AI 论文与研究

腾讯联合清华、上海 AI Lab 发布 ContextPilot 论文，训练智能体主动管理自身工作上下文，并在单次上下文编辑层面分配信用。方法在搜索、删除、摘要之外加入全局规划、长期记忆和自适应软压缩，让智能体可以卸载信息而非只能丢弃；训练上利用上下文与熵变化定位关键编辑决策，采样分支并从经过该编辑的分支轨迹估计动作级优势。

论文《Teach the Magnitude， Not the Direction》提出 CREST 框架，为多轮多步 LLM 智能体做分层信用分配：每轮单独获得验证的信用，再由同一模型充当自教师，对轮内不确定决策加大学习权重，但教师只能调节更新幅度、不能推翻验证器的判断。

While WhisperX accelerates speech transcription via intra-audio batching, it isolates audio segments, losing the historical context needed for coherent punctuation and terminology transcription. Conversely, standard Whisper retains context…

Bridging model-based control and learned policies in long-horizon manipulation has harbored a silent disagreement: control executes specified objectives, learning amortizes that behavior into a reactive policy, yet existing protocols disca…

Uncoupled no-regret dynamics provide a decentralized route to equilibrium, but prior guarantees for individual regret retain a polylogarithmic dependence on the horizon. We remove this dependence for every finite $N$-player normal-form gam…

Many parameter-efficient methods generate the parameters of a large neural network from a low-dimensional latent representation. Given an architecture $Φ$ with $P_Φ$ parameter slots, we write $\boldsymbolθ_f=\mathcal{G}(\boldsymbolξ_f)$, w…

The 2025--2026 AI market has seen a wave of stealth releases: frontier models launched anonymously on developer platforms under codenames. For their users, identity determines data-handling terms, supply-chain risk, and capability expectat…

BioMedRAG introduced retrieval-augmented generation with a learned chunk scorer for biomedical information extraction. However, it relies on fixed-size chunking which can fragment semantic evidence. We propose a configurable semantic chunk…

Ontology alignment (OA) has evolved through several methodological paradigms, ranging from lexical and structural aligners to knowledge graph embedding (KGE) models and, more recently, Large Language Model (LLM)-based approaches. Although …

Neural network mixed-effects models (NMMs) have gained traction by combining the strong representation and predictive power of artificial neural networks with the capacity of mixed-effects modeling to capture complex correlation structures…

Large language models (LLMs) offer promising clinical decision support but remain vulnerable to hallucinated facts, unsupported recommendations, and citation errors. We present DIASENTINEL, a fully on-premise multi-agent system for one-yea…

The motivation for this paper is the investigation of the trade-offs implicit in probabilistic models used in machine learning. Models are often used to make predictions in the form of conditional probabilities. However, a pair of conditio…

Research planning is the decisive capability of AI scientists. Yet a research plan admits no verifiable answer, so reinforcement learning lacks the environment it requires: tasks paired with a critic. Rubrics extracted from scientific pape…

The effect of Large Language Model (LLM) scale on ontology learning (OL) performance remains insufficiently characterized. We present a controlled evaluation of 13 models spanning dense and Mixture-of-Experts variants from the Qwen3.5 and …

Google 提出 SKILL.state，用显式可变执行状态替代追加式对话历史，解决长程智能体运行变慢和上下文污染问题。模型每步仅读取不可变技能规范、当前结构化状态和最新观察，中间推理在生成有效状态更新后即被丢弃，提示词不再随运行增长。在多个数据集、模型和执行环境中，任务准确率提升，累计 token 消耗下降，且该抽象与架构无关，可移植到现有技能运行时。

Generative models have become central across science and industry, from image and text synthesis to the design of molecules and materials. Quantum generative models are considered one of the most promising applications for quantum computer…

Many important forms of human learning begin with a vague goal, such as "become a better physicist" or "improve at research." Learners must interpret the goal, identify capability gaps, decide how to learn, and determine whether they have …

Efficient evaluation changes the protocol used to support claims about model behavior, yet it is rarely tested whether those claims remain stable after the evaluation itself is made cheaper. We stress-test conclusion robustness in responsi…

Users of a deployed language model routinely encounter behaviours that testing almost never surfaces, since deployment puts the model through orders of magnitude more interactions than any evaluation can simulate. Automated auditors make t…

Industrial post-training is a brownfield regime. Teams inherit a deployed checkpoint and must land targeted improvements under fixed compute and mixture budgets without regressing the rest. The maintained artifact is increasingly dataware:…

Google 等机构提出 SKILL.state，用显式可变执行状态替代不断增长的对话历史，解决长程任务中智能体变慢和上下文污染问题。模型每步仅读取技能规范、当前状态和最新观测，中间推理在生成有效状态更新后即被丢弃。在多个数据集、模型和执行环境中，任务准确率提升，累计 token 消耗下降，且该抽象与架构无关，可移植到现有技能运行环境。

Large language models (LLMs) increasingly interact with external environments and accumulate substantial behavioral experience, yet existing agent benchmarks largely evaluate them as fixed policies. It therefore remains unclear whether an …

Accurate daily predictions of cold hardiness in woody plants are critical in regions where freezing temperatures can damage dormant buds and reduce seasonal yield. Existing biophysical, hybrid, and deep learning models have shown high pred…

Class-incremental learning (CIL) requires a model to incrementally learn tasks that contain new classes without accessing earlier training data while preserving the ability to recognize all seen classes. Recently, pretrained-model-based ap…

论文提出"记忆增强压缩"（Memory-Augmented Compression）方法，将可复用推理从生成阶段转移到提示词中，无需训练即可降低思维链成本。该方法将已解示例蒸馏为可复用推理记忆，按查询检索并注入，使部分计算从自回归解码转移到更并行的预填充阶段。

今天，我们分享了关于 Solaris 的新研究，这是我们首个界面世界模型（Interface World Model）。 Solaris 是一种新型操作系统，能够实时逐帧生成交互式界面，无需任何代码。我们发现，在生成新界面时，Solaris 在结构相似性和信息保留方面均优于前沿大语言模型。点击下方链接了解更多信息并申请早期访问权限。

UC Berkeley 联合多机构发布 Vero 基准，首个要求智能体在仓库级同时写实现与形式化证明，含 43 个 Lean 4 多模块实例、743 个 API 和 2，705 条形式化规范。

Google 与 NASA JPL 在 PNAS 发表 MAPL-EMIT，一个基于 Swin-S vision transformer 的深度学习框架，用 EMIT 高光谱卫星数据自动完成甲烷羽流的检测、定量与源头定位。

Google 的 WikiSkill 论文提出一个框架，让 AI 智能体将运行经验自动持久化到知识库（wiki）中，并据此持续调优技能以供复用。该方法与模型无关，进化后的技能可迁移至更小模型，有时甚至超越更大模型的表现。论文强调构建跨公司与项目的持久知识库以升级技能。

一篇论文提出FAR框架，将AI数学从"解题"转向"选题"，在文献中搜索开放问题并大规模尝试，再筛选出最值得专家关注的结果。在组合学试点中，该流程将51，110篇论文缩减至4，717个开放猜想，产出1，050个新解法，598个通过自动评判，77个推荐专家评审。人工抽查15项成果全部正确。

字节跳动新论文提出 Chain-of-Experience 方法，在上下文中保留先前尝试与反馈而非整理成简洁摘要。在 6 个数学、编程和知识基准上，自我反馈平均得分 71.0%，高于无反馈迭代求解的 66.8%；使用正确性或执行器反馈可达 79.3%。

清华NLP团队（OpenBMB成员）推出九歌推敲（Jiuge-Tuiqiao），入选EMNLP 2026系统演示。该工具将古诗创作建模为字符矩阵与锁定矩阵，用户锁定字词后模型在剩余位置硬约束生成，并附28，739条搭配、PPL排序名句等四类证据供用户决策。在50行完形任务中，结合名句证据将Hit@1从12%提升至60%；80次生成请求中格律与押韵合规率达100%。

亚马逊新论文表明，KV缓存策略不仅是推理优化，还能改变模型所需的训练方式。若模型推理时会遗忘部分上下文，训练时也应模拟这种遗忘--将微调与KV缓存策略匹配，可防止长上下文失效。在128k token测试中，全注意力微调模型在稀疏推理下常产生冗长无意义回答，而匹配缓存策略微调的模型能正常作答并停止；该方法支持任意缓存策略，可在40 GB A100上计算4B模型梯度。

This paper presents Murano, an open source framework for designing, running, and reproducing mechanistic interpretability studies of large language models, intended for researchers across disciplines. These studies often combine loading, r…

Large language model-based multi-agent systems are evolving from fixed interaction topologies toward dynamically orchestrated Agent Swarms. However, existing benchmarks are still largely based on single-agent or general-purpose agent tasks…

Antarctic sea ice concentration (SIC) forecasting is an important yet challenging task due to the coexistence of complex spatial structure, long-range temporal dependencies, and strong seasonal variability. Conventional convolution-based m…

Multimodal Large Language Models (MLLMs) are increasingly deployed in multi-image scenarios requiring complex reasoning across visual contexts. However, current MLLMs remain fundamentally limited by object hallucination-generating plausibl…

Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs (KGs). Embedding models are trained relying on both positive and negative samples of triples. However,…

LLM agents need to sustain goal-consistent reasoning across long multi-turn interactions under strict resource constraints. However, as the multi-turn context accumulates, it can destabilize the underlying LLM's internal representation of …

Removing a specific individual's information from multimodal large language models (MLLMs) is often needed after deployment, but existing methods rely on a retain set, which is hardest to obtain at that point, and rebuilding it recreates t…