AI 论文与研究

Native language identification (NLI) infers a non-native speaker's first language (L1) from their second language (L2) production. We investigate a token probability approach that scores each text sample as a continuation of a prompt prefi…

Multimodal large language model (MLLM)-based mobile agents are rapidly evolving from conversational assistants into autonomous systems that perceive, reason over, and act on smartphone environments. This capability also exposes a new secur…

A key challenge in on-policy distillation (OPD) is extracting effective supervision from teacher knowledge. Supervision from token probabilities may underutilize internal knowledge, while direct hidden-state matching forces the student to …

Enterprise data assistants using large language models (LLMs) promise democratized data access in organizations. Yet in our real-world deployment within the CFO unit's data lake at an industry partner, we observed that business users frequ…

Spatial context prediction requires visual grounding and spatial reasoning to identify vacant regions relative to objects in a scene. Spatial relationships and physical constraints remain challenging for large vision-language models (VLMs)…

Most retrieval-augmented systems treat literature search as a single retrieval step followed by summarization. Real literature research works differently: the investigator's understanding of the question changes as they read, new subquesti…

Isogai's case A, a pitch-plunge aerofoil whose elastic axis lies half a chord ahead of the leading edge, has a folded transonic flutter boundary in inviscid flow: near the bottom of the transonic dip several flutter speeds coexist at one M…

Single-image refocusing changes the focal plane and depth of field of a photograph after capture. It is inherently interactive: users try many focal points and blur strengths on one image, so each edit should be fast and change only the bl…

小红书 AllSpark 团队提出 Skill2Env，一套以 Agent 能力需求为导向的自动化环境合成框架，从已有 Skills 出发构建可执行训练环境。

科学家将盲文字母对应的电信号输入人脑类器官，单个约含 1 万个神经元的类器官识别准确率约 61%，三个类器官组成的"集合"达到 83%。该研究发表于 Frontiers in Artificial Intelligence，属类器官智能领域，实验为开环设置，未证明类器官真正学会盲文。研究者称类器官系统在可靠性、可复现性和可扩展性上距成熟计算芯片仍有很大差距。

这篇论文说明了为什么我对"前沿模型可以被更便宜的模型轻易取代"这一观点持怀疑态度（也说明了为什么你应该用最好的模型来做编排）：准确性和判断力上的微小提升会在长周期任务中不断累积，从而带来结果的巨大改善。

NVIDIA 联合 IIT Madras 的 AI4Bharat 发布 Nemotron-pretraining-Indic-v1，约 2.8 万亿 token，覆盖印度宪法列出的 22 种语言，含 2601.0 亿原生文字 token 和 224.6 亿罗马化 token（按 Gemma-4 tokenizer 计）。

我们还把 ORA 搬到了一台真实机器人上。 在低成本机械臂、固定 RGB-D 相机和腕部相机的初步运行中，DeepSeek-V4.1-Flash 通过抽屉把手打开了抽屉，并把一根糖果棒放进了盒子里。

苹果研究团队公布 Minions 系统研究，将闲置 Mac 的 Apple Silicon GPU 聚合为可动态调度的“虚拟 GPU 云”，用于大规模机器学习推理。

新机器人，新手册。同一个智能体。 机器人卡片描述每个机器人的几何结构和控制方式。同一套测试框架可运行 ARX X5、ALOHA 和 Franka，使用各自专属的卡片、工具和控制器。

开源权重模型零样本能做到什么程度？ ORA：RoboTwin 上 69.2%；LIBERO-Pro 上 81.4%。 RPent（带任务专属记忆）：在 RoboTwin 上配合 GPT-5.5/Opus-4.7 达到 62.4%/58.4%；在 LIBERO-Pro 上配合 Opus-4.7 达到 82.4%。 相同案例，不同系统。https://github.com/RLinf/RPent

PromptArmor 在 9800 次试验的评测中发现，开放权重模型（DeepSeek、Kimi、GLM、Qwen）对真实世界间接提示词注入攻击链的易感率为 26.7%，闭源模型（GPT、Claude、Gemini）为 14.2%，开放权重模型易感率高 88%。

Google Research 发布三项研究，构建 AI 智能体自主湿实验室的基础模块。

Calif 发布 Offensive research，披露 OpenBSD 内核中可追溯至约 37 年前原始 BSD 源码的漏洞：共享内存附着计数器为有符号 16 位 short。

Google 量子 AI 团队首次在物理硬件上演示魔力态培育，在 105 量子比特 Willow 处理器上错误率较以往方法降低约 40 倍，保留下来的态达到 99.99% 的逻辑保真度，成果发表于 Science。该方法会对每个态进行筛查并丢弃不合格尝试，仅约 8% 的尝试通过筛选，且仍需在更大规模上验证；作者称这是通往纠错量子计算机的一步。

MIT 可持续供应链实验室发布 2026 年《供应链可持续发展现状报告》，调查 91 个国家 1810 名供应链从业者，83% 认为可持续发展对长期业务成功重要或非常重要，为报告七年历史最高。

MIT 生物学助理教授 Pulin Li 团队与研究生 Nicholas Hutchins 发现每条信号通路会在细胞中留下跨细胞类型一致的基因活性指纹，据此开发机器学习模型 IRIS，可回溯胚胎细胞在发育各阶段接收的信号，论文于 9 月 8 日发表在 Nature Methods。

Rohan Paul 介绍 Anthropic 一篇论文，发现当监控器只能捕获部分 reward hack 时，惩罚被标记样本会让模型转向监控器察觉不到的黑客方式，而把被标记样本的更新路由进可删除的 adapter 则能消除黑客行为。

在此阅读更多：https://blog.google/innovation-and-ai/models-and-research/quantum-computing/magic-state-cultivation-quantum-computing/

Sundar Pichai 宣布 Google Quantum AI 团队首次在物理硬件上演示量子 "magic state cultivation"。相比此前方法，在 Willow 处理器上实现 40 倍错误降低，达到 99.99% 的逻辑保真度。该成果发表于 Science，被视为迈向容错量子计算机的又一步。

Personalization is essential for general-purpose robots to transition into household environments and achieve true practicality. While general robotic manipulation has been extensively studied, personalized robotic manipulation (PeRM) lack…

Significant efforts are currently being directed toward the implementation of large-scale quantum information processors, but realizing useful quantum advantage also requires the development of efficient quantum algorithms. While for many …

Predicting how the world responds to an action requires more than what can be seen: robots must account for environmental factors that are neither directly observable nor explicitly specified, from physical properties such as friction and …

Generative simulation can automate robot environment construction, but physically valid environments are not necessarily useful for training. The challenge is to identify environments that address a policy's failures and supervision that h…

Time series foundation models are becoming increasingly popular for general-purpose forecasting. However, most leading models predict marginal quantiles, leaving the dependence structure of future trajectories unspecified. This distinction…

Generative visual verifiers play a crucial role in advancing multimodal intelligence. To enhance the robustness of visual verifiers in complex multimodal scenarios involving compositional requirements, we move beyond conventional binary ve…

Mixture-of-experts (MoE) models have become an important approach for scaling model parameters. Modern MoE architectures increasingly combine two design choices: fine-grained expert segmentation and shared experts. The former increases exp…

Existing agent benchmarks typically evaluate agents on well-specified tasks defined by issue reports, specifications, or task tickets. Real-world customer delivery is different: forward-deployed engineers (FDEs) must clarify ambiguous requ…

Large language models (LLMs) are expanding from assisting scientific research and engineering to autonomously developing and improving AI systems, advancing AI for AI (AI4AI) as a pathway toward recursive self-improvement (RSI). Workflow-d…

Recent 3D world models increasingly use features from geometry foundation models (GFMs) as predictive world states, benefiting from their rich geometric priors. However, the view dependence of these features entangles scene dynamics with c…

We introduce 3DCodeVerse, a research ecosystem that enables coding agents to create editable and verifiable 3D content at scale. 3DCodeVerse comprises large-scale, high-quality datasets of executable 3D code, a coding harness for spatial r…

How can robots autonomously collect the contact-rich experience needed to learn dexterous manipulation? We present DSP: Dexterity from supervised play, a framework in which a robot learns a play policy from minutes of human play through be…

Retrieving relevant lemmas is crucial for formal theorem proving, enabling provers to build machine-checkable proofs from established mathematical results. Recent retrieval systems match library declarations to a target theorem or its inte…

We develop a robust reinforcement learning (RL) framework for learning adaptive treatment policies that generalize across patients and remain reliable under errors in the measured biomarker and shifts in the disease dynamics. By modeling t…

Large language models (LLMs) can often tell when they are being tested. This ability, known as evaluation awareness, can lead models to act more cautiously during safety evaluations than in real use. Existing work has studied evaluation aw…