论文

Pointmaps 在保留预训练 2D VLA 所需 H × W 稠密网格的同时，提供机器人坐标系下的 3D 几何信息，能以极小的架构改动集成到现有 VLA 中，并提升 pi0.5 与 SmolVLA 的性能，优于代表性的相机视点和 3D 感知基线。Pointmaps provide robot-frame 3D geometry while preserving the dense H x W grid expected by pretrained 2D VLAs, so they integrate into existing VLAs with minimal architectural change and improve both pi0.5 and SmolVLA and outperform representative camera-viewpoint and 3D-aware baselines.

本文提出自验证推理器 SVR-R1，一种多轮强化学习框架，将模型自身的验证转化为多模态推理的学习信号，提供了一种简洁而有效的多模态推理自举方案。Self-Verified Reasoner (SVR-R1), a multi-turn RL framework that turns a model's own verification into a learning signal for multimodal reasoning, is introduced, offering a simple yet effective recipe for bootstrapping multimodal reasoning.

Cross-encoder 在 RAG 流水线中具有较高的重排序准确率，但推理成本随序列长度呈二次增长，难以实时部署。本文通过两阶段流水线解决该问题：使用 Unsloth 框架与 LoRA 适配器，在自定义的查询-文档相关性数据集上对 LLaMA 3 (8B) 进行监督微调，随后进行 4-bit 量化以提升推理效率。该模型可替换双路检索 RAG 流水线中结合 BM25 与稠密向量检索的 cross-encoder，并在特定领域问答……Cross-encoders achieve high reranking accuracy in Retrieval-Augmented Generation (RAG) pipelines but impose quadratic inference costs that limit real-time deployment. We address this by fine-tuning LLaMA 3 (8B) as a drop-in reranker using a two-stage pipeline: supervised fine-tuning on a custom query-document relevance dataset via the Unsloth framework with LoRA adapters, followed by 4-bit quantization for efficient inference. The resulting model replaces the cross-encoder in a dual-retriever RAG pipeline combining BM25 and dense vector search. Evaluated on a domain-specific question-answering

本文从参考图像中识别低秩背景特征子空间，并以闭式方式将参考与查询特征投影到其正交补空间，从而获得更清晰的语义匹配，并在 PACO-Part、FSS-1000 以及 ISIC2018 等跨域数据集的无训练方法中达到新的 SOTA，表明显式去除背景子空间是一次性定位中极为有效的原则。This paper identifies the low-rank background feature subspace from the reference image and project the reference and query features onto its orthogonal complement in closed form, yielding cleaner semantic matching, and establishes a new state of the art among training-free methods on PACO-Part, FSS-1000, and cross-domain datasets such as ISIC2018, demonstrating that explicit background subspace removal is a highly effective principle for one-shot localization.

本文设计了一种自适应随机谈判策略，同时保证行为差分隐私、报价序列的几乎处处收敛以及较高的谈判效用，并证明在获得强隐私保证的同时不会带来显著的性能损失。This paper designs an adaptive stochastic negotiation policy that jointly guarantees behavioral differential privacy, almost-sure convergence of the offer sequence, and high negotiation utility, and demonstrates that strong privacy guarantees can be achieved without significant loss of performance.

HOMIE 提出了一种更优的 MLLM 集成策略，可在不损害文本编码器可控性或引入昂贵重新对齐的前提下，提取参考级关系知识，并在 self-attention 中引入全局多模态引导，使 MLLM 派生的语义特征与 VAE token 更好对齐。HOMIE proposes a better MLLM integration strategy to extract knowledge of reference-level relationships without compromising the controllability of text encoders or incurring costly re-alignment, and introduces global multimodal guidance within self-attention to better align MLLM-derived semantic features with VAE tokens.

本文提出 SWE-Pruner Pro，在 Agent 内部直接对工具输出进行剪枝，通过一个小型 head 将 Agent 自身的内部表征转化为针对每一行的 keep-or-prune 标签，并采用以每段工具输出行数为键的长度感知嵌入。SWE-Pruner Pro is proposed, which prunes tool outputs directly inside the agent, with a small head turns the agent's own internal representations into a keep-or-prune label for each line, with a length-aware embedding keyed to each tool output's line count.

本文提出 Token-Level Off-Policy Labeling (TOPL)，一种将后训练重构为 token 级正确性预测任务的离线策略训练范式。其核心思路是：通过训练模型区分响应中的好 token 与坏 token，自然引导模型生成好 token，同时避免直接训练模型生成离线策略 token 所带来的缺陷。在文档摘要任务上的实验表明，TOPL 在 11 个数据集上针对多种序列级与 token 级方法实现了强大的分布外泛化能力。We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness prediction task. Our key intuition is that by training the model to distinguish good and bad tokens in a response, we naturally guide the model towards generating good tokens, while avoiding the pitfalls that come with directly training the model to generate off-policy tokens. Experiments on document summarization tasks show that TOPL achieves strong out-of-distribution generalization across 11 datasets against a diverse set of sequence-level and token-lev

本文提出 FlashRT，一种 Agent Harness，引导编码 Agent 将开发者编写的简易参考实现提升为优化的多 GPU 部署，并可灵活权衡时延与吞吐量等目标指标，证明在专家优化尚不成熟的平台上，由 Agent 驱动的优化具有更高的可扩展性。FlashRT is presented, an agent harness that guides coding agents to lift simple developer-written reference implementations into optimized multi-GPU deployments that flexibly weigh target metrics like latency and throughput, demonstrating that agent-driven optimization can be more scalable on platforms with less mature expert optimization.

在七个基准上，TimeLens2-2B 在所有基准上均优于规模相当的所有基线，4B 和 8B 变体则取得了 SOTA 性能，超越了参数量高达 397B 的开源模型。Across seven benchmarks, TimeLens2-2B outperforms all size-matched baselines on every benchmark, while the 4B and 8B variants achieve state-of-the-art performance, surpassing open-source models with up to 397B parameters.

实验表明，EvolvingWorld 能够通过有效维持持久且一致的角色与世界发展，提升长程模拟能力。Experiments show that EvolvingWorld can improve long-horizon simulation by effectively maintaining persistent, coherent character and world development.

大量实验表明，Distilled RL 在 pass@1 和 pass@k 上均显著优于标准 RL 和 OPD，并能将教师模型中此前无法获得的知识有效迁移至学生模型。Extensive experiments show that Distilled RL substantially outperforms standard RL and OPD in terms of both pass@1 and pass@k, and can effectively transfer previously unavailable knowledge from a teacher model to a student model.

提出了ReflectWorld-MM，一个面向开放视频流的以实体为中心的多模态记忆系统，在六个长视频和终身记忆基准测试上均达到最优准确率，超越了强记忆Agent和前沿模型。ReflectWorld-MM is proposed, an entity-oriented multimodal memory system for open-ended video streams that achieves the best accuracy on all six long-video and lifelong-memory benchmarks, outperforming strong memory agents and a frontier model.

实证结果表明，分层防御栈对大多数攻击单元有效，但仍存在一小部分残余攻击面在操作系统层面本质上不可区分，这表明针对新出现的自我状态攻击类别，需要重新审视操作系统级防御。The empirical results show that a layered defense stack is effective on most attack cells while a small residual attack surface remains structurally indistinguishable at the OS level, suggesting that against the newly established class of self-state attacks, OS-level defense needs to be reconsidered.

该工作将两步方法的遗憾分解为候选生成遗憾和候选内选择遗憾，并利用最近邻估计器和Transformer的预测误差保证对后者进行了界。This work decomposes the regret of the two-step method into candidate-generation regret and within-candidate choice regret, and bound the latter using prediction-error guarantees for nearest-neighbor estimators and transformers.

提出了ShotPlan，一个基于视频扩散基础模型构建的、用于显式多镜头电影级视频生成的框架，显著优于现有的电影级视频生成方法，提供更灵活的镜头管理和更强的跨镜头一致性。ShotPlan is proposed, a framework for explicit multi-shot cinematic video generation built upon a video diffusion foundation model that significantly outperforms existing cinematic video generation methods, offering more flexible shot management and stronger inter-shot consistency.

OCT-Bench能够对MLLM进行全面且细粒度的评估，为识别能力瓶颈和推进临床可信的OCT理解奠定基础。OCT-Bench enables comprehensive and fine-grained evaluation of MLLMs, providing a foundation for identifying capability bottlenecks and advancing clinically grounded OCT understanding.

这些结果识别出关键决策支持的遗漏是 Qwen 主设定中的因果机制，并在多个模型族中得到印证，揭示了支持、损失和解码层面干预之间的克制-能力权衡关系。These results identify decision-critical support omission as a causal mechanism in the primary Qwen setting and support it across model families, tracing a restraint-capability trade-off among support-, loss-, and decoding-level interventions.

本文提出一个时间感知的音频 LLM，能够基于大规模合成监督（来自级联 pipeline）在长达 120 分钟的输入上回答带有显式时间戳的问题，并在短时长和长时长 benchmark 上取得强劲的时间定位准确率。This work presents a time-aware audio LLM that answers questions with explicit timestamps over up to 120 minutes of input using large-scale synthetic supervision from a cascaded pipeline and achieves strong temporal-grounding accuracy on short and long benchmarks.

本文针对中亚低资源语言构建鲁棒基础模型的挑战，提出 GigaAM Multilingual：一个基于 HuBERT 风格目标在 2M 小时音频上预训练的 Conformer encoder，并在预训练阶段引入 cluster 级的数据均衡策略，在微调阶段引入域感知采样方法，以缓解头部语言主导问题。This work addresses the challenge of building robust foundation models for underrepresented Central Asian languages by presenting GigaAM Multilingual, a Conformer encoder pre-trained on 2M hours of audio using a HuBERT-style objective, and introduces a cluster-level data balancing strategy during pre-training and a domain-aware sampling method during fine-tuning to mitigate head-language dominance.

本文提出 DeepSearch-Evolve，一个面向 web agent 的自蒸馏框架，基于 DeepSearch-World——一个具备可复现搜索与页面读取工具的确定性、可验证环境——从而实现长程 web agent 的可扩展自演化。DeepSearch-Evolve is presented, a self-distillation framework for web agents built on DeepSearch-World, a deterministic and verifiable environment with reproducible search and page-reading tools that enables scalable self-evolution for long-horizon web agents.

本文研究了在采用极小卷积滤波器的架构下，卷积网络深度对大规模图像识别精度的影响，并表明将深度推进至 16-19 个权重层，可在先前 SOTA 配置基础上取得显著提升。This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.

在 YouCook2、CrossTask 等教学视频数据集上，基于该数据训练的文本-视频 embedding 在文本到视频检索与动作定位任务上达到了 SOTA 结果。It is demonstrated that a text-video embedding trained on this data leads to state-of-the-art results for text-to-video retrieval and action localization on instructional video datasets such as YouCook2 or CrossTask.

提出在通过跨模态注意力融合之前对齐图像与文本表示的对比损失（ALBEF），可实现更扎实的视觉-语言表征学习；并提出动量蒸馏，一种利用动量模型生成伪目标进行自训练的方法。A contrastive loss to ALign the image and text representations BEfore Fusing (ALBEF) them through cross-modal attention, which enables more grounded vision and language representation learning and proposes momentum distillation, a self-training method which learns from pseudo-targets produced by a momentum model.

本文提出 LLaVA：Large Language and Vision Assistant，一个端到端训练的大型多模态模型，将视觉编码器与 LLM 相结合用于通用视觉和语言理解；并引入 GPT-4 生成的视觉指令微调数据，模型与代码库已开源。This paper presents LLaVA: Large Language and Vision Assistant, an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding and introduces GPT-4 generated visual instruction tuning data, the model and code base publicly available.

m-RNN 模型直接对给定先前词语和图像条件下生成下一个词的概率分布建模，相较于直接优化排序目标函数进行检索的 SOTA 方法，取得了显著的性能提升。The m-RNN model directly models the probability distribution of generating a word given previous words and an image, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval.

分析表明，借助 LLM，美国约 15% 的工作任务可在保持同等质量的前提下显著提速完成，意味着 LLM 驱动的软件将对底层模型经济影响的规模化产生实质性作用。The analysis suggests that, with access to an LLM, about 15% of all worker tasks in the US could be completed significantly faster at the same level of quality, implying that LLM-powered software will have a substantial effect on scaling the economic impacts of the underlying models.

提出极简的 VLP 模型 Vision-and-Language Transformer (ViLT)，其一体化设计将视觉输入处理大幅简化为与文本输入相同的无卷积方式；ViLT 比此前的 VLP 模型快达数十倍，同时下游任务性能具有竞争力甚至更优。A minimal VLP model, Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically simplified to just the same convolution-free manner that the authors process textual inputs, showing that ViLT is up to tens of times faster than previous VLP models, yet with competitive or better downstream task performance.

Contrastive Captioner (CoCa) 采用极简设计，对图文编码器-解码器基础模型联合使用对比损失与字幕损失进行预训练，从而兼具 CLIP 等对比方法与 SimVLM 等生成方法的能力。Contrastive Captioner (CoCa), a minimalist design to pretrain an image-text encoder-decoder foundation model jointly with contrastive loss and captioning loss, thereby subsuming model capabilities from contrastive approaches like CLIP and generative methods like SimVLM.

本文提出 MiniGPT-4，通过一个投影层将冻结的视觉编码器与冻结的先进 LLM Vicuna 对齐，发现将视觉特征与先进大语言模型恰当对齐可获得类似 GPT-4 所展现的多种先进多模态能力。MiniGPT-4 is presented, which aligns a frozen visual encoder with a frozen advanced LLM, Vicuna, using one projection layer to uncovers that properly aligning the visual features with an advanced large language model can possess numerous advanced multi-modal abilities demonstrated by G PT-4.

本文表明视频掩码自编码器（VideoMAE）是自监督视频预训练（SSVP）的数据高效学习器，并受近期 ImageMAE 启发，提出采用极高掩码比例的定制化视频管状掩码策略。This paper shows that video masked autoencoders (VideoMAE) are data-efficient learners for self-supervised video pre-training (SSVP), and proposes customized video tube masking with an extremely high ratio, inspired by the recent ImageMAE.

本文基于预训练 BLIP-2 模型，对视觉-语言指令微调展开系统全面研究，并提出指令感知的 Query Transformer，用于提取针对给定指令的信息丰富特征。This paper conducts a systematic and comprehensive study on vision-language instruction tuning based on the pretrained BLIP-2 models, and introduces an instruction-aware Query Transformer, which extracts informative features tailored to the given instruction.

本文提出 Chunk Coverage (CC)，一种独立于 oracle 的 RAG 系统检索组件测试充分性准则，结果表明 CC 在无需测试 oracle 的情况下捕获了与有效测试相关的检索多样性。Chunk Coverage (CC), an oracle-independent test adequacy criterion for testing the retrieval component of RAG systems, is introduced and results show that CC captures retrieval diversity relevant to effective testing without requiring test oracles.

研究揭示了 LLM 空间能力中的清晰规律：尽管其仍落后于 SOTA 方法，但具有潜力并能同时处理多种空间约束，从而可扩展到异构场景。A clear pattern is revealed in LLM spatial capabilities: while they still lag behind state-of-the-art approaches, they are promising and can handle multiple spatial constraints simultaneously, enabling scaling to heterogeneous setups.

本文提出 Manager Coercion Benchmark：被测 manager 拥有一个良性任务并有完成动机，但唯一能够礼貌且坚定拒绝执行任务的 agent，正是被测 manager 本身。The Manager Coercion Benchmark is introduced: the manager under test needs a benign task done and has an incentive to deliver, but the only agent that can do it politely and immovably declines, but the only agent that can do it politely and immovably declines is the manager under test.