g-mean² 分数可能受各模型有效任务子集不同的影响，且不反映监控器在部署中作为控制手段的实际效果。

科学博客新文章：设计针对蝎子神经毒素的蛋白结合体。 蝎子每年蜇伤约 120 万人，抗毒血清往往价格高昂或供应短缺。Claude 在寻找新疗法方面迈出了早期一步。 https://www.anthropic.com/research/claude-scorpion-toxin-binder-design

同一实验室的模型往往具有相似的人格画像，不过有些实验室存在明显不同的子群。 按30项人格特质对模型进行聚类，可得到三个自然形成的家族：Anthropic 模型与 OpenAI 的 GPT-6 模型归为一类，Google 模型与 Meta 的 Muse 模型归为一类，较早期的 OpenAI 模型则与 Llama 4 Maverick 同属一类。下文我们将逐一拆解各实验室内部的子群。

Ai2 发布 FairStress 基准，用 300 个场景扩展成超过 1300 万测试用例，测量大语言模型在高风险决策中面对对抗前缀时的公平性。评测发现模型在身份信息显式时更易放弃支持少数群体编码候选人的正确决策，平均放弃差距为 6.9（隐含身份）至 9.3 个百分点（显式标签）。基准已开放下载并集成到 Olmo-eval，测试的九种去偏方法中仅反事实链式推理在保留准确率的同时缩小差距。

微软研究院播客对话 Catalyst Lab 负责人 Chris White，回顾他从哈佛博士后借调 DARPA、在阿富汗领导先进技术行动，到开发用于打击人口贩运的暗网搜索工具的经历。节目探讨了问题选择在创新中的作用，以及他见过人性阴暗面后仍保持乐观的原因。

Stanford HAI 数字经济实验室于 2026 年 6 月推出按月更新的 AI Economic Indicators 仪表盘，实时追踪 AI 采用对就业、工资、消费者剩余与 AI 使用的影响。

防御性网络安全方面可能也很出色，但没人会发这种内容来博眼球！

IBM Research 发布 ALTK-Evolve 方法，用 GPT-4.1 从 Agent 自身训练轨迹中蒸馏行为准则追加到提示词，不更新权重，使零售场景 Pass^4 从 57.5% 升至 67.5%，在 2026 年 10 月 12 日的 τ²-bench 榜单快照中超过所有零售条目。

Princeton、Stanford、UK AISI 等机构于 2026 年 10 月 19 日发布 72 页综述「Emerging Trends in AI Agent Evaluation」，用 How、What、Who 三个维度梳理智能体评测的 33 个趋势。

小红书 AllSpark Team 提出 ROSS，让模型重新学习训练中自己生成的 rollout，通过筛选有价值轨迹片段并保留完整上下文做选择性监督。基于 Qwen3.6-35B-A3B，该方法在单轮 RL 中把数学均分从 75.19% 提至 76.98%、代码从 42.09% 提至 44.21%，MOPD 均分从 58.40% 提至 62.20%。

Ai2 发布 FairStress 基准，通过 300 个两候选决策场景和对抗前缀，测量 LLM 在面对说服性论据时改变决策的难易是否因候选人所属人口群体而不同，全部组合超过 1300 万测试用例，并已开放下载、集成进 Olmo-eval。

普林斯顿论文发现，将多个开发者各自构建的专用智能体组合起来，可超越任何单一开发者的通用智能体，GAIA准确率达70%。在相同总开发预算下，单独得分最高仅35%的专用智能体组合后达到70%，比通用智能体中位数高12个百分点；但在仅需网页搜索的SealQA上，组合系统成本更高且表现更差。作者认为智能体应填补共享系统的空白：单独得分19%的智能体可将组合系统从40%提升至53%。

Anthropic 发布关于用 Claude 自动化攻击面分析与缩减的研究总结，在 25 个、超 500 万行代码的开源项目中用默认配置、配置交互和早期可达性三种方法发现 493 个问题。

据彭博社报道，研究人员在《柳叶刀·数字健康》发表研究，开发出仅凭一张面部照片预测癌症患者生存几率的 AI 工具。模型使用超过 4000 万张人脸图像和 3 万多名癌症患者照片训练，可生成风险评分，分数最高者更可能在 90 天内死亡，估算年龄误差约 5 岁，并能在不同年龄、种族、性别和癌症类型人群中生效。

有智能体把 Knuth 遗留的一个小型开放问题拆成 3,735 个 SAT 子问题，在一台 8 核机器上跑了约 153 CPU 小时，得出不存在 12 门电路，答案应为恰好 13 门。

推荐阅读！ 面向可扩展潜在token化的生成式表征学习

Princeton 团队联合 Stanford、UK AI Security Institute 和 Brookings 的研究者发布论文《Emerging Trends in AI Agent Evaluation》，从评测科学与方法、研究议程和评测生态三个维度总结 33 个新兴趋势。

智能体战争对所有国家都将是大事。AI将有能力执行震网级别的网络行动，下一场军备竞赛将在自我改进的智能体之间展开。然而目前我们几乎没有针对这一点的评测。我认为没有任何国家为即将到来的规模和速度做好了准备

Artificial Analysis 推出 AA-PersonalityBench，从 30 项人格特质和 21 项沟通特征两个维度衡量语言模型差异，基于 900 道精选问题、由 LLM 评审团两两对比打分。

MIT、波士顿儿童医院与 Inria 的研究者开发并训练了深度学习模型 E(3)-Pose，可在胎儿头部运动时辅助成像，生成供放射科医生查看的高分辨率三维图像。该模型把胎儿头部的左右对称性等物理原理嵌入网络架构，而非仅从数据中学习物理规律，从而更准确地识别大脑朝向。相关论文已在 2026 年欧洲计算机视觉会议（ECCV）上展示，目前模型主要基于第三孕期胎儿训练。

NVIDIA 在德国 Jülich 超算中心的 JUPITER 上完成 104 量子比特容错量子计算模拟，这是迄今规模最大的容错量子处理器完整模拟。该模拟验证了 magic state cultivation 方案，所需物理量子比特比此前方法少 90%，并首次在真实硬件噪声下评估其表现。模拟基于 GH200 超芯片与 cuQuantum、CUDA-Q 运行，可让研究者在造硬件前预测设计性能。

Modern text-to-image (T2I) systems use internet-scale language pretraining for text-conditioned image generation. We ask whether language pretraining is necessary for strong T2I generation. We introduce Halcyon, a family of early-fusion mu…

Multimodal large language models (MLLMs) have made rapid progress on visual reasoning, yet many tasks require evidence that is difficult to obtain through internal reasoning alone: our controlled study on 22 benchmarks shows that simply in…

While LLM agents can generate visually plausible CAD assemblies, these designs often fail to fulfill their intended physical functions. We study functional CAD design, a setting where agents interpret underspecified requests to construct a…

Multimodal reinforcement learning with verifiable rewards (RLVR) provides reliable task-level supervision, yet offers limited guidance for localizing errors in visual reasoning. On-policy self-distillation (OPSD) complements this supervisi…

Reinforcement learning enables mobile robots to make fast and scalable navigation decisions in constrained human-shared environments. However, directly extending single-robot approaches to multi-robot settings often fails to ensure safety …

In this paper, we systematically investigate the mechanisms, scaling behavior, and generalization of physical Recursive self-improvement. We introduce PhysRSI-eval to quantify physical RSI through a five-axis metric: First-Attempt Performa…

Long-horizon agents must learn heterogeneous capabilities from interleaved trajectories. Updating shared parameters to learn new capabilities can degrade previously acquired ones, resulting in catastrophic forgetting. As a trajectory unfol…

A seemingly reasonable push in Sokoban can make the puzzle unsolvable. To avoid such mistakes, an agent has to anticipate the consequences of its actions before execution, especially when feedback from the real environment is sparse or act…

The goal of a world model is to capture the dynamics of an environment. This has the great appeal that it allows agents to plan and simulate a course of actions with the help of a world model. For these uses to be reliable, however, the le…

Systems that combine several large language models (LLMs) often use one context for many questions. When the context is passed between models as text, the receiving model processes all of its tokens again for every question. Avoiding this …

Bilevel optimization provides a framework for machine learning tasks such as hyperparameter optimization, data reweighting, and meta-learning. Existing Newton-based bilevel methods typically rely on the classical hypergradient formula, whi…

LLM agents that compute the right answer can still answer wrongly. Agent harnesses increasingly keep long tool outputs out of the context window, saving them to files that the agent searches or reads on demand. We ask where this costs accu…