AI 论文与研究

We consider online prediction from experts, a fundamental problem in machine learning, under differential privacy constraints. Existing private algorithms achieve near-optimal regret $\tilde{O}(\sqrt{T})$, where $T$ is the time horizon. Ho…

Graph-level out-of-distribution (OOD) generalization is critical for deploying graph neural networks in real-world applications where distribution shifts are inevitable. Existing approaches typically require class labels during representat…

O'Reilly 基于 209 名安全从业者（总样本 351 人）的调查报告分析 AI 与安全的交叉现状。最常见攻击是 AI 生成钓鱼（55%）、漏洞扫描利用（30%）和 AI 辅助恶意软件生成（26%）；受访者最担心的风险是过度依赖 AI 未加人工验证和通过 AI 工具泄露敏感数据（各 49%）。

Model predictive control is a high-performance control method, but it incurs comparatively high online computational costs across many control tasks and is challenging to realize in low-level hardware settings. To address this, we present …

One of the enduring challenges in online platforms is recommendation. Both communities and advertisers face difficulties in analyzing complex user behavior, identifying habits and evolving preferences, and providing timely recommendations …

Aligned large language models are trained to comply with benign requests while refusing harmful ones. However, recent work shows that this refusal behavior is fragile and can often be disrupted by simple activation- or weight-space interve…

Training modern AI models has become increasingly expensive, and updating deployed models can alter their internal feature representations, affecting applications built upon them. In retrieval systems, such updates often require complete g…

Linear-attention language models keep their context in a recurrent state of fixed size, where an inference system stores one copy of this state for every sequence it decodes at the same time. Storing the state at lower precision therefore …

Direct preference optimization (DPO) incurs training costs in output scoring, sequence execution, and backpropagation, so reducing supervision alone does not necessarily save computation. We introduce a unified framework for stochastic pru…

Autoregressive (AR) models have proved effective for zero-shot prediction from longitudinal electronic health records, but impose strictly sequential generation on trajectories containing temporally clustered and interdependent clinical ev…

We study an extremal regression problem, an example of multiple instance learning (MIL), where the predictors are given as multisets, called bags. The bag elements (called instances) possess hidden labels which are pooled to produce an obs…

Residual-based causal discovery assumes that each variable is generated from its parents and an independent noise source. It estimates this noise and tests its independence from candidate causes. For identifiable functions such as additive…

Masked language modelling (MLM) is the dominant pretraining objective for transformer encoders applied to longitudinal electronic health records (EHRs). However, reconstructing individual events may overemphasise noisy or weakly informativ…

Speculative decoding accelerates large language model inference by employing a compact draft model to propose candidate tokens that are verified in parallel by a target model. Conventional training relies on soft-label distillation, which …

小红书自研的稀疏注意力索引器加速方法 DoSA，在保持原始 Top-K 选择结果的前提下，用分数上下界提前排除不可能入选的候选。

Anthropic 的 Claude 在 Claude Science 研究环境中几乎自主设计了针对蝎神经毒素的蛋白质结合剂，初始提示后独立工作 24 小时，共产出 318,484 个设计。

李飞飞在 Cloudflare Connect 2026 上表示，自己也是不少前沿闭源模型的满意用户，但开放生态的分量正变得越来越重。她给出两点理由：开放权重让创业者能站在其上创新，突破可能从任何地方冒出来；模型能力太强必须安全，而安全与危险的边界不能只交给忙着变现的公司判断，学术界、公共部门和政府都需能上手检验。她还批评动辄把“人类灭绝”挂在嘴边的技术讨论方式不负责任。

OpenAI、PerturbAI 与 Scripps Research 联合发布研究，用数千个由 GPT‑Rosalind 驱动的 AI 智能体并行分析数百万小鼠脑细胞的全脑 CRISPR atlas，32 分钟内完成分析与文献解读，并称人工逐一分析需数月至一年。

DeepSeek 开源支撑其线上生产服务的高性能推理框架 DeepInfer，基于 Apache 2.0 许可证，并同步发布技术报告。框架集成 FlashMLA、DeepGEMM、DeepEP 等此前开源内核，加上调度、并行、DSpark 投机解码等系统层实现，同时支持 NVIDIA SM100 GPU 和华为昇腾 950 NPU。

Google Research 在 JAMA Network Open 发表前瞻性多中心研究，评估基于"盲扫"超声的 AI 孕周评估模型在不同临床环境和设备上的泛化能力。

感谢试用 Claude！ 对一个用 Go 构建、并移植到 Rust 的 http 代理进行基准测试，看看能否进一步压榨出更多性能。

OpenAI 于 10 月 20 日发布研究报告，介绍其增强 GPT-6 Astra 等模型构建和迁移 COBOL、Fortran、Visual Basic 等低资源编程语言的能力，并推出 MultiLangBench（20 种语言从零构建兼容 SQLite 的数据库引擎）和 COBOL 迁移至 C++、Java、Python、Rust 两项评测。

Meta 等机构发布 ProPerMem 基准，用约 618K token 的全年个人记忆和人工验证的触发窗口评测记忆驱动的主动助手。所有基线最高仅 15%，GPT-5.3-Chat 在窗口内给定证据时达 96%，但自主定时降到 38%、加检索降到 15%；兼顾内容与时机并减少触发可把准确率从 10% 以下提升到 42.4%。

一位作者结合自己以词频统计判定作者身份的本科论文经历指出，AI 生成的文本会抹平风格，让人难以感知作者本人及其语境。AI 写作还会损失精度：模型需靠假设把文本拼接起来，作者也少了在落笔中发现逻辑漏洞的机会。作者称自己读 AI 生成文本的速度至少慢三分之一，加上多数模型极为啰嗦，读者侧的总阅读时间大幅上升。

Rohan Paul 介绍了 Google DeepMind 论文《Are Self-Evolving Agents Continual Learners?》的发现：自我进化智能体通过编辑系统提示词、记忆或技能文件来改进，但在任务流变化时会出现遗忘，学新任务最快的方法破坏旧任务也最多。

Anthropic 在科学博客发布 Claude 设计蝎子神经毒素蛋白结合剂的研究，Rohan Paul 转述称 Claude 在 Claude Science 环境中作为智能体自主完成整个蛋白设计流程：选择毒素攻击位点、驱动 12 个蛋白设计程序、自设评分阈值。

Video generation models can now depict remarkably realistic motion. Yet it is unclear how to use this motion beyond pixels: to animate a specific 3D asset and replay the animation in its 3D scene. For instance, a video model can show a mon…

Agentic artificial intelligence (AI) systems hold immense potential for automating complex workflows in materials discovery and design, but extending them to domain-intensive mechanistic investigations remains challenging. Here, we present…

Dexterous multi-finger hands hold promise to unlock diverse skills like functional grasps, in-hand reorientation, and dynamic handover that the human-designed world assumes, yet existing learning recipes force a trade-off. Goal-reaching re…

Motivated by the human ability to integrate experience across language, perception, and action, we introduce UniWALM, a unified world-action-language model that learns video, action, and language in a single shared representation, where ea…

Warehouse robot fleets continuously observe operational conditions, but preserving these observations as reusable semantic memory incurs substantial computation, communication, and storage costs. For example, explaining recurring aisle blo…

Planning under uncertain control execution noise requires reliable feedback policies across diverse environments. We introduce Planning Under Noise (PUN), a neural operator that learns noise-aware value fields for planning in unseen geomet…

Spatial intelligence has recently been assessed through generative tasks such as spatially grounded image editing, yet language-guided generation of spatially consistent views from partial multi-view observations remains underexplored. We …

Multi-token prediction (MTP) can accelerate language-model decoding by drafting several tokens and verifying them in a single target-model pass. However, the best speculation horizon changes with context: longer drafts offer greater amorti…

Graph generative models commonly use graph-specific Transformer architectures to jointly update node and edge features. However, these architectures do not directly benefit from recent hardware-aware attention implementations, limiting the…

Picture-description dementia classification uses a shared visual scene, but a transcript-level prediction alone does not identify the scene evidence underlying its inputs. NarraSpace links words and speech times to named picture elements a…

Modern distributed microservice deployments generate massive volumes of heterogeneous, multi-format observability logs across physically and logically partitioned infrastructure. Isolating root causes during cascading failures remains an a…

Mathematical reasoning remains underexplored for low-resource languages, including Dialectal Arabic. To bridge this gap, we introduce the DAR-MGSM (Dialectal Arabic Multilingual Grade School Math) Benchmark, the first manually curated benc…

Current evaluation of LLM-generated debate largely focuses on argumentative quality, leaving the value structure of generated arguments underexamined. We introduce a diagnostic metric framework for comparing how values are expressed and or…

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…