AI 论文与研究

Meta 提出将智能体 harness 优化拆分为多个分支的搜索方法，每个分支保留自身优于其他分支的开发用例、丢弃所有分支已解决的用例，并基于历史重写自己的提案策略，再由路由器为新输入选择分支。

Microsoft 论文提出 Coding-Agent Skill Distillation（CASD），把 agent 的已有日志交给普通编码 agent 写代码做语料级统计，识别重复失败模式并生成优化后的系统提示词，无需迭代回滚。

3D Gaussian avatars support fast rendering, however, their real-time animation is often challenged by the costly neural inference. We address this bottleneck and show that the animation of pretrained avatar models can be closely approximat…

LLMs are increasingly applied to cybersecurity workflows, where they are expected to translate analysts' intent into tool invocations. However, existing evaluations focus on knowledge-based assessments or end-to-end agentic tasks, and do n…

Building reliable robot capabilities across diverse tasks requires substantial human effort to develop and maintain skills, design rewards, and integrate perception with control. We present Reconstruct, Practice, Go Real (RPG), a framework…

In diffusion transformers, a class label or a text prompt is embedded once, and the same condition is reused at every denoising step. We ask whether predicted embeddings can serve as this condition instead. Next-Embedding Predictive Autore…

What makes great scientists great? Even as AI systems start to make progress on open problems, scientists remain far ahead of them at sensing which prior idea, buried in an ever-growing archive of research, a new problem needs. To study th…

High-resolution 3D generation increasingly relies on voxel latents and multi-stage pipelines that first predict active structure and then synthesize local geometry. While effective, this design fragments continuous surfaces into many local…

We show that multimodal models possess strong reasoning abilities and that an appropriate harness can unlock their potential to solve tasks across diverse interactive environments. We introduce VISTA, a visual harness that gives a general-…

Full-parameter fine-tuning of large language models (LLMs) incurs substantial optimizer state memory overhead, limiting the model sizes that fit on modern GPUs. Existing approaches either compress optimizer state, abandon first-order gradi…

Several state-of-the-art methods for online reinforcement learning in continuous control improve policies using action gradients of a learned critic. However, critics are typically trained to predict returns, and accurate value predictions…

Discrete diffusion language models offer a compelling alternative to autoregressive generation for tasks demanding bidirectional reasoning and global constraint satisfaction. Yet they share a structural bottleneck: when decoding in paralle…

The generalized Schrödinger bridge on a graph moves mass between two distributions while charging a cost for the states visited. It has been approached by learning the rates of a controlled continuous-time Markov chain, with a temporal-dif…

While Large Language Models (LLMs) have demonstrated striking capabilities on frontier mathematical problems, it remains unclear whether they possess the structural mathematical understanding underlying their solutions. In this paper, we t…

Step-size selection remains a central challenge in large-scale neural network optimization; conservative steps slow convergence, while aggressive steps can destabilize it. We combine \textbf{Z}ero-and-\textbf{F}irst-\textbf{O}rder optimiza…

Intrinsically disordered protein regions (IDRs) play central roles in cellular processes such as transcriptional regulation, signal transduction, and subcellular localization, yet their functional design remains challenging. Structure-base…

Distribution Matching Distillation (DMD) trains a few-step student from the difference between separately estimated target and student scores, so it must keep an auxiliary diffusion model fitted to the student's evolving distribution at ex…

Molecular learning models are strongly shaped by their underlying representations. Yet standard sequential and graph formalisms struggle to explicitly encode higher-order topology, such as ring systems and recurring motifs. Existing higher…

Looped Transformers achieve parameter efficiency by repeatedly executing a shared block across recurrent loops. Each loop yields an intermediate representation decodable for the same next token, yet standard decoding discards earlier state…

Quasi-Newton (QN) methods have long been among the most effective methods for large-scale unconstrained convex optimization. Two obstacles have limited their use in deep learning: non-convexity and enormous parameter sizes. We introduce So…

Current controllable video generation systems often rely on 2D motion trajectories or sparse drag signals for object motion. These controls are ambiguous because the same 2D trajectory can correspond to different 3D motions, especially whe…

Multi-teacher on-policy distillation (MOPD) aims to combine the strengths of RL-trained teachers in a single student, but how teacher signals affect parameter changes remains underexplored. We study Qwen3-1.7B with four domain teachers tra…

NVIDIA 新论文提出，与其给 AI 智能体更多选项，不如给它一个更好的裁判：让小型智能体每步生成 8 个候选命令，再由裁判模型挑选执行。用前沿强模型当裁判时，成功率从 50% 提升到 68%，且无需重新训练；若由小模型自评，提升则小得多。

营销公司 Graphite 对前沿模型写作习惯的研究发现，共 13000 个短语在 AI 内容中出现频率至少是人类内容的两倍。

卡内基梅隆、MIT、纽约大学和斯坦福的研究团队开发的不完全信息博弈 AI Ataraxos，以 15 胜 1 负 4 平击败了被认为是最强 Stratego 玩家的 Pim Niemeijer。

Meta 等机构提出 Context Language Models（CLM），把上下文作为文件让模型用 Bash 自由编辑，自主决定保留、重写或删除内容，并区分于 RLM 不允许模型直接编辑实时交互上下文的做法。zero-shot 下在 BrowseComp-Plus 上比现有上下文管理策略高 11.4% 准确率、省 21.5% FLOPs；在线 RL 使 Qwen3.5-9B 提升 47.6% 且少用 12% FLOPs；配套 Suffix Cache Reuse 相比…

Meta 等机构发布论文提出 Context Language Models（CLM），将上下文作为可读写的文件让模型用 Bash 自主编辑，决定保留、重写或删除内容。

研究人员开发的 AI 系统 Ataraxos 以 85% 的有效胜率（平局计半胜）击败了曾获 4 次世界冠军的史上最强 Stratego 选手 Niemeijer，并在 2025 世界锦标赛表演赛中取得 40 局 38 胜。

Adobe 提出把标准答案写成每次运行都重新取数的代码，再由 AI 评分器对照检查智能体回答。在 53 个测试用例上，这种做法的 AI 评分与人类专家的一致率比文字描述答案高 29%，且少用 16% token；若没有可对照的答案，AI 评分器表现还不如随机。论文题为《Skill-based Agentic Evaluation for Real-time Data Science Tasks》。

Google DeepMind 联合牛津、芝加哥大学论文探讨 AI 大量取代工作后政府应如何保障民众，结论是没有任何单一政策在所有情景下都有效。再培训等最受欢迎的思路只在 AI 持续创造新岗位时有用，否则只是重新分配剩余岗位。作者建议现在扩大失业救济与低薪税收抵免，失业持续则把抵免转为收入下限，劳动者经济份额持续下降则让公民持有公共投资基金份额。

We introduce Gacha Decoding, an inference-time method for eliciting diverse language model generations that scales with model capability. Across open-ended domains (in-the-wild chat, creative writing, planning for image generation, and pro…

Peptide sequence governs both the structures formed through supramolecular assembly and the dynamics by which they emerge, but predicting either requires resolving slow collective processes among many interacting molecules. Molecular dynam…

Attributing model behavior to synthetic training data requires knowing what produced each training item before estimating what that item caused. A waveform-label pair does not preserve this knowledge. We propose a generation-provenance sub…

We study causal logistic bandits with counterfactual fairness constraints. The causal structure is given through known factual and counterfactual feature maps that share an unknown logistic reward parameter, but the learner observes only f…

A user's movie, news, and dialogue histories differ in their native actions and outputs, yet each interaction supplies evidence that can update user memory. We study whether these histories can train one reusable update mechanism. An actio…

World models allow agents to plan in latent space by choosing a sequence of actions that most reduces the distance to a given goal state. Thus, planning can benefit from latent representations whose distances mirror commute-times in the en…

Understanding a nonlinear dynamical system from time series requires not only reproducing its trajectories, but also identifying a simple representation that preserves its essential dynamical structure. Almost-linear recurrent neural netwo…

Factories are shifting toward smaller lot sizes with high product customization, requiring frequent re-programming of flexible and reconfigurable automation systems. LLM-based agents can be deployed in two complementary roles: Offline, the…

Multilayer networks allow the modeling of interactions between the same entities across different contexts, such as temporal observations, varying settings, or interactions of different types. The goal of community detection in multilayer …

Single-particle cryo-electron microscopy (cryo-EM) has become a widely adopted technique for biomolecular structure determination. The conventional cryo-EM computational pipeline first combines many particle images to reconstruct an electr…