论文

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.

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.

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.

ReViV 是首个用于整体第一人称 4D 重建的统一框架，能够从单个单目 RGB 视频中同时提取观察者与视角动态，在整体自身体、手部与注视重建以及相机跟踪方面达到 SOTA 精度与效率，同时保持极具竞争力的第一人称深度估计能力。ReViV is the first unified framework for holistic egocentric 4D reconstruction that extracts both viewer and view dynamics from a single monocular RGB video and achieves state-of-the-art accuracy and efficiency across holistic ego-body, hand, and gaze reconstruction, camera tracking, while maintaining highly competitive egocentric depth estimation.

本工作通过 teacher forcing 与 ODE 蒸馏，将一个双向动作条件教师模型逐步蒸馏为因果学生模型，并提出 LongForcing，将学生模型的长时间自展开与扩展时域教师模型对齐，从而缓解累积的分布漂移与自回归漂移。This work progressively distill a bidirectional action-conditioned teacher into a causal student through teacher forcing and ODE distillation, and introduces LongForcing to align long student self-rollouts with an extended-horizon teacher, mitigating accumulated distribution shift and autoregressive drift.

在该研究的评估协议下，部分 AI 生成回答获得了高于可用参考答案的评分，尤其当其包含准确且相关的支撑细节时尤为明显。Under the study's evaluation protocol, some AI-generated responses received higher ratings than the available reference answers, particularly when they contained accurate and relevant supporting details, particularly when they contained accurate and relevant supporting details.

这些发现表明，即便长上下文评估正从简单检索转向复杂推理，对相关证据的准确 grounding 仍是一项不可或缺且仍有大幅提升空间的能力。These findings suggest that, even as long-context evaluation shifts from simple retrieval toward complex reasoning, accurate grounding in relevant evidence remains an indispensable capability with substantial room for improvement.

DeepDebug 在两个测试的开源权重 backbone 上均取得了所评估方法中最高的严格归因准确率，在 qwen3.5-9b 上达到 28.8% 的精确 agent 与步骤准确率，而最强的单遍 baseline 为 21.7%。DeepDebug achieves the best strict attribution accuracy among the evaluated methods on both tested open-weight backbones, reaching 28.8 percent exact agent-and-step accuracy on qwen3.5-9b versus 21.7 percent for the strongest single-pass baseline.

本文提出 Verbatimize 这一新任务，能够以高质量规范化逐字转录对语音语料库进行可扩展的创建与扩充，并通过有监督的 cross-attention 微调，将不流畅语音上的词级时间戳表现提升至超过强制对齐 baseline。Verbatimize is proposed, a new task enabling scalable creation and enrichment of speech corpora with high-quality canonical verbatim transcriptions and supervised cross-attention fine-tuning that improves word-level timestamps on disfluent speech beyond forced-alignment baselines.

本文研究 SkewAdam——一种基于以下观察构建的优化器：MoE 的三类参数群体在规模与梯度统计上差异足够大，不应共享相同的状态；研究表明优化器状态的存放位置至少与状态容量同等重要。SkewAdam, an optimizer built on the observation that the three parameter populations of an MoE differ enough in size and gradient statistics that they should not receive the same state, is studied, suggesting where optimizer state lives matters at least as much as how much of it there is.

本文描述了 TensorFlow 接口及 Google 构建的该接口实现，已被用于开展研究，并在计算机科学及其他十余个领域中将机器学习系统部署至生产环境。The TensorFlow interface and an implementation of that interface that is built at Google are described, which has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields.

本文推出参数规模从 7B 到 65B 的基础语言模型集合 LLaMA，并证明完全使用公开数据集即可训练出 SOTA 模型，无需依赖专有或不可获取的数据。LLaMA, a collection of foundation language models ranging from 7B to 65B parameters, is introduced and it is shown that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets.

研究发现，在上述多个维度上进行的指令微调可显著提升多种模型类别（PaLM、T5、U-PaLM）、多种提示设定以及多种评测基准（MMLU、BBH、TyDiQA、MGSM、开放式生成）上的表现。It is found that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups, and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation).

通过从概率分布的动态 nucleus 中采样文本，可在有效截断不可靠分布尾部的同时保持多样性，使生成文本更接近人类文本质量，在不牺牲流畅性与连贯性的前提下提升多样性。By sampling text from the dynamic nucleus of the probability distribution, which allows for diversity while effectively truncating the less reliable tail of the distribution, the resulting text better demonstrates the quality of human text, yielding enhanced diversity without sacrificing fluency and coherence.

本文指出基于 Transformer 的次二次时间模型的关键缺陷在于无法执行基于内容的推理，并将选择性 SSM 集成到不包含注意力乃至 MLP 块的简化端到端神经网络架构（Mamba）中。This work identifies that a key weakness of subquadratic-time models based on Transformer architecture is their inability to perform content-based reasoning, and integrates selective SSMs into a simplified end-to-end neural network architecture without attention or even MLP blocks (Mamba).

一个能够将任意自然语言任务轻松映射为人类可读 prompt 形式的系统，并在覆盖多种任务的多任务混合数据上对预训练 encoder-decoder 模型进行微调。A system for easily mapping any natural language tasks into a human-readable prompted form and fine-tune a pretrained encoder-decoder model on this multitask mixture covering a wide variety of tasks.

研究结果表明，文档表示不应被视为检索开始前一次性的固定预处理选择，而应作为一个明确的优化目标。The results suggest that document representation should not be treated as a fixed preprocessing choice made before retrieval begins, but as an explicit optimization target.

本文提出 IteraSim RAG，一个面向自动化 OpenFOAM 算例生成的 RAG 软件后端，围绕三大局限构建：求解器选择、湍流闭合、边界条件与有限体积默认值。IteraSim RAG is presented, a retrieval-augmented software back-end for automated OpenFOAM case generation built around three limitations: solver selection, turbulence closures, boundary conditions and finite-volume defaults.

FinMMEval 2026 Task 2 围绕多语言证据评估金融领域的短答问答，采用文档 RAG、跨语言证据处理、结构化提示、答案压缩与验证策略。FinMMEval 2026 Task 2 evaluates short-answer financial question answering over multilingual evidence over multilingual evidence using document retrieval-augmented generation, cross-lingual evidence handling, structured prompting, answer compression, and validation strategies.

Self Gradient Forcing（SGF）是一种两阶段训练策略，在原生自回归训练目标内恢复缺失的"记忆写入"监督信号，通过对未来视频 latent 的损失来训练模型将上下文编码为更有效的因果记忆。Self Gradient Forcing (SGF), a two-pass training strategy that restores this missing memory-writing supervision within the native autoregressive training objective, using losses on future video latents to train the model to encode context into more effective causal memory.