论文

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.

通过在 post-training 与 in-context learning 中注入新知识的一系列实验，展示了该沙箱的实用性：这些方法让 LittleLearner 更好地利用已有知识，但并未提升其 out-of-scope 能力。The sandbox's utility is illustrated in a first suite of experiments on injecting new knowledge through post-training and in-context learning, which let LittleLearner better utilize existing knowledge, but do not raise out-of-scope capabilities.

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.

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.