论文

记忆是长时程 LLM 智能体的核心，但现有记忆系统主要保存交互内容，而未建模哪些智能体在何种条件下可信。这一局限在多智能体系统中尤为关键，因为中心模型可能无法直接验证来自对等方、看似合理或相关的响应。我们提出 Σ-Mem，一种在线可靠性记忆，记录单个对等方的历史能力证据以及跨对等集的对等关系证据。两种证据均以实对称状态形式维护，并基于后Memory is central to long-horizon LLM agents, yet existing memory systems primarily preserve interaction content rather than modeling which agents can be trusted and under what conditions. This limitation is particularly important in multi-agent systems, where a central model may be unable to directly verify plausible or correlated peer responses. We introduce Σ-Mem, an online reliability memory that records historical competence evidence for individual peers and peer relationship evidence across the peer set. Both forms of evidence are maintained as real symmetric states and updated from post

对 Fairness Pruning 的实证评估表明，群体偏置处理与模型能力运行在可分离的电路上，奠定了从盲目零化向定向行为调制过渡的方法论基础。Empirical evaluation of Fairness Pruning empirically confirm that demographic bias processing and model capabilities operate on dissociable circuits, establishing the methodological foundations for transitioning from blind zeroing toward directional behavior modulation.

提出 MisKnow-Agent，一个受控评测框架，通过可控的权威线索与来源风格构造支撑人工审核结论的任务文档，并采用报告级 false-conclusion 采纳率（仅统计认可错误结论的报告），基于三种 backbone LLM 评估 DeerFlow 与 WebThinker。MisKnow-Agent is introduced, a controlled evaluation framework that constructs task-specific documents supporting manually audited false conclusions with controlled authority cues and source styles that evaluates DeerFlow and WebThinker with three backbone LLMs using a report-level false-conclusion adoption rate that counts only reports endorsing the false conclusion.

提出 OmniScope，一个无需训练的 token 压缩框架，以 query 作为跨模态共享的语义锚点，并对音频与视频分别估计相关性；由此给出 OmniLLM 推理的简单设计原则：跨模态共享 query，但不共享显著性估计。This work proposes OmniScope, a training-free token compression framework that uses the query as a shared semantic anchor while estimating relevance separately for audio and video, and suggests a simple design principle for OmniLLM inference: share the query across modalities, but not the salience estimates.

研究表明，领域特定的 orchestration 可使缺乏专业团队的实验室也能实现可审计的预处理，并为其他科学领域的 AI agent 提供了可借鉴的设计原则。The results indicate that domain-specific orchestration can bring auditable preprocessing within reach of laboratories lacking dedicated expertise, illustrating design principles applicable to AI agents in other scientific domains.

本工作识别出记忆溯源洗白现象：基于LLM的记忆整合过程中，外部观察可能被改写为看似用户历史或工作流支持的内容，在保留动作触发的同时，抹去本应限制其权威性的低可信度来源。This work identifies memory provenance laundering: during LLM-based memory consolidation, an external observation may be rewritten as apparent user history or workflow support, preserving an action trigger while erasing the low-trust source that should limit its authority.

多维评估-验证奖励(EVR)将评估分解为独立视觉准则；针对每个准则，MLLM Evaluator生成多个候选假设，Verifier在具体视觉证据中grounding每个claim以接受或拒绝，产生可靠且细粒度的奖励信号。A Multi-dimensional Evaluation-Verification Reward (EVR) decomposes evaluation into distinct visual criteria; for each criterion, an MLLM Evaluator generates multiple candidate hypotheses, and a Verifier grounds each claim in concrete visual evidence to accept or reject it, producing reliable and fine-grained reward signals.

提出QWorld，用分位数-分位数匹配目标替代EP，直接将投影后的潜在样本与秩匹配的高斯分位数对齐，从而在尾部保持有效的修正梯度。QWorld is proposed, which replaces EP with a quantile-quantile matching objective that directly aligns projected latent samples with rank-matched Gaussian quantiles, thereby maintaining effective corrective gradients in the tails.

提出Criterion-Distilled Policy Optimization (CriPO)，通过on-policy自蒸馏增强基于rubric的RL，并采用反事实自教师定位负优势rollout中与准则相关的token，将其token级优势翻转为正值，保留本将被抑制的有用模式。Criterion-Distilled Policy Optimization (CriPO) is proposed, which enhances rubric-based RL via on-policy self-distillation and employs a counterfactual self-teacher to locate criterion-relevant tokens in negative-advantage rollouts and flips their token-level advantages to positive values, preserving useful patterns that would otherwise be suppressed.

提出SA，一种Stable Advantage Fusion框架，避免熵崩塌，在所有六个模型-领域设定上持续优于固定系数的GRPO+OPD融合，聚合得分提升0.70%，训练更稳定。SA is proposed, a Stable Advantage Fusion framework that avoids entropy collapse and consistently outperforms fixed-coefficient GRPO+OPD fusion, improving the aggregate score by 0.70% across all six model-domain settings while achieving more stable training.

研究发现privileged shifts无法给出可靠的答案对齐方向，其幅度主要反映反事实敏感性而非token级学习价值；提出Counterfactual Sensitivity Credit Reallocation (CSCR)，作为GRPO的简单扩展，降低高敏感token的credit并对token级优势重新归一化，同时保留原始credit预算与verifier确定的方向。These findings show that privileged shifts fail to provide reliable answer-aligned directions, while their magnitudes primarily reflect counterfactual sensitivity rather than token-level learning value, and propose Counterfactual Sensitivity Credit Reallocation (CSCR), a simple extension of GRPO that reduces credit for highly sensitive tokens and renormalizes token-level advantages to preserve both the original credit budget and verifier-determined direction.

世界模型为规划和行动提供了预测性基础，但现有建模方式仅回答物理层面的问题：它是什么/在哪里，以及将如何演变。然而，人类行为由隐藏的心理状态驱动（一个人相信什么、想要什么、意图做什么、感受如何，以及认为在社会上何为可接受），因此仅追踪物理场景而忽略每个智能体所知与所信内容的模型，会对看起来正确的场景预测出错误的行动。我们将心理世界建模（MWM）形式化为一个通用理论框架，将心理变量作为世界模型的核心组成部分。World models enable a predictive substrate for planning and action, yet existing formulations merely answer a physical question: what/where it is, and how will it evolve. Human behavior, however, is driven by hidden mental state (what a person believes, wants, intends, feels, and considers socially permissible), so a model that tracks the physical scene but not what each agent knows and believes about it predicts the wrong action for the right-looking scene. We formulate Mental World Modeling (MWM), a generic theoretical framework that makes mental variables core components of a world model ra

提出EMBL AI Librarian，一个升级Europe PMC接口的知识层，面向AI agent，提升多项任务表现：文献综合、claim验证、开放域问答，以及下游生物学任务如protocol问题与序列操作。EMBL AI Librarian is introduced, a knowledge layer that upgrades the Europe PMC interface for AI agents that improves performance across a range of tasks: literature synthesis, claim verification, open-domain question answering, and downstream biology tasks such as protocol questions and sequence manipulation.

训练后对齐往往较浅，会在微调中被侵蚀。而中训练干预能否在干净隔离于训练后的情况下产生持久对齐，此前未经检验。我们通过宪法式中训练来测试：在 120B 规模上，插入基于原则与价值观的内容，与仅做回放的对照组进行对比。我们基于 Anthropic 的 Constitution 构建了 394M token 的宪法语料，并采用 2×2 析因设计（课程顺序 × 审慎推理），形成四种宪法式中训练条件与一组对照，随后在自生成与既有...Post-training alignment is often shallow, eroding under fine-tuning. Whether midtraining interventions, cleanly isolated from post-training, can produce durable alignment remains untested. We test this via constitutional midtraining: inserting principled, values-based content into midtraining against a replay-only control at 120B scale. Our 394M-token constitutional corpus, built from Anthropic's Constitution, uses a 2x2 factorial design (curriculum ordering x deliberative reasoning) to produce four constitutionally midtrained conditions plus a control, evaluated on self-generated and establis

UEmbed (Unified Embedding)是一种decoder-only多模态嵌入模型，在单次因果前向中同时产出稀疏词项与稠密表示，提供新范式：在单一模型中统一稠密与稀疏嵌入，并将稀疏检索扩展以统一文本与多模态输入。UEmbed (Unified Embedding), a decoder-only multimodal embedding model that produces both sparse lexical and dense representations in one causal forward pass, offers a new paradigm: it unifies dense and sparse embeddings in one model, while further extending sparse retrieval to unify text and multimodal inputs.

将长视野执行重新表述为任务状态管理问题，提出LongHorizon-Harness，在执行外部显式维护任务状态，并仅用从环境中独立验证的事实更新它。This work reformulate long-horizon execution as a task-state management problem and proposes LongHorizon-Harness, which maintains the task state explicitly outside execution and updates it only with facts independently verified from the environment.

本文将技能生成建模为序列编辑过程，把技能构建分解为可单独评估的编辑，并提出一种新颖的回滚奖励，通过在锚定查询上对比原始技能与编辑后技能的下游执行效果来评估每次编辑。This work forms skill generation as a sequential editing process that decomposes skill construction into individually evaluable edits, and introduces a novel rollback reward that evaluates each edit by comparing downstream execution under the original and edited skills on an anchored query.

本文提出 Motion Beyond Morphology（超越形态的运动）这一视角，旨在跨固定结构对应迁移运动，通过两阶段框架保留在不同目标形态间仍具有意义的动力学。This work introduces Motion Beyond Morphology, a perspective that seeks to transfer motion beyond fixed structural correspondence, by preserving dynamics that remain meaningful across different target morphologies by proposing a two-stage framework.

本文提出 DAPD，一种具有两级锚定的统一框架，可显著缓解特权错觉，在 Qwen3-4B 上以平均 +2.00 分优于 OPSD。DAPD is proposed, a unified framework with two levels of anchoring that significantly alleviates privilege illusion, outperforming OPSD on Qwen3-4B by +2.00 points on average across tasks.

本文提出 3DZip，一种三阶段 token 压缩框架：首先采用粗粒度体素化去除点级冗余，再通过 Determinantal Point Process 基于特征空间多样性选取锚点 token，最后在空间约束下融合剩余 token 以保持几何一致性。3DZip is proposed, a three-stage token compression framework that first applies coarse voxelization to remove point-level redundancy, then selects anchor tokens based on feature-space diversity via a Determinantal Point Process, and finally merges remaining tokens under spatial constraints to preserve geometric coherence.

本文提出 CADENA（西班牙语意为"链"），一种将 3D 网格重建为参数化 CAD 程序的模型，按顺序逐个生成操作序列，并在每一步将目标与当前预测几何进行对比。This work introduces CADENA (Spanish for"chain"), a model that reconstructs a 3D mesh as a parametric CAD program, growing its sequence of operations one at a time and comparing the target with the currently predicted geometry at every step.

本文提出 LeapTalk，一种通过单次前向实现稳定且实时说话头生成、可扩展至任意长视频的新颖框架，并引入音频驱动的无分类器引导机制，在极端步数缩减下保持细粒度唇形同步。LeapTalk is proposed, a novel framework that achieves stable and real-time talking-head generation with a single forward step, scaling to arbitrarily long videos, and an audio-driven classifier-free guidance mechanism to maintain fine-grained lip synchronization under extreme step reduction.

GradCuit（梯度穿越电路）在所选 Transformer 层、提示隐藏表示与生成续写之间插入可优化的潜变量，开启了鲁棒且可解释的测试时缩放新维度，使 LLM 调整其推理方式，而不仅仅是重新生成、采样或重排输出。GradCuit (gradient through circuit), which inserts optimizable latent states at a selected Transformer layer between the hidden representations of the prompt and the generated continuation, opens a new axis of robust and interpretable test-time scaling, where LLMs adapt how they reason rather than merely regenerate, sample, or rerank outputs.

提出 BERTopic，一种通过开发类内 TF-IDF 变体来提取一致性主题表示，从而扩展主题建模流程的主题模型BERTopic is presented, a topic model that extends the process of topic modeling by extracting coherent topic representation through the development of a class-based variation of TF-IDF.

提出一种从校正后的图像对中提取深度信息的方法，使用卷积神经网络在小图像块上学习相似性度量，并针对该任务考察了两种网络架构：一种面向速度优化，另一种面向精度优化This work presents a method for extracting depth information from a rectified image pair by learning a similarity measure on small image patches using a convolutional neural network and examines two network architectures for this task: one tuned for speed, the other for accuracy.

在训练过程中，Random Erasing 在图像中随机选择一个矩形区域并以随机值擦除其像素，在图像分类、目标检测与行人重识别任务中相较于强基线均带来稳定提升In training, Random Erasing randomly selects a rectangle region in an image and erases its pixels with random values and yields consistent improvement over strong baselines in image classification, object detection and person re-identification.

简化了 MoE 路由算法，设计出通信与计算成本更低的直观改进模型，并首次证明大型稀疏模型可以使用更低精度格式进行训练This work simplifies the MoE routing algorithm and design intuitive improved models with reduced communication and computational costs and shows large sparse models may be trained, for the first time, with lower precision formats.

Pointer sentinel-LSTM 模型在 Penn Treebank 上以远少于标准 softmax LSTM 的参数量达到 SOTA 语言建模性能，并开源了 WikiText 语料库The pointer sentinel-LSTM model achieves state of the art language modeling performance on the Penn Treebank while using far fewer parameters than a standard softmax LSTM and the freely available WikiText corpus is introduced.

在视觉问答与视觉定位任务上对多模态紧凑双线性池化（MCB）进行了广泛评测，结果一致表明 MCB 优于去掉 MCB 的消融版本This work extensively evaluates Multimodal Compact Bilinear pooling (MCB) on the visual question answering and grounding tasks and consistently shows the benefit of MCB over ablations without MCB.

文章论证了 Transformers 可作为医学图像分割任务的强大编码器，并通过与 U-Net 结合，恢复了局部空间信息以增强更精细的细节。It is argued that Transformers can serve as strong encoders for medical image segmentation tasks, with the combination of U-Net to enhance finer details by recovering localized spatial information.

文章提出了 SegFormer，一个简单高效且强大的语义分割框架，将 Transformers 与轻量级 MLP 解码器统一，并在 Cityscapes-C 上展示了出色的零样本鲁棒性。SegFormer is presented, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders and shows excellent zero-shot robustness on Cityscapes-C.

分析表明，VisualBERT 能够在无任何显式监督的情况下将语言元素对应到图像区域，甚至对句法关系敏感，例如能够跟踪动词与其论元对应图像区域之间的关联。Analysis demonstrates that VisualBERT can ground elements of language to image regions without any explicit supervision and is even sensitive to syntactic relationships, tracking, for example, associations between verbs and image regions corresponding to their arguments.

文章介绍了一种自监督视觉表征模型 BEiT（Bidirectional Encoder representation from Image Transformers），在图像分类和语义分割上的结果表明，该模型取得了与先前预训练方法相当的竞争性结果。A self-supervised vision representation model BEiT, which stands for Bidirectional Encoder representation from Image Transformers, is introduced, and results on image classification and semantic segmentation show that the model achieves competitive results with previous pre-training methods.

在输入和输出直接进行 4 倍下采样与上采样的设定下，实验表明，基于纯 Transformer 的 U 形编码器-解码器网络优于完全卷积或 Transformer 与卷积相结合的方法。Under the direct down-sampling and up-sampled of the inputs and outputs by 4x, experiments demonstrate that the pure Transformer-based U-shaped Encoder-Decoder network outperforms those methods with full Convolution or the combination of transformer and convolution.

BLIP 通过引导式 caption 方式有效利用含噪网络数据，由 captioner 生成合成 caption，并由 filter 去除噪声样本；在以零样本方式直接迁移到视频-语言任务时，展现出强大的泛化能力。BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones, and demonstrates strong generalization ability when directly transferred to video-language tasks in a zero-shot manner.

本文提出了首个用于睡眠阶段分类的深度学习方法，无需计算频谱图或提取手工特征即可端到端学习，利用了全部多变量多模态 PSG 信号（EEG、EMG、EOG），并能利用每个 30 秒窗口数据的时序上下文。This work introduces here the first deep learning approach for sleep stage classification that learns end-to-end without computing spectrograms or extracting handcrafted features, that exploits all multivariate and multimodal polysomnography (PSG) signals (EEG, EMG, and EOG), and that can exploit the temporal context of each 30-s window of data.