AI 论文与研究

Modern language models undergo reinforcement learning with verifiable rewards (RLVR) on top of already-trained checkpoints. A key promise of RLVR is the discovery of new reasoning strategies. In principle, a model can sample novel ideas ab…

Conditional memory architectures such as DeepSeek Engram use input n-grams to look up learned embeddings, expanding the capacity of large language models (LLMs) with limited additional computation. Beyond model scaling, this architecture h…

Real-time robot control demands enough visual history to infer motion and task progress, but processing that history can delay action. We present Long-WAM, a model-system framework for scaling the context of causal world-action models unde…

Heavy-tailed noise has been widely observed in modern machine learning, motivating the use of methods like gradient clipping and normalization. While these methods are well understood in centralized settings, much less is known in decentra…

Vision-language-action models (VLAs) are strikingly sensitive to instruction phrasing and do not inherit the language robustness of the vision-language models they are built on. A one-word edit can move success by tens of points: $π_{0.5}$…

GNN-to-MLP distillation aims to retain the predictive accuracy of a message-passing teacher while deploying a graph-free MLP at inference. Existing methods mainly transfer node-wise predictions or use confidence-based reweighting, but they…

Machine unlearning asks for a deletion algorithm whose output is close to retraining from scratch without the selected forget examples. In this work, we study forget-only unlearning, where the deletion algorithm receives only the trained m…

Latent world models have shown a remarkable ability to predict future states and to plan in the real world. In practice, however, we lack a principled way to estimate how their capabilities scale with model size, data, and compute, an open…

Language-model agents are increasingly asked to carry out open-ended scientific research, yet their results are usually graded against a known answer, a rubric, or a language-model reviewer, none of which can tell whether a new scientific …

Prompted embedding models have recently received increasing attention, particularly for retrieval, where detailed retrieval instructions are provided as part of the retrieval prompt. Several new datasets and studies have examined this sett…

Large language models are increasingly applied to tasks grounded in long, heterogeneous information sources. Conventional Retrieval-Augmented Generation (RAG) relies on fixed similarity-based retrieval, while agentic variants adapt queries…

Many of the questions now put to large language models have no correct answer to score against: what a policy is worth, which option a user should choose, how to weigh competing values. Stated-preference economics has faced this problem fo…

Online learning is an attractive framework in many domains because it permits well-defined learning even when data are dependent or chosen adversarially. This generality, however, comes at a steep price, introducing significant statistical…

Robot foundation models provide strong visuomotor control, yet their performance can degrade when object positions or task instructions change. Further improvements often require post-training on substantial robot data, which can be costly…

Small tabular datasets with expert-designed spectral features are the norm in operational Earth observation, and the networks applied to them are typically hand-designed. We revisit one such published model -- a Sentinel-2 algal bloom clas…

We study the best arm identification problem in a stochastic environment with a novel form of adversarial perturbations, which we coin Shifting Means. While classically the mean rewards of the $K$ arms are stable in time, in Shifting Means…

Sampling from a softmax distribution is a fundamental operation in machine learning, but its linear complexity in the number of items makes exact sampling impractical at scale. Two-level softmax (2LS) sampling is a popular alternative enab…

How can we predict which base checkpoint is worth an expensive round of agentic post-training? End-to-end pass@$K$ tests whether successful behavior already appears in a base model's distribution, but it is a poor fit for agentic coding: m…

推荐阅读，因材施教！ Sherpa: Teaching LLMs to Teach Adaptively

Deployments of research agents are moving to populations of thousands that share one pool of compute, while most current systems organize one project at a time or leave the population unorganized. We argue that such a population will acqui…

Microsoft Research Asia 提出 Harnessed Agentic RL 训练范式并开源重建的 Agent Lightning v1.0，让部署时使用的同一 agent harness 直接参与强化学习，无需在训练框架内重写 agent。

Johns Hopkins 大学天体物理学家、Anthropic 研究员 Brice Ménard 与 Claude Science 合作制作首张完整紫外线全天图，结合远紫外 154 nm 与近紫外 232 nm 波段。

Goodfire Research 为 Kimi K3 和 GLM 5.3 构建基于激活探针加 LLM judge 的监控级联，并部署到生产推理栈。

AWS AI Labs 论文提出 AECP（Artifact-Exclusive Communication Protocol），要求多智能体编码团队只通过结构化工件通信并由 harness 执行，在不换模型的情况下将测试通过率提升 28.2%、墙钟时间缩短 16.5%。

应对紧急情况的最佳时机，是在它发生之前。 Waymo 新研究推出了一套首创框架，用于自动驾驶车辆事故管理演练——从桌面推演到全规模实战演练。 该框架改编自应急管理最佳实践，帮助自动驾驶开发者、运营合作伙伴和一线应急人员共同检验预案、加强协同。 阅读更多：https://waymo.com/blog/2026/10/incident-management-exercises/

应对紧急情况的最佳时机，是在它发生之前。 Waymo 新研究推出了一套公司中立的 AV 事故管理演练框架——从桌面推演到全规模演练。 该框架改编自应急管理最佳实践，帮助 AV 开发者、运营合作伙伴和一线应急人员共同测试预案、加强协同。 阅读更多：https://waymo.com/blog/2026/10/incident-management-exercises/

NVIDIA 论文提出 VERA，将基准轨迹转化为 9000 多个可重启沙箱环境，带 rubric 评分，只保留能运行且可从可观测证据打分的环境，并交替更新模型权重和 harness。

关于 Alexandre：https://www.linkedin.com/in/alexbouayad/ 申请参会：https://arcprize.org/events/research-summit-2026

了解如何识别 AI 生成内容：https://blog.google/innovation-and-ai/models-and-research/google-deepmind/synth-id-ai-content/

NVIDIA Nemotron 团队基于 Nemotron 3，用 SFT、RL 和反馈驱动推理分别构建了在 IOI 2026 与 IMO 2026 达到金牌水平的系统。

MIT 论文《Harness as a Language》提出 JAZ 框架，让模型把自身提示词和完整交互历史视为可搜索、可传给子智能体的代码变量，只保留 agent loop。

Traffic flow modeling is essential for understanding and predicting the collective dynamics of vehicles on road networks. Cellular automata provide a simple, interpretable yet powerful framework for representing these dynamics via local in…

An essential control of AI agents is their ability to manage runtime. This ability requires a sense of time-awareness, to predict and estimate wall-clock time and to control their own actions. Prior work has focused on time-awareness, but …

Reinforcement learning (RL) fine-tuning improves vision-language-action (VLA) policies through closed-loop experience, yet generalization beyond the fine-tuning distribution remains limited. Our analysis reveals a selective reshaping of ex…

A holographic superconductor whose scalar mass depends on the gauge field strength, $M(\Fsq)$, reproduces a superconducting dome for a suitable $M$, and recovering that $M$ from a given dome has so far taken days for a single training run.…

Artificial intelligence supports building operations in several forms, each with its own barrier. Expert rules must be tuned for every system, supervised models need labeled data that buildings rarely record, and language models return fre…

We describe the Itgan systems for the three ASR subtasks of NADI 2026, namely robust country-level ASR (1.1), mixed-dialect ASR (1.2), and Tunisian code-switched ASR (1.3). All three share one recipe, Whisper adapted with LoRA on consumer …

Sample complexity is a widely used metric in sequential decision-making problems, defined as the number of suboptimal decisions during the interaction between the agent and an environment. We study the sample complexity of stochastic multi…

Knowledge graph embedding (KGE) models encode the entities and relations of a knowledge graph into a low-dimensional latent space, enabling tasks such as classification or link prediction. Most KGE models follow an autoencoder architecture…

Prevailing multi-vector visual document retrievers store each page as about a thousand patch vectors, often in vector databases run by a third party. Since no one can read a page from its vectors, this index is easily treated as less sensi…