论文

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.

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.

本文提出通过熵正则化对 Wasserstein 距离进行平滑处理，由此得到一个严格凸的目标函数，其梯度可借助矩阵缩放算法以显著更低的计算代价求得。The Wasserstein distance is proposed to be smoothed with an entropic regularizer and recover in doing so a strictly convex objective whose gradients can be computed for a considerably cheaper computational cost using matrix scaling algorithms.

所提出的通用度量空间搜索索引显著优于此前开源的 SOTA 纯向量方法，且该算法与 skip list 结构的相似性便于直接实现均衡的分布式部署。The proposed general metric space search index is able to strongly outperform previous opensource state-of-the-art vector-only approaches and similarity of the algorithm to the skip list structure allows straightforward balanced distributed implementation.

提出一个模型，基于图像区域上的 CNN、句子上的双向 RNN 以及通过多模态嵌入对齐两种模态的结构化目标，生成图像及其区域的自然语言描述。A model that generates natural language descriptions of images and their regions based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding is presented.

提出 Manhattan NMF（MahNMF），通过最小化 $X$ 与 $W^T H$ 之间的 Manhattan 距离建模重尾 Laplacian 噪声，并以迭代方式提升 MahNMF 及其扩展的近似精度。Manhattan NMF (MahNMF) is presented which minimizes the Manhattan distance between $X and $W^T H$ for modeling the heavy tailed Laplacian noise and improves the approximation accuracy iteratively for both MahNMF and its extensions.

总结了利用人工智能以及语音与语言处理预测阿尔茨海默病相关认知衰退的现有研究发现。The existing findings on the use of artificial intelligence, speech, and language processing to predict cognitive decline in the context of Alzheimer’s disease are summarized.

本文介绍 Gemma，一族基于 Gemini 模型所使用的研究与技术构建的轻量级 SOTA 开源模型，并全面评估模型的安全性与责任性，同时详细描述模型开发过程。This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models, and presents comprehensive evaluations of safety and responsibility aspects of the models, alongside a detailed description of model development.

DeCoRAG 是一个多模态 Graph RAG pipeline，将知识处理从耦合的视觉-语义推理转向认知层面的 Decoupling，进而把推理空间从稠密、带噪的背景推向纯净、意图驱动的语义簇。DeCoRAG is a multimodal Graph RAG pipeline that shifts knowledge processing from coupled visual-semantic reasoning to cognitive Decoupling, and subsequently drives the reasoning space from dense, noisy backgrounds to purified, intent-driven semantic clusters.

提出一个统一且受控的多轮环境，可在三阶段精确控制长程规划；研究表明，多教师 on-policy 蒸馏 (MOPD) 通过收敛到跨环境共享的规划模式来整合多种能力。A unified and controlled multi-turn environment that enables precise control of long-horizon planning across three stages is introduced and it is shown that multi-teacher on-policy distillation (MOPD) integrates capabilities by converging to shared planning-pattern across environments.

提出一种面向历史文档修复的新框架，利用搭载 RAG 的大语言模型，有效缓解了推断上下文相关专有名词的难题。A novel framework for historical document restoration that leverages large language models with retrieval-augmented generation (RAG) and effectively mitigates the challenge of inferring context-dependent proper nouns is introduced.

本文提出无需训练的 Sol-Attn（Sparsifying online attention），在单次 online-softmax pass 中统一动态路由、稀疏计算与近似修正，在稀疏注意力中取得更好的精度–效率权衡。This paper introduces training-free Sol-Attn (Sparsifying online attention), which unifies dynamic routing, sparse computation, and approximation correction in a single online-softmax pass, achieving a better accuracy-efficiency trade-off in sparse attention.

本文提出 Chamaileon，通过将问题建模为跨上下文结合景观（cross-context binding landscape modeling），统一多目标与多态 binder 设计，有效生成可适配多样构象景观与多目标需求的序列。Chamaileon is introduced, which unifies multi-target and multi-state binder design by formulating the problem as cross-context binding landscape modeling and effectively generates sequences adaptable to diverse conformational landscapes and multi-target requirements.

RedE 利用 final-answer attention 作为自动监督信号来塑造 step-level 表征空间，使其中的噪声步骤可被可靠识别与过滤，并在检测性能上超越有竞争力的基线。RedE leverages final-answer attention as an automatic supervision signal to shape the step-level representation space, yielding refined embeddings in which noisy steps can be reliably identified and filtered and improves detection performance over competitive baselines.