AI 论文与研究

哈佛物理学者 Matthew Schwartz 在 Anthropic 客座文章中提出寻找 Claude-shaped 问题，并开源了用于定量科学精确计算的 BootLoops 工具包。

微软研究院开发了一套端到端机器学习流水线，结合太阳风观测、AE 与 Dst 指数预报、地质电导率和电网数据，为美国本土 66,935 座变电站生成 30-60 分钟前的局地空间天气风险估计。

Goodfire Research 开发了利用蛋白质语言模型嵌入的序列感知监控器，用于筛查 AI 智能体生物任务中的双用途序列。在自建双用途任务基准上，其监控器在关注序列上的拦截率高于前沿模型自身防护，同时在良性序列上的拒绝率更低；对蛋白质片段改写和碎片化等对抗攻击更鲁棒，每条序列检测耗时为毫秒级，且检测能力随蛋白质模型能力提升而增强。

Google DeepMind 团队发表论文，提出基于 SynthID 的 SynthIDBio 技术，可在流行蛋白设计工具 ProteinMPNN 生成序列时嵌入不影响功能的水印，帮助 DNA 合成方筛查未知蛋白来源。

MIT、CMU、NYU 与 Stanford 的研究人员开发出 AI 系统 Ataraxos，在隐藏信息棋盘战棋 Stratego 上大幅超越世界顶级人类选手，论文发表于 Nature。

Artificial Analysis 发布 AA-Video-T2V v2.0 和无音频版 AA-Video-T2V-Silent v2.0 文生视频基准，统一以 1080p 高码率评估，按 10 种用例、10 种能力和多种视觉风格排名，分别基于超 68,000 票（1,000+ prompts）和超 47,000 票（500 prompts）的人工偏好投票。

腾讯混元联合复旦、清华发布 ExplorationBench，用可执行规则的 Alien Worlds 评测 AI 自主探索能力，含 AlienCode（31 条隐藏规则改动、70 任务）和 AlienLogic（24 条推理规则、70 定理）两个沙盒。

当前的 harness 在 LLM 如何管理自身上下文方面设置非常僵化。我们展示了你可以让 LLM 直接编辑上下文——从而实现更好的长时程记忆、FLOP 高效适配，以及更多。 不用再害怕 compaction 了！

面壁智能联合清华 THUNLP 提出 Diffusion Reward Models（DRM），不再把人类偏好压缩成单一分数，而是学习完整奖励分布，保留分歧、不确定性与多种合理判断。

BlueQubit 团队使用 IBM 120 量子比特的 Nighthawk r2 处理器，在 36 个周期、918 个双量子比特门配置下仅用 19 秒完成 100 万次随机量子线路采样，研究人员估算 Frontier 超级计算机复现同等任务约需 110 年，约 1.2 × 10²⁷ 次计算操作。

Time series anomaly detection (TSAD) is increasingly deployed in streaming settings, where data arrive sequentially and may exhibit non-stationarity. As a result, several works from the recent literature propose streaming anomaly detection…

We study additive regression under a potentially non-product random design on $[0,1]^d$, allowing the dimension $d$ to grow with the sample size $n$. We introduce coupled smoothness classes that separately control the regularity of the mar…

SpatialClaw 重新思考智能体空间推理的动作接口 论文：https://huggingface.co/papers/2606.13673

Large Audio Language Models (LALMs) rely on effective audio encoders for multi-task performance. We introduce UniAE-MoE, a unified audio encoder designed to model cross-domain audio representations and achieve outstanding downstream unders…

Wireless split learning (SL) reduces on-device computation by offloading upper layers to a server, yet transmitting high-dimensional intermediate features at each iteration remains a major communication bottleneck. Existing methods select …

A deep classifier is defined not only by the decision it produces, but also by the sequence of transformations through which that decision is formed. Treating this evolution as a dynamical system across layers provides a natural framework …

Trustworthy Legal AI requires systems that can answer legal questions while grounding their responses in authoritative sources. However, existing Vietnamese legal benchmarks provide limited coverage of real-world legal consultations. We in…

Developmental psychology holds that certain priors are given to infants prior to experience rather than induced from data, and that the influence of such priors is suppressed under strong, well-constrained conditions but reasserts itself u…

Mamba-style and hybrid language models compress their past into a fixed-size recurrent state that is rewritten at every generated token. Storing this state in low precision saves memory bandwidth, but every rounding error is fed back into …

Fiber orientation and compartmental microstructure are central to the characterization of white matter tissue in diffusion MRI, yet existing methods either resolve fiber orientations without quantifying microstructure, or quantify microstr…

World Models are appearing as the next major frontier in computer vision. However, their robustness is currently largely unexplored. We identify the phenomenon of hallucination in latent World Models: given a state and an action, the predi…

Deep neural networks tend to rely on simple features that may be spurious and thus fail to generalize. We study this problem in the setting of linear probes, where a (generalized) linear model is fitted on the representations of a (pretrai…

NVIDIA、MIT 和牛津团队发布 Physis-Lang，一个自进化的物理语言框架，通过在视频描述中加入 physics_reasoning 字段和 physics_negative_prompt，把物理语言用于数据筛选、训练和推理。

In this paper, we consider asymptotic properties of the support vector machine (SVM) in high-dimension, low-sample-size (HDLSS) settings under a spiked model. The existing theory of the SVM in the HDLSS context relies on the geometric repr…

Fingerprint recognition is a widely deployed biometric, but supervised training requires large labeled enrollment sets. Self-supervised learning (SSL) removes this requirement, and hybrid quantum-classical models have been proposed to enri…

Reinforcement Learning with Verifiable Rewards (RLVR) has improved the reasoning capabilities of Multimodal Large Language Models (MLLMs), yet existing frameworks rely on coarse, sequence-level reward signals that lack the fine-grained sup…

Persistent spatial memory enables embodied agents to navigate familiar environments across repeated visits. However, targets may move while unobserved, including during navigation, making remembered locations unreliable by the time an agen…

Score-based variational inference (VI) provides an alternative to Kullback--Leibler (KL)-based VI by minimizing the Fisher divergence between the variational distribution and the target. A prior score-VI approach formulates this optimizati…

Speaker diarization systems based on speaker embeddings and neural diarization exploit complementary forms of speaker information, but their intermediate representations are not directly compatible. We introduce Training-Free Affinity Fusi…

Deep research tasks require agents to navigate large knowledge spaces, synthesize evidence across many sources, and adapt their plans as findings emerge. Directed acyclic graph (DAG)-based multi-agent systems suit this setting because they…

Does memory-dependent control need nonlinear recurrent dynamics? We study simulated air-hockey defence under temporary loss of puck tracking. A DreamerV3 teacher outperforms a memoryless policy under tracking loss, while resetting the teac…

Textual skills enable large language model (LLM) based agents to accumulate reusable procedural knowledge without updating model parameters. Yet existing skill evolution remains largely confined to the text space: an optimizer must diagnos…

Jack Hughes、Ben Collier 和 Daniel R. Thomas 的论文分析近10万条网络犯罪论坛相关帖子，提出 vibercrime 一词：AI 是便利帮手而非犯罪的即插即用工具。生成式AI 主要扩大垃圾网站、机器人账号和拟人化诈骗信息的规模，编码助手多为已有编程能力者省时，尚未显著降低技术门槛或催生创新性黑客手段。

RSIGame 是一个带递归自我改进的自主智能体游戏开发框架，通过局部“探索-诊断-改进”循环与全局质量跟踪循环，缓解迭代优化对少量测试用例的过拟合。在 140 个 GameCraft-Bench 任务、两个游戏引擎和五个生成器上，RSIGame 在相同开发预算下持续提升游戏质量。

论文研究野生 AI 生成网页文本对语言模型预训练的影响，发现 FineWeb 过滤后 2026 年 6 月网页数据中 27.5% 的 token 被 Pangram 标为 AI 生成，8 月升至 31.1%。

LexReward 是一个面向法律语言模型的分类体系驱动奖励框架，从 Style、Element、Chain 三个维度刻画法律回答质量，并据此构建成对偏好数据用于 DPO 与奖励模型训练。实验显示，基于评分细则的奖励能可靠区分不同质量的法律回答，DPO 训练在三个维度上均带来提升；所训练的奖励模型 LexRM 可在无需参考答案的情况下，通过强化学习分别提升对应维度的策略表现。

研究团队构建了 95 小时城市步行游览视频数据集 WT++，用于严格按时间顺序、滑动窗口批次的流式自监督预训练。结果显示对比学习和蒸馏方法在此设置下表现不佳，MAE 更稳健但仍不及标准 i.i.d. 预训练，主要瓶颈是批次内帧高度相似。

研究分析了生成式行为克隆策略在相同观测对应多个有效动作时的多模态表征瓶颈：隐变量策略需在潜表征中保留动作条件信息，过强的后验-先验正则化会抑制该信息，正则化过弱则依赖部署时先验覆盖相关潜区域；动作空间生成策略则受限于基空间到动作空间传输映射的平滑性，小 Lipschitz 常数难以覆盖多个分离模态。

研究者提出 Game-Guided Skill Discovery（GGSD），通过在游戏中自对弈发现人类可直接操控的机器人运动技能。该方法让分层智能体与自身历史版本对抗，高层策略从少量离散技能中选择、低层策略学习对应行为，训练后人类可替换高层策略直接操控智能体。

研究者构建了蛋白质问答数据集 FoldingCorpus 与后训练方案 Fold2Reason，通过离散结构答案和连续 3D 几何两种信号进行训练。在 FoldBench 上，其结构预测得分达到 Qwen3.5-9B 的 2.7 至 3.5 倍，并在空间、图、科学及通用推理等 10 个基准上全部取得提升，宏平均准确率从 45.09% 升至 48.33%（+3.23 个百分点）。