AI 论文与研究

An LLM agent's capability is largely magnified by its harness, namely the prompts, control flow, tooling, memory, and context management surrounding the frozen backbone model. Recent methods increasingly automate this process by iterativel…

Agents today often take real-world actions that depend on long-term memory and context recall over time. However, most current memory benchmarks are built for a conversational question-answer format, where the question itself signals that …

As agents are deployed with increased autonomy, even extremely rare events along their stochastic output trajectories can occur and prove catastrophic. Safe deployment therefore does not depend on whether these events can occur, but on how…

LLM agents are increasingly deployed in collaborative settings, yet long-term interaction may give rise to undesirable coordination. We study the emergence of collusion in a long-horizon multi-agent environment: two agents repeatedly compl…

Scientific workflows often require choosing among known relations before a deterministic calculation can proceed. Whether observations share a culture, treatment or reference standard can change the scientific meaning of the resulting coun…

OpenAI 正与一个由数学家组成的独立顾问小组合作，以负责任地分享 AI 与数学领域的进展。该小组将就如何评估和传达新的数学成果、维护学术与职业标准，以及构建支持数学研究和学习的工具提供建议。OpenAI 希望让数学家处于核心位置，共同塑造 AI 如何支持数学理解并让成果惠及更广泛社区。

Visual instructions for physical tasks are typically authored in one context and followed in another, requiring users to translate demonstrated tools, materials, and spatial relationships into their own environment. We introduce Generative…

Pharmaceutical process characterization is central to Quality by Design because it defines how variations in process parameters affect the ability to meet product quality specifications, thereby supporting proven acceptable ranges and robu…

Neural operators evaluate parametric partial differential equations cheaply but degrade sharply outside their training distribution. Physics-informed neural networks avoid dependence on labeled data, yet their optimization can be basin-fra…

A model generalizes outside its training distribution only when it computes a representation structurally equivalent to the generating mechanism, not an approximation fitted to it. Such equivalence is necessary for exactness in and out of …

Despite the success of neural models in natural language processing, their black-box nature limits interpretability and conceals the linguistic phenomena underlying their predictions. We present SLITE, an explainable hybrid model for Recog…

In this paper, we study nonasymptotic $L^p$ error bounds for interval length and conditional coverage in split conformalized quantile regression (CQR). Our bounds rely on local regularity conditions and accuracy guarantees for the estimate…

Personal AI agents make recommendations and take actions on people's behalf in high-stakes economic contexts, e.g., buying a flight, choosing health insurance, or selecting a graduate program. The agent is given access to the user's person…

Scientific weak signals are early, low-visibility research directions that later become central to mature scientific topics, yet existing resources such as trend tracking, citation forecasting, and foresight reports rarely provide validate…

LangSmith 现已支持将 Jev 用作评估裁判（Jev-as-a-Judge），可对智能体 trace 进行结构化反馈评估，覆盖生产运行、数据集与回归测试场景。官方称该方式更快、更便宜。

用 Jev 重新整理约 2.3K 篇 AI 研究论文，总成本 $0.14、耗时约 83 秒。Jev 与旧标签（由 DeepSeek V4 Flash 生成）一致率 75%，并找出约 579 处高置信度主题变更；人工抽检 30 处分歧后全部采纳，变更已在生产环境验证。作者认为通过组合 System One 与 System Two 模型可显著改进流水线。

Grok 4.7 基准测试

Artificial Analysis 评测 OpenAI 的 GPT-6 Sol 和 Luna，两模型价格较 GPT-5.6 约减半，Sol 定价 $2/$10、Luna $0.10/$0.50 每百万输入/输出 token。

通义 QwenAudio 上线 Qwen-Audio-3.1-Realtime 产品页，并同步放出技术报告预览，导航栏已链接至报告 PDF。该仓库共 3 次提交，最新一次于 9 月 21 日完成，页面包含 demo 与概览脚本。

定制分子正在推动医学、材料和农业的进步，但生产它们既慢又贵。一篇新的 Nature 论文重点介绍了 RetroChimera，一个有助于加速化学合成的预测模型，帮助研究人员探索广泛的分子。: https://msft.it/6012a5Ogy

Benchmark 五位普通合伙人 Jack Altman、Peter Fenton、Chetan Puttagunta、Everett Randle 和 Eric Vishria 将在 TechCrunch Disrupt 2026 主舞台同台，这是该机构现任全体合伙人首次集体亮相 Disrupt 舞台。

Multiverse 提出将 LLM 整块删除重构为受限二元优化问题，等价于求解 Ising 玻璃的低能态，从而无需跑 benchmark 即可筛选候选配置。在 Llama-3.3-70B-Instruct 压缩 50% 时，该方法在 MMLU 上比最佳同类块删除方法高出近 23 个百分点。

Principal Component Analysis (PCA) minimises aggregate reconstruction error, which can inadvertently represent majority subgroups with substantially higher fidelity than minority subgroups. Fairness-aware extensions of PCA correct this dis…

We introduce the Endless Exam, a benchmark for measuring mathematical progress from today's models toward artificial superintelligence through fourteen parameterised construction families. Each submitted object is checked automatically for…

Concepts are commonly defined as abstract, compact representations of knowledge and treated as basic units of intelligent behavior. Yet, cognition, psychology, and AI lack a shared mathematical language for them. Modern systems represent c…

Functional annotation of newly sequenced proteins remains a bottleneck in molecular biology: the number of sequences in public repositories grows far faster than the capacity for manual curation. Most computational approaches consider anno…

Random forests (RFs) predict well but are opaque, whereas single decision trees are interpretable but unstable. Artificial representative trees (ARTs) were developed as interpretable surrogate models for RFs, but their use as standalone pr…

Model merging aims to combine multiple fine-tuned models derived from a common pretrained model into a single multi-task model without additional joint training. Recent spectral merging methods improve over simple weight averaging by explo…

In recent years, large language models (LLMs) have emerged as a popular alternative for evaluation. Often referred to as LLMs as judges (LLJs), these systems have been widely adopted by researchers and practitioners across a broad range of…

AI agents are being deployed rapidly, accompanied by a growing number of AI-specific attacks and corresponding incidents. As incident reporting becomes increasingly important for legal compliance, governance, accountability, and security; …

Fine-tuned checkpoints and adapters now fill public repositories, and the most common operation applied to these artifacts is model merging: arithmetic on their weights that assembles capabilities cheaply. We ask what this operation does t…

Offline reinforcement learning (RL) typically trains a critic by minimizing a regression loss against bootstrapped value targets stabilized by target networks with exponential moving average (EMA) updates. Multi-step targets incorporate be…

In this work, we present a method for shape reconstruction and tracking from video via agentic analysis-by-synthesis. Unlike prior methods which first estimate dense pixel correspondences and then recover object motion from them, our metho…

Visual token pruning is a promising approach to reducing the inference cost of large vision-language models (LVLMs), yet aggressive token reduction often causes substantial performance degradation. We identify three key factors behind this…

Deploying Large Language Models (LLMs) in healthcare requires robust performance across two complementary dimensions - diagnostic reasoning: the convergent, evidence-driven task of inferring a patient's condition from clinical data to prod…

Music festival lineups emerge from complex relationships among artists, genres, releases, labels, and past performances, making the prediction of future lineups a natural fit for temporal knowledge graph (TKG) forecasting. In this work, we…

Using a Large Language Model (LLM) as the clusterer at production scale is hard: prompts cannot hold the entire label space, and per-document serial processing does not deliver the throughput real workloads require. We present RAILS, a ret…

Distributed reinforcement learning (RL) scales training by parallelizing actors and learners around an Experience Buffer. As RL workloads grow, however, the buffer becomes more than a replay queue: it is the storage substrate of a large-ca…

Intelligent transportation systems require Incremental Learning (IL) to continually improve their overall performance in dynamic environments. However, most edge devices lack the computational resources to support on-device IL, requiring u…

Predicting postprandial glycemic response (PPGR) is fundamental to personalized nutrition and type 2 diabetes management, yet existing approaches typically rely on manually reported dietary intake, limiting their scalability in free-living…