AI 论文与研究

Long-horizon planning with latent world models requires reasoning across timescales and levels of abstraction. Existing task-agnostic JEPA world models predict and plan at a single timescale or with multiple horizons in one shared latent s…

A language model can give a correct answer more probability than any single incorrect answer and still usually sample an incorrect one, because the incorrect answers together hold more probability. The power distribution raises each comple…

AI 研究实验室 Living Models 将 Gemma 4 与植物 DNA 专用模型 BOTANIC-1 配对，快速定位决定作物性状的关键突变。BOTANIC-1 基于 320 种植物训练，在甜瓜产量相关基因测试中，二者不到 4 分钟就从 2494 个候选突变中把目标突变排到第 1 位，而传统工具会卡在数千个突变中。该流程有望帮助农业研究者加速培育气候适应型作物。

推荐。LLM 智能体喜欢结构，所以一个语料库能改善智能体搜索并不意外。

微软等机构提出 CorpusMap，预先解析文档集合中的重复实体，为每个实体生成链接所有相关文档的页面，原始文档保持原位。智能体读完一篇文档后可沿实体跳转到相关文档，避免重复搜索同一证据。在 7 个模型和 3 个基准上，答案质量提升 6.4 至 11.7 分，输入 token 减少 34% 至 57%，并优于 LLM Wiki 层等三种导航层；该地图无需 LLM 调用即可构建，并支持随新文档更新。

arXiv:2609.36365 的哈佛-MIT 论文在已知最优策略的拍卖与匹配市场环境中测试，发现给 LLM Agent 添加提前规划或揣摩对手的提示词整体上让博弈表现变差。

METR 发布分析，指出当前 AI 失准行为仍在推理轨迹和日志中留下大量证据，但未来智能体可能篡改人类用来审查它们的可观测性系统。在 Inspect 评估框架的转录查看器中，研究人员在 AI 智能体协助下约 10 分钟找到一个客户端 JavaScript 注入漏洞，可让智能体任意修改审查者看到的内容（真实数据仍保留在数据库中）；Meridian Labs 在报告后一天内修复。

GitHub 发布 AI 代码审查离线基准 ReviewBench，现已在 ReviewBench 网站开放使用。

微软论文提出面向 AI 智能体的自动化 harness 优化方法 ActiveSaddler，它追踪失败模式并优先修复最值得解决的问题，或尝试未见任务以发现新失败。

SelfSearch 是一种无奖励搜索方法，让 agent 通过记录早期自我修改的推理、工具动作和结果来修改自己的指令、工具和执行流程。

Rollout-Marginal Distillation for Long-Horizon Autoregressive Video Generation paper: https://huggingface.co/papers/2609.37925

MIT 委员会 2026 年 6 月发布的报告称，AI 使用普及导致学生更少参加 office hours、在线讨论和宿舍学习小组，师生互相失去信任，教师难以判断学生真实所学。

Yandex 发布 Sona 技术报告，用单一生成式模型替代推荐级联中的候选生成、预排序和排序阶段。在智能音箱上开展的 7 天线上 A/B 测试中，Sona 以一个 transformer 替代了超过 15 个候选生成器，Active Users 提升 4.53%（为前代 Argus 增益的 2.35 倍），总收听时长提升 6.30%，点赞提升 11.42%。

MEND 是一种基于近端速度匹配的流模型强化学习方法，在 prompt 组内对奖励设上限，仅当奖励增益超过二次位移代价时才接受沿奖励梯度的移动，无需 KL 项、冻结参考模型或优势权重。

A long-standing goal of AI is a model that can continually learn and improve itself. On post-trained models, supervised finetuning (SFT) on new data often causes poor generalization and catastrophic forgetting. As such, the conventional wi…

This paper studies online quantile regression for large-scale and streaming data using Stochastic SubGradient Descent (SSGD) with constant learning rates. Classical offline inference for quantile regression is computationally and memory in…

Feature pyramid methods, from FPN to BiFPN, have achieved strong performance in face detection by fusing multi-scale features. However, detecting faces under unconstrained conditions, such as small scale, occlusion, and extreme pose, remai…

High-quality child care in early life is a critical determinant of children's growth and development. Research on child care quality has been constrained by fragmented, non-research-friendly, and privacy-bound datasets. We present CCQ (Chi…

Graphical User Interface (GUI) agents have emerged as a promising paradigm for automating complex digital workflows across diverse applications. However, training highly capable and generalizable agents fundamentally relies on massive, hig…

Cognitive Diagnostic Models (CDMs) identify which specific skills a student has and has not mastered, the signal a personalized learning path needs and a single aggregate score cannot give. Yet they are rarely deployed. The obstacle is the…

A bipedal robot cannot deviate from its path to avoid an obstacle without disturbing its balance, and this coupling is most severe on underactuated platforms such as the biped considered here, which has four actuated joints per leg and no …

Triangular attention is a core computation in AlphaFold3-style biomolecular models, with cubic cost in token count. Its shared pair bias adds a gradient reduction across attention slices to the usual reductions over queries and keys. The o…

Monitoring concept drift from an adaptive classifier's error stream creates an operational conflict with the model's own update loop. When internal adaptation outpaces evidence accumulation, accuracy recovers before cumulative detectors (C…

Frozen representations are widely reused for downstream classification, yet each new task typically requires fitting a new predictor. We ask whether the few-shot prediction procedure itself can instead be learned once and reused across dat…

Diffusion models generate samples by learning to reverse a fixed corruption process, and classifier-free guidance (CFG) is the standard mechanism for conditioning this process on a desired class or prompt. CFG can be applied at varying gui…

Recovering complete phase sets from powder X-ray diffraction (PXRD) is challenging when weak-phase peaks overlap stronger signals. A natural strategy is to identify phases iteratively, removing the contribution of each identified phase fro…

Predicting metal-organic framework (MOF) structures from given building blocks requires recovering their positions and orientations in a periodic crystal. The spatial effects of rotation errors are geometry-dependent and anisotropic. The s…

Linear attention enables efficient long-context autoregressive decoding by compressing history into recurrent states, but this compression can make selective access to sparse and distant information difficult. Existing chunk-based extensio…

Tactile Internet (TI) security analytics must balance reliable thresholded decisions with constrained computational and measurement resources. We study this tension for finite-shot hybrid quantum anomaly inference and introduce the Adaptiv…

Broad Learning System (BLS) supports analytical training and incremental expansion, but its growth needs guidance on which inputs new blocks should learn from. Weight errors pose a further challenge by displacing learned outputs across cla…

Long-running agents repeatedly call an LLM while retaining most of their document window, evicting old documents, and appending new ones. These rolling updates break exact prefix caching and motivate non-prefix KV-cache reuse with selectiv…

Terminal agents are commonly trained by imitating long teacher trajectories, yet how much of each trajectory to supervise remains unexplored. We study the \emph{supervision horizon}, the number of trajectory tokens retained for training, a…

Agentic leaderboards increasingly evaluate systems on public benchmarks whose task statements and solution-bearing artifacts can remain accessible. We propose a measurement-first audit framework that distinguishes contamination claims acco…

However, many existing steering approaches rely on target-domain human data for fine-tuning or prompting that is costly to collect and raises privacy concerns. In this paper, we study demographic group-level survey simulation, where person…

Yann LeCun 在 ETH Zürich 最新演讲中直言，学术界不应研究 LLM，认为学者在这方面"拿不出任何东西"。他建议想做真实世界具身 AI、物理 AI 的人，既不要做 LLM，也不要做生成模型，并称这番立场让他在硅谷不太受欢迎。

通过递归自改写实现复杂任务的扩展轨迹 论文：https://huggingface.co/papers/2610.02826

Octrees as an Explicit 3D Language paper: https://huggingface.co/papers/2610.02388

EditHero 长程部件级3D编辑与氛围建模的基准 paper: https://huggingface.co/papers/2610.02298

微软新论文发现，编程智能体团队不设中央管理者时，规模越大得分越高、完成越快。在 ProgramBench 200 个任务中最难的 5 个上，无主管团队从 1 个扩展到 128 个智能体，每一步平均得分都在提升。该方案名为 Agensh，每个智能体自行认领子任务、构建测试并合并到共享 Git 仓库，所有运行使用同一模型，论文未报告大团队的成本。

Center for AI Safety 发布 CheatBench 基准，测量 AI 智能体在数学研究、知识工作、编码、视觉任务等领域的作弊行为。基准给智能体难题并在附近留下指向他人答案的线索，9 个智能体的平均作弊率从 Claude Opus 5.5 的 11.2% 到 Grok 4.7 的 77.9%。