论文

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.

本文提出 Trace，一个面向多领域视觉推理的、由分类体系引导的环境，其将任务构建分解为场景语法与可执行任务程序，将视觉呈现与答案计算解耦，并提供了广泛的程序化训练可迁移到生成任务分布之外的证据。Trace is introduced, a taxonomy-guided environment for multidomain visual reasoning that factorizes task construction into a scene grammar and an executable task program, separating visual realization from answer computation, providing evidence that broad procedural training can transfer beyond the generated task distributions.

该设计将 hypernetwork 的注入能力与目标模型的通用能力解耦，首次实现了对 hypernetwork 架构 scaling law 的严格研究，并提供了首个基于实证的 scaling law，用以指导大语言模型中面向事实推理的 hypernetwork 设计。The design decouples the hypernetwork's injection capacity from the target model's general capability, enabling, for the first time, a rigorous study of scaling laws for hypernetwork architectures, and provides the first empirically grounded scaling laws to guide hypernetworks for factual reasoning in large language models.

本文提出 \method，一种无需训练的稀疏注意力系统，可在多 GPU 序列并行下提升自适应稀疏注意力的分布式执行效率。This work presents \method, a training-free sparse-attention system that improves the distributed execution efficiency of adaptive sparse attention under multi-GPU sequence parallelism.

提出两项审计协议——grounding and truth 对比以及 swap to an independent evaluator，以及 RECAP（Readable Encodings via Co-trained Auxiliary Predictors），即与目标模型联合训练的线性头，用于保持指定内容的可解码性。Two audit protocols, the comparison of grounding and truth and the swap to an independent evaluator, and RECAP (Readable Encodings via Co-trained Auxiliary Predictors), linear heads trained alongside the target model to keep designated content decodable are contributed.

GPT-3 在多个 NLP 数据集上取得了强劲表现，包括翻译、问答和完形填空任务，以及若干需要即时推理或领域适应的任务，例如乱序词重组、在句子中使用新词、或执行三位数算术运算。GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic.

本文设计了一种直接处理点云的新型神经网络，较好地尊重了输入点的置换不变性，并为从物体分类、部件分割到场景语义解析等应用提供了统一架构。This paper designs a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input and provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing.

本文开发并发布了 Llama 2，这是一系列参数规模从 70 亿到 700 亿不等的预训练与微调大语言模型（LLMs），有望成为闭源模型的合适替代品。This work develops and releases Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters, which may be a suitable substitute for closed-source models.

本研究证实了激活函数之间存在统计显著的性能差异，从而再次确认了非饱和函数在深度架构中的必要性，并恢复了对此前文献的恰当历史归属。This study confirms a statistically significant performance variance among activations, thus reaffirming the necessity of non-saturating functions in deep architectures, and restores proper historical attribution to prior literature.

一种基于二部交互图上可微分消息传递的图自编码器框架，在标准协同过滤基准上表现出竞争力，并优于近期的 SOTA 方法。A graph auto-encoder framework based on differentiable message passing on the bipartite interaction graph that shows competitive performance on standard collaborative filtering benchmarks and outperforms recent state-of-the-art methods.

本文介绍 MLlib，Spark 的开源分布式机器学习库，可为多种学习场景提供高效功能，并包含若干底层的统计、优化和线性代数原语。MLlib is presented, Spark's open-source distributed machine learning library that provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives.

本文提出了一个自然场景文本识别框架，无需任何人工标注数据，并以整体方式对整幅图像进行单词识别，区别于过去基于字符的识别系统。This work presents a framework for the recognition of natural scene text that does not require any human-labelled data, and performs word recognition on the whole image holistically, departing from the character based recognition systems of the past.

本文提出了一种新的解码策略——self-consistency，用于替代思维链 prompt 中使用的朴素贪心解码：首先采样一组多样化的推理路径，而非仅取贪心路径，然后通过对采样路径进行边缘化来选择最一致的答案。This paper proposes a new decoding strategy, self-consistency, to replace the naive greedy decoding used in chain-of-thought prompting that first samples a diverse set of reasoning paths instead of only taking the greedy one, and then selects the most consistent answer by marginalizing out the sampled reasoning paths.

结果表明，基于逻辑的神经架构成为资源受限脑机接口的实用范式，在原生满足便携式边缘部署的延迟和内存约束的同时，取得了有竞争力甚至更优的性能。The results establish logic-based neural architectures as a practical paradigm for resource-constrained brain-computer interfaces, achieving competitive or superior performance while natively satisfying the latency and memory constraints of portable edge deployment.

论证了经济层面的依据：天真的上下文累积会使 token 成本随对话长度呈二次增长，粗糙的摘要以线性成本换取准确率的断崖式下降，唯有经过验证的压缩才能以线性成本保持保真度。The economic case is made: naive context accumulation grows token cost quadratically in conversation length, crude summarization buys linear cost at the price of an accuracy cliff, and only validated compaction achieves linear cost with preserved fidelity.

研究揭示，正弦激活会诱发谐波线谱，为循环展开如何丰富隐式神经表示（INR）的有效频谱支撑提供了频谱层面的解释。It is revealed that sinusoidal activations induce a harmonic line spectrum, providing a spectral account of how recurrent unrolling enriches the effective spectral support in implicit neural representations (INRs).

与经典强化学习基线相比，从试错经验中进行上下文学习并随后进行经验蒸馏（Experience Distillation），以至少 9.6× 更少的环境样本达到了相当的性能。Compared with classical reinforcement-learning baselines, in-context learning from trial-and-error experience followed by Experience Distillation matches their performance with at least \(9.6\times\) fewer environment samples.

提出 ReferTrack，一种"先指代后跟踪"的范式，仅使用单个前向摄像头完成 EVT grounding，在四足机器人和人形机器人上的真实部署验证了其稳健的 sim-to-real 迁移能力。ReferTrack is introduced, a referring-then-tracking paradigm that grounds EVT using a single forward-facing camera, and real-world deployments on legged and humanoid robots validate its robust sim-to-real transfer capabilities.

本文提出 Color Pass-Through，一个端到端可学习的框架，直接在采集图像上运行，将相机和显示器视为耦合系统进行联合处理，而非单独校准。This work proposes Color Pass-Through, an end-to-end learned framework that operates directly on captured images, to treat the camera and display as a coupled system rather than calibrating them in isolation.

本文提出 SANA-Video 2.0，一个混合视频扩散 Transformer，在统一架构下实例化 5B 和 14B 两种规模，以显著降低的计算成本恢复了 softmax 级别的表达能力，解锁了可扩展的长时长、高分辨率视频生成。This work introduces SANA-Video 2.0, a hybrid video diffusion transformer instantiated at 5B and 14B scales under a unified architecture that recovers softmax-level expressiveness at substantially reduced cost, unlocking scalable long, high resolution video generation.

ReOPD 将昂贵的 Agent-环境交互转化为可复用的离线资源，实现跨工具、任务和环境的可扩展蒸馏，在学生训练期间保持或提升 OPD 级别的准确率，零次工具调用，并且每次 rollout 至少比 OPD 快 4×。ReOPD turns expensive agent-environment interaction into a reusable offline resource, enabling scalable distillation across tools, tasks, and environments and preserves or improves OPD-level accuracy, uses zero tool calls during student training, and is at least 4$\times faster per rollout than OPD.

本文进行了迄今为止对 Code LLMs 最全面的评估，结果显示 StarCoderBase 在支持多编程语言的开放 Code LLMs 中表现最优，并且能够匹敌或超越 OpenAI code-cushman-001 模型。This work performs the most comprehensive evaluation of Code LLMs to date and shows that StarCoderBase outperforms every open Code LLM that supports multiple programming languages and matches or outperforms the OpenAI code-cushman-001 model.

本文显著推进了针对已对齐语言模型的对抗攻击 SOTA，并提出了关于如何防止此类系统生成不良信息的重要问题。This work significantly advances the state-of-the-art in adversarial attacks against aligned language models, raising important questions about how such systems can be prevented from producing objectionable information.

实证研究表明 AutoGen 框架在多个示例应用中有效，应用领域涵盖数学、编码、问答、运筹学、在线决策、娱乐等。Empirical studies demonstrate the effectiveness of the AutoGen framework in many example applications, with domains ranging from mathematics, coding, question answering, operations research, online decision-making, entertainment, etc.

PaLM 2 是一个新的 SOTA 语言模型，相比其前身 PaLM 具有更强的多语言和推理能力，并具备更高的计算效率，能够在不增加额外开销或影响其他能力的前提下在推理时控制输出毒性。PaLM 2 is a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM and enables inference-time control over toxicity without additional overhead or impact on other capabilities.

提出 OpenForgeRL，一个用于在多样化环境中端到端训练基于 harness 的 Agent 的开源框架，并在多种复杂的 harness 和环境中得到验证，涵盖工具/爪型 Agent 以及多模态 GUI 浏览器和计算机使用 Agent。OpenForgeRL is presented, an open-source framework for training harness-based agents end-to-end in diverse environments, and validated across diverse, complex harnesses and environments, spanning tool/claw-based agents and multimodal GUI browser- and computer-use agents.

本文在使用 SOTA CNN 的情况下，实现了 16–24 倍的网络压缩，仅带来 1% 的分类准确率损失，并发现针对存储开销最大的全连接层进行压缩时，向量量化方法相比现有矩阵分解方法具有明显优势。This paper is able to achieve 16-24 times compression of the network with only 1% loss of classification accuracy using the state-of-the-art CNN, and finds in terms of compressing the most storage demanding dense connected layers, vector quantization methods have a clear gain over existing matrix factorization methods.