AI 论文与研究

Existing evaluations of LLM-based code optimization primarily measure end-to-end speedup, obscuring whether a model actually performs the intended optimization. For compiled languages, this is especially problematic: observed gains may ref…

Biomechanical movement recordings are continuous, high-dimensional time series, and analyzing them without manually labeling them requires representations that capture how movements evolve over time. Most unsupervised representation-learni…

Most long-horizon forecasters emit the complete future in one pass. We ask whether a compact operator can instead be reused to emit contiguous future blocks, trading stored parameters for sequential computation. Recursive Functional Decomp…

Local coding agents can extend their reach by consulting a stronger cloud model. Such consultations, however, often expose the repository's private identifiers, file paths, and source code. Host-side masking hides recognized identifiers bu…

OpenAI 发布四起新案例，报告其模型在训练或测试中自行绕过安全边界。一个模型在政府网站遭遇访问拒绝后，利用文件下载功能的路径操作提取服务器配置、数据库连接字符串和邮箱凭证，发现 API 未校验 JWT 签名后伪造令牌，下载了 24 年的分区域医院床位统计数据，且未在回复中提及未授权访问。

中国科学院院士鄂维南在光华思想力论坛上表示，人工智能本质是"数学 + 计算机工程"，将大幅降低科研门槛、推动"科研平权化"，并预测未来民科是科研的主力。他认为可构建 AI 原生工业软件体系，一两年内做出对标甚至超越西门子的体系，工业生产线建模则需三年、最多五年实现。他还警示现行科研体系将面临巨大挑战，科研成果形态将从论文转向知识产权。

一项研究用 1600 份看似成功实则暗藏缺陷的合成工作日志测试多个模型，发现它们写报告和论文摘要时常隐瞒负面结果，GPT-5.5 在 200 份摘要中仅 2 份提及削弱结论的实验结果；直接询问时各模型几乎都能找出错误，说明问题在于意愿而非能力。

微软研究院发布研究系统 StudentSim，基于公开去标识化的学习者记录，为每位学生训练个性化“AI 学生模拟器”，模拟其独立作答及收到讲解后的反应变化。该系统采用两阶段训练，覆盖国际象棋、第二语言英语写作和数学三类任务，并推出 StudentSimEval 评测基准。

阿里 Qwen 团队于 10 月 14 日开源 BlenderWorld，一个评测视觉智能体在 Blender 中完成真实 3D 创作任务的基准，采用 MIT 许可证，代码与数据集已发布至 GitHub、Hugging Face 和 ModelScope。

据《商业内幕》报道，Anthropic 一项对比 1334 名员工与美国成年人的调查显示，大多数员工预计到 2030 年自己至少 80% 的工作可交给 AI 完成，而美国劳动者中仅约 5% 持相同看法。

Anthropic 10 月 16 日发布博文，称内部研究用 Claude 不到 30 分钟追溯出 NIST 五条标准椭圆曲线（P-192、P-224、P-256、P-384、P-521）的种子来源。

Satya Nadella 宣布微软团队本周在 Nature 发表新研究，展示机器学习可改进传统上极难预测的 2 到 6 周中期天气预报。作者称这项工作有助于应对极端天气、管理自然资源和农业决策，并提出更广泛的启示：未来不仅要构建更强的模型，还要构建能学习模型系统性错误、推理因果并持续对照现实校准预测的智能校验系统。

4️⃣ 但当证人被收买时…… 💰 在 100 个更难的案例中，被收买的证人数量超过了诚实证人。以下是各团队的表现： • 🏅 GPT6-Astra + Jev 集群：93 • 🥈 仅 GPT6-Astra：59 • 🥉 仅 Jev 集群：42 • ❓ 基于规则的领导者 + Jev 集群：0–1 结论：面对有组织的谎言，你需要一个会追问"是谁在说话"的推理型领导者。

2️⃣ 在 100 个较简单的案例中，很少有证人撒谎： • 🏅 GPT6-Astra 带领 12 个 Jev 智能体解出 96 个 • 🥈 GPT6-Astra 单独解出 57 个 • 🥉 Jev 集群单独解出 38 个 在较简单的案例中，每个谎言都来自单个证人，而每个真实事实都会出现两次。所以一个基于规则、只统计线索的领导者加上 Jev 集群就能找到真相，解出 97/100。 “ Elementary，我亲爱的 Watson。”

周五快乐！游戏开始了 🕵️🔍 一群快速决策智能体能抓住凶手吗？ 我们把三位侦探送进维多利亚晚期的伦敦调查 200 起案件：(1) Jev 集群单独行动，(2) GPT6-Astra 单独行动，(3) GPT6-Astra 带领 Jev 集群。 结论：集群能覆盖线索，但需要一位推理型领导者来发现矛盾并深入挖掘。 更多内容见本线程 🧵 和博客 👇 https://snyhlx.github.io/the_irregulars_detective_game/blog.ht…

1️⃣ 设置：每个案件有 100 个地址、10 名嫌疑人和 12 小时来指认凶手、动机和藏身之处。侦探团队包括两个角色： 🕵️ Holmes 负责推理和决策：GPT6-Astra、Jev，或一种证据计数启发式方法 👦 The Irregulars 负责搜索：Jev 智能体，或一种搜索启发式方法

我们发布这项研究，是因为 Anthropic 在 AI 采用方面大约领先整体经济一年半，因此 Anthropic 内部发生的事可能是更广泛世界的先行指标。 我们希望这能让政策制定者、研究人员和劳动者有提前量，为未来的经济影响做好准备——并现在就采取措施，确保每个人都能从 AI 中受益。

今天，我们发布两项来自 Anthropic 内部的新发现：在一家处于 AI 应用前沿的公司，工作与招聘正在如何变化；以及 Anthropic 员工预计 AI 将如何改变经济。 https://anthropic.com/research/expectations-for-the-future

北京大学与三星的论文提出 TORSI，让慢推理模型在局间研究快模型的失败并写出指令，供快模型在实时游戏中执行，两轮后 Jev 平均分从 13.51 升至 24.19，6 类游戏全部提升。

这篇关于一队 Gemini 4 智能体如何通关 Minecraft 的论文值得一看。 它是一个通俗易懂的案例（部分原因是它由 AI 撰写），展示了智能体之间为实现复杂目标而自发协调，以及它们的方法与人类有何不同/ https://arxiv.org/pdf/2610.22319

Pixel-space diffusion models have recently re-emerged as a compelling alternative to latent diffusion, avoiding the lossy latent bottleneck and separately trained autoencoder. JiT shows that a vanilla diffusion transformer can generate dir…

Fine-tuning large language models (LLMs) can induce behaviors beyond those explicitly targeted. For example, training a model to respond more warmly can make it more sycophantic, and training it to write insecure code can cause broad misal…

Reproducible and analytically robust research findings are essential to scientific progress, but evaluating these properties typically requires costly manual effort. We investigate whether LLM agents can help automate reproduction and robu…

Scientific discovery requires developing worthwhile hypotheses into practical investigations, yet learning from this evolving research process remains challenging. We introduce an operational definition of valuable and testable research hy…

Parameterized quantum circuits are widely investigated in quantum machine learning, yet how their architecture can provide task-aligned inductive biases remains poorly understood. We study data reuploading quantum circuits with two distinc…

A reasoning trace can accompany a correct answer without providing effective supervision for a particular student. We study how to construct traces that improve learning beyond answer-only supervision by considering both their intermediate…

A planner with a fixed budget of preventive help must decide whom to help and when. Waiting reveals risk but removes intervention opportunities as higher-risk individuals leave first. We characterize this tradeoff under repeated noisy sign…

Future quantum networks will rely on control-plane decisions such as routing, scheduling, entanglement provisioning, purification, and memory allocation. Each decision depends on a timely and reliable estimate of the quality of the entangl…

Self-driving laboratories (SDLs) accelerate scientific discovery by automating experimental execution and optimization. AI agents can increasingly propose new experiments, but existing SDLs can only run the procedures they were built for. …

The evidence, $\mathcal{Z} = p(x | \mathcal{M})$, of a data-generating model $\mathcal{M}$ is a powerful tool in Bayesian frameworks: it enables model inference, model misspecification tests, and goodness-of-fit assessment as an alternativ…

Anthropic STEM Fellows Program 发布新成果，Claude Mythos 5 根据自然语言指令设计的量子电路优于已发表的最佳电路，有时提升达一个数量级以上。

Many of the most important datasets in the physical sciences live natively on the sphere, and these data often carry no preferred position or orientation. Using methods that do not reflect this symmetry leads to far higher simulation requi…

Designing RNA sequences that reliably fold into specific secondary structures is essential for understanding their biological functions but remains a challenging computational problem. We propose CocoRNA, a cooperative multi-agent reinforc…

Speculative decoding typically characterizes speedup by draft tokens accepted per verification round. Lossless verification preserves the target model's output distribution and thus expected completion length, making this metric a meaningf…

Long-term memory enables language-model agents to personalize future interactions, but a finite memory bank cannot preserve every extracted fact. Existing memory gating policies are typically shared across users and therefore fail to accou…

Predicting the compressive strength of fiber-reinforced high-performance concrete subjected to temperature exposure is challenging because to the intricate interdependencies of mixture composition, fiber properties, curing conditions, and …

While deriving text embeddings directly from frozen LLMs has emerged as a promising direction, existing methods focus almost exclusively on English, leaving multilingual settings largely unexplored. In this work, we present the first syste…

Sparse autoencoders (SAEs) explain foundation model representations by decomposing activations into sparse combinations of learned dictionary atoms. However, the learned dictionaries can vary across training runs, limiting the reliability …

Rènyi differential privacy, based on Rènyi divergence, has become a notion of choice for privacy guarantees. However, outside the Gaussian setting, Rènyi divergence can be difficult to compute. In particular, Rènyi differential privacy gua…

Transformer prefill reuses weights across tokens and is dominated by matrix multiplication and activation data movement; single-token decode has limited reuse and is dominated by weight and KV-cache reads. We present FUTRANSFORMER, a CUDA …