论文

提出 HPSE，构建一种混合 rollout，能够在 student 自身轨迹中 coverage 缺失的精确位置补齐缺失事实，同时在其他位置保持 on-policy。HPSE is proposed, which builds a hybrid rollout that steps in to place missing facts onto the student's own trajectory precisely where its coverage fails, while staying on-policy elsewhere.

Gambit 通过周期性剪除低潜力轨迹并立即从高质量前缀分支，利用轻量 scorer 探测 hidden state，将算力动态集中到最有潜力的推理路径上，同时保持持续的高硬件利用率。By periodically pruning unpromising trajectories and immediately branching from high-quality prefixes, Gambit dynamically concentrates compute onto the most promising reasoning traces via a light-weight scorer probing hidden states while maintaining continuous high hardware utilization.

提出 Intermittent Low-Frequency Lockout（ILL），一种基于通用波形模板在黑盒设置下评估该风险的不可听 red teaming 方法；同时提出 Distributional Requery Guard（DRG），用于缓解该风险。This paper proposes Intermittent Low-Frequency Lockout (ILL), an inaudible red teaming method that evaluates this risk using a universal waveform template in a black box setting and proposes Distributional Requery Guard (DRG), an inaudible red teaming method to mitigate this risk.

一种具有固定大小记忆的循环 Transformer 架构，可泛化 sliding-window attention，同时在训练期间保持并行性，并在验证 loss 和下游预训练 benchmark 上优于 sliding-window 和 latent recurrent Transformer 基线。A recurrent Transformer architecture with fixed-size memory that generalizes sliding-window attention while remaining parallelizable during training and improves validation loss and downstream pretraining benchmarks over sliding-window and latent recurrent transformer baselines.

本文提出一种权重裁剪的替代方案：对 critic 相对于其输入的梯度范数施加惩罚。其性能优于标准 WGAN，能以几乎无需调参的方式稳定训练多种 GAN 架构。This work proposes an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input, which performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning.

当将监督规模扩展到 680,000 小时的多语言、多任务数据时，所得到的模型在标准 benchmark 上泛化良好，在 zero-shot transfer 设置下常可与此前全监督方法的结果相当，且无需任何微调。When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results but in a zero-shot transfer setting without the need for any fine-tuning.

建立一个可复现的选择性 3D 定位框架，并指出跨视角对应（cross-view correspondence）是主要操作瓶颈。The study establishes a reproducible framework for selective 3D localization and identifies cross-view correspondence as the dominant operational bottleneck.

首个同时使用 LLM 推理和 tag-aware 翻译来明确处理并评估英罗 MT 中性别偏见的方法。This is the first method to explicitly address and evaluate gender bias in English-Romanian MT using both LLM inference and tag-aware translation.

对八个法律 RAG 系统在 GDPR 和某国国内民法两个法律语料上的幻觉行为进行细粒度分析，发现含有错误假设的 false-premise 问题在人工编写问题上产生高幻觉率。A fine-grained analysis of hallucination behavior in eight legal RAG systems across two legal corpora, the GDPR and a national civil law, finds that false-premise questions, containing incorrect assumptions that must be rejected, produce high hallucination rates on the manually-drafted questions.

提出 Second Thought，一种免训练的推理框架，在每个 Thought 阶段结束时立即 fork 四个辅助分支，与主循环并发解码，并在环境 observation 到达时将生成的 thought 合并回去。This work proposes Second Thought, a training-free inference framework that forks four auxiliary branches the instant each Thought phase concludes, decodes them concurrently with the main loop, and merges the generated thoughts back when the environment observation arrives.

提出 Latent On-Policy Self-Distillation（LOPD），不再提出另一种手工设计、附带新形式 privileged context 的 OPSD 变体，而是让 teacher 的 privileged context 本身可从经验端到端学习。This work introduces Latent On-Policy Self-Distillation (LOPD), which, rather than proposing another hand-crafted OPSD variant with a newly prescribed form of privileged context, makes the teacher's privileged context itself learnable end-to-end from experience.

MMDiff 是一种多模态 model-diffing 框架，训练多模态 SAE 并将其转化为特征级接口，用于发现和控制多模态行为；研究表明多模态 SAE 不仅可作为可解释性工具，还可作为审计、引导和控制 MLLM 行为的机制，以实现更安全、更具能力的生成。MMDiff, a multimodal model-diffing framework that trains multimodal SAEs and turns them into feature-level interfaces for discovering and controlling multimodal behavior, shows that multimodal SAEs can serve not only as interpretability tools, but as mechanisms for auditing, steering, and controlling MLLMs behavior toward safer and more capable generations.

提出 Self-Supervised Visual On-Policy Distillation（S²VOPD），一种简单有效的方法，通过非对称增强视图构建 on-policy 学习信号，系统地探索了视觉增强的广阔设计空间，并发现非对称性至关重要。Self-Supervised Visual On-Policy Distillation (S$^2$VOPD), a simple yet effective method that constructs on-policy learning signals from asymmetric augmented views, systematically explores a broad design space of visual augmentations and uncover that asymmetry matters.

引入 student reference KL 损失并 mask 特殊终止 token 的 advantage，以缓解生成过长和频繁截断的问题；在 HLE 和 HiPhO 等科学 benchmark 上取得改进，表明 OPD 传递的推理能力可泛化至数学训练领域之外。This work introduces a student reference KL loss and mask the advantages of special termination tokens to mitigate the problem of excessive generation length and frequent truncation, and improves on science benchmarks such as HLE and HiPhO, suggesting that OPD transfers reasoning capabilities that generalize beyond the mathematical training domain.

提出 Mimir v1，一个基于 Hierarchical Reasoning Model（HRM）架构的 10 亿参数语言模型，从头训练，在英语上具有高度竞争力，并仅使用合规的后训练数据在丹麦语上创下新的 SOTA。Mimir v1 is introduced, a 1-billion-parameter language model based on the Hierarchical Reasoning Model (HRM) architecture that is trained from scratch and delivers highly competitive performance for English and sets a new state of the art for Danish using only permissible post-training data.

提出 LITTLECURRICULUM，一个为美国小学课程量身定制的 88B token 精选预训练语料，并由此训练得到 LITTLELEARNER；该模型具备开放式评估所需的语言能力，同时其知识和能力边界清晰可映射到可解释的课程指南。This work introduces LITTLECURRICULUM, a curated 88B-token pretraining corpus tailored to U.S. elementary school material, which yields LITTLELEARNER, a model with sufficient language competence for open-ended evaluation, yet with clear knowledge and capability boundaries mapped to interpretable curriculum guidelines.

提出 UniProbe，一个轻量、统一、可学习的检测器，通过单次前向传播建模冻结 LVLM 的异构计算 trace，在 token 级和物体级幻觉检测上达到 SOTA。UniProbe is introduced, a lightweight, unified, learnable detector that models a frozen LVLM's heterogeneous computational trace from a single forward pass and achieves state-of-the-art token-level and object-hallucination detection.

通过跨 46 个任务（涵盖四个认知领域）的电路分析，发现 LLM 发展出与人类大脑相似的模块化架构：在人类大脑中依赖同一网络的任务会在 LLM 中招募重叠的神经元，而依赖不同网络的任务则招募不同的神经元。Using circuit analyses across N=46 tasks spanning four cognitive domains, it is found that Large Language Models develop a modular architecture that mirrors the human brain: tasks drawing on the same network in humans recruit overlapping neurons in LLMs, whereas tasks drawing on different networks recruit distinct neurons.

FreeToken 是一个 edge-native 的 MoE serving 系统，将个人机器视为一个统一、弹性的推理平台而非小型 GPU，把开放权重转化为可部署的本地软件，使用户已有的机器成为运行前沿规模智能的实用平台。FreeToken is an edge-native MoE serving system that treats a personal machine not as a small GPU, but as a unified, elastic inference platform, turning open weights into deployable local software, making the machines users already own a practical platform for frontier-scale intelligence.

将文档结构形式化为多模态超图（Multimodal Hypergraph），以超边作为统一语义容器来封装跨文本、图像和表格的多路关联，超越点对点建模，并引入 Anchor-driven Incremental Refinement 机制。This paper formalizes the document structure as a Multimodal Hypergraph, utilizing hyperedges as unified semantic containers to encapsulate multi-way associations across text, images, and tables, thereby transcending point-to-point modeling and introducing an Anchor-driven Incremental Refinement mechanism.

提出 DSPrompt，一种 Dynamic Soft Prompt 防御框架，无需修改检索 pipeline，直接重塑 retriever 的 embedding 语义，并以极低的计算成本 consistently 优于现有防御基线。DSPrompt is proposed, a Dynamic Soft Prompt defense framework that directly reshapes the retriever's embedding semantics, without modifying the retrieval pipeline, and is consistently outperforming existing defense baselines at a fraction of their computational cost.

提出 Intent-Guided Decoding (IGD)，一个根据用户意图在检索上下文和参数化记忆之间进行仲裁的框架，显著提升了 RAG 中的事实恢复能力。Intent-Guided Decoding (IGD) is proposed, a framework that arbitrates between retrieved context and parametric memory according to user intent and substantially improves factual recovery in RAG.

AnyTalk 能够在多样化的人脸网格和 blendshape 配置下生成唇形同步动画，显著减少人工工作和数据需求；并通过将 AnyTalk 蒸馏为精简网络 $\text{AnyTalk}_{RT}$ 来提升可用性，从而实现实时性能。AnyTalk enables lip-synced animations across diverse face meshes and blendshape configurations, significantly reducing manual effort and data requirements and enhances usability by distilling AnyTalk into a streamlined network, $\text{AnyTalk}_{RT}$, thereby enabling real-time performance.

GRNEdit 是一个轻量级的两阶段指令驱动通用视频编辑框架，性能优于多个 14B 开源编辑器，同时其 8B 模型与领先的开源编辑器表现相当。GRNEdit, a lightweight two-stage framework for instruction-based general video editing that outperforms multiple 14B open-source editors, while the 8B model performs on par with leading open-source editors.

提出一个即插即用的 2D Motion Interface，使预训练于 3D 的 MoLM 能够在不修改或微调原始模型的情况下接受 2D 运动输入，并在 2D 运动任务上优于从头训练 MoLM。A plug-and-play 2D Motion Interface is introduced that enables 3D-pretrained MoLMs to accept 2D motion inputs without modifying or fine-tuning the original models and outperforms training MoLMs from scratch on 2D motions.

提出 Large Discovery Model (LDM)，一种经验驱动的循环架构，将生成模型与贝叶斯非参数奖励代理模型耦合，产生一种感知不确定性的价值，用于引导候选的生成、精炼与选择。This work introduces the Large Discovery Model (LDM), an empirically grounded recurrent architecture that couples a generative model with a Bayesian non-parametric reward surrogate model, yielding an uncertainty-aware value that guides candidate generation, refinement, and selection.

WorldRover 将长视野世界探索转化为可扩展的数据生成问题，为需要在可探索世界中构建、维护并重访一致表征的模型提供监督信号。WorldRover turns long-horizon world exploration into a scalable data-generation problem, providing supervision for models that must build, maintain, and revisit coherent representations of an explorable world.