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今天的主题是：Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG

Summary

The provided text is a survey of Agentic Retrieval-Augmented Generation (RAG), a paradigm that enhances large language models by integrating autonomous AI agents into the RAG pipeline. This allows for dynamic retrieval strategies, contextual understanding, and iterative refinement, addressing the limitations of traditional RAG systems. The survey covers the evolution of RAG paradigms, detailed Agentic RAG architectures, and applications across industries like healthcare, finance, and education. It also explores implementation strategies, challenges in scaling, ethical considerations, performance optimization, and relevant frameworks and tools. Finally, the survey provides an overview of benchmarks and datasets used to evaluate RAG systems.

这篇文章是关于代理化检索增强生成（Agentic RAG）的综述，介绍了一种通过将自主AI代理集成到RAG流程中来增强大型语言模型的范式。通过这种方式，RAG能够实现动态的检索策略、上下文理解和迭代优化，克服了传统RAG系统的局限性。综述涵盖了RAG范式的演变、详细的代理化RAG架构以及在医疗、金融和教育等行业中的应用。文章还探讨了实现策略、扩展中的挑战、伦理考量、性能优化，以及相关的框架和工具。最后，文章提供了评估RAG系统所使用的基准和数据集的概述。

原文链接：https://arxiv.org/abs/2501.09136

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今天的主题是：Chain of Agents: Large Language Models Collaborating on Long-Context Tasks

Summary

Large language models (LLMs) struggle with long contexts due to limitations in processing extensive information. The "Chain-of-Agents" (CoA) framework addresses this by using multiple LLM agents that collaborate to process long documents. CoA divides the input into segments, assigns each segment to a worker agent, and then uses a manager agent to integrate the information and produce a final output. This method outperforms traditional approaches like Retrieval-Augmented Generation (RAG) and full-context LLMs, particularly in question answering, summarization, and code completion tasks. CoA also mitigates issues with focus within long contexts and is task-agnostic, training-free, and highly interpretable. Ultimately, the "Chain-of-Agents" framework facilitates improved processing and reasoning over long contexts, expanding the potential applications of LLMs in various domains.

大型语言模型（LLMs）在处理长上下文时面临困难，因为它们在处理大量信息时存在限制。为了应对这一挑战，"Chain-of-Agents"（CoA）框架通过使用多个LLM代理来协作处理长文档。CoA将输入划分为多个片段，将每个片段分配给一个工作代理，然后通过一个管理代理整合信息，最终生成输出。这种方法在问答、摘要和代码补全等任务中，特别是在处理长文档时，表现优于传统的检索增强生成（RAG）和全上下文LLM。CoA还解决了长上下文中的注意力问题，并且是任务无关的、无需训练的，并且具有高度的可解释性。最终，"Chain-of-Agents"框架通过提高长上下文的处理和推理能力，扩展了LLM在各个领域的潜在应用。

原文链接：https://arxiv.org/abs/2406.02818

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今天的主题是：Kimi k1.5: Scaling Reinforcement Learning with LLMs

Summary

This technical report introduces Kimi k1.5, a multimodal large language model trained with reinforcement learning (RL). The report highlights the model's training techniques, including long context scaling and policy optimization, emphasizing a simplistic yet effective RL framework. Kimi k1.5 achieves state-of-the-art reasoning performance across several benchmarks, even outperforming models like OpenAI's o1 and GPT-4o in certain short-CoT reasoning tasks. A key aspect is the exploration of long-context RL, with the model trained on sequences up to 128k tokens and improved policy optimization that uses a variant of online mirror descent for robust policy optimization. Furthermore, the report details long2short methods, infrastructure optimization, and ablation studies, showcasing Kimi k1.5's advancements in multi-modal AI capabilities and token efficiency.

这份技术报告介绍了Kimi k1.5，一款通过强化学习（RL）训练的多模态大型语言模型。报告重点讲述了模型的训练技术，包括长上下文扩展和策略优化，强调了一种简洁而有效的RL框架。Kimi k1.5在多个基准测试中达到了最先进的推理表现，甚至在某些短链推理任务中超越了OpenAI的o1和GPT-4o模型。一个关键方面是对长上下文RL的探索，该模型训练时处理的序列长度可达128k个tokens，并采用一种在线镜像下降的变种方法进行强化的策略优化。报告还详细介绍了长2短方法、基础设施优化和消融研究，展示了Kimi k1.5在多模态AI能力和token效率方面的进展。

原文链接：https://arxiv.org/abs/2501.12599

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今天的主题是：Humanity’s Last Exam

Summary

"Humanity's Last Exam" (HLE) introduces a new benchmark designed to assess the knowledge of large language models (LLMs) at the frontier of human expertise. This dataset contains 3,000 multiple-choice and short-answer questions across various subjects, emphasizing deep reasoning skills and resistance to simple internet retrieval. The questions undergo a rigorous review process by subject-matter experts to ensure difficulty and quality. Evaluations reveal that current LLMs exhibit low accuracy and poor calibration on HLE, indicating a significant gap in capabilities. The authors suggest HLE offers a reference point for AI progress and informs discussions on AI risks and governance. The creation of the data was a global effort by almost 1000 expert contributors.

《人类最后的考试》（HLE）推出了一个新基准，旨在评估大型语言模型（LLMs）在接近人类专家前沿领域的知识水平。该数据集包含3000个多项选择题和简答题，涵盖多个学科，重点考察深度推理能力并避免简单的互联网检索。所有问题都经过了学科专家的严格审查，确保难度和质量。评估结果显示，当前的LLM在HLE上的准确性较低，且校准效果差，表明其能力存在显著差距。作者认为，HLE为AI进展提供了一个参考点，并为AI风险与治理的讨论提供了依据。该数据的创建是由近1000名专家贡献的全球合作成果。

原文链接：https://arxiv.org/abs/2501.14249

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今天的主题是：DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Summary

DeepSeek-AI introduces DeepSeek-R1-Zero and DeepSeek-R1, reasoning-focused large language models. DeepSeek-R1-Zero uses reinforcement learning (RL) without supervised fine-tuning (SFT) to achieve remarkable reasoning capabilities. DeepSeek-R1 builds upon this by incorporating multi-stage training and "cold-start" data before RL, achieving results comparable to OpenAI's models. The company releases DeepSeek-R1-Zero, DeepSeek-R1, and distilled smaller models to support the research community. Experiments demonstrate that DeepSeek-R1 excels in reasoning tasks, outperforming other models in certain benchmarks, and distillation from DeepSeek-R1 greatly improves the reasoning abilities of smaller models. The study explores the benefits of RL and distillation, also discussing unsuccessful methods like Process Reward Models and Monte Carlo Tree Search.

DeepSeek-AI推出了DeepSeek-R1-Zero和DeepSeek-R1，这两款专注于推理的大型语言模型。DeepSeek-R1-Zero通过强化学习（RL）实现了显著的推理能力，而无需监督微调（SFT）。DeepSeek-R1在此基础上进一步发展，结合了多阶段训练和“冷启动”数据，在进行RL之前进行预训练，取得了与OpenAI模型相当的成果。公司发布了DeepSeek-R1-Zero、DeepSeek-R1以及经过蒸馏的小型模型，以支持研究社区。实验表明，DeepSeek-R1在推理任务上表现出色，在某些基准测试中超越了其他模型，并且从DeepSeek-R1进行蒸馏显著提升了小型模型的推理能力。研究还探讨了强化学习和蒸馏的优势，并讨论了如过程奖励模型和蒙特卡洛树搜索等未能成功的方法。

原文链接：https://arxiv.org/abs/2501.12948

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今天的主题是：Evolving Deeper LLM Thinking

Summary

This paper introduces Mind Evolution, a novel evolutionary search strategy for enhancing the problem-solving capabilities of Large Language Models (LLMs) in natural language planning. The method uses an LLM to generate, combine, and refine potential solutions iteratively, guided by feedback from an evaluator. Mind Evolution outperforms existing inference strategies by effectively leveraging inference time compute without needing a formal problem definition. The paper showcases impressive results on benchmarks like TravelPlanner and Natural Plan, even introducing a new challenging task called StegPoet. The core innovation lies in its ability to optimize solutions directly in natural language space, eliminating the need for task formalization. Ablation studies confirm the importance of critical conversation and feedback mechanisms within the evolutionary process. The authors demonstrate that the approach can achieve high success rates, sometimes even exceeding 99%, and point to the potential for future development of LLM-based evaluators to broaden the scope of application.

本文介绍了Mind Evolution，这是一种新颖的进化搜索策略，旨在提升大型语言模型（LLMs）在自然语言规划中的问题解决能力。该方法利用LLM生成、组合和迭代优化潜在解决方案，并通过评估器的反馈指导进程。Mind Evolution通过有效利用推理时的计算资源，超越了现有的推理策略，且无需正式的问题定义。本文在TravelPlanner和Natural Plan等基准任务上展示了令人印象深刻的结果，并引入了一个名为StegPoet的新挑战任务。其核心创新在于能够直接在自然语言空间中优化解决方案，省去了任务形式化的需求。消融实验确认了在进化过程中关键对话和反馈机制的重要性。作者证明该方法能够实现高成功率，有时甚至超过99%，并指出未来开发基于LLM的评估器具有扩大应用范围的潜力。

原文链接：https://arxiv.org/abs/2501.09891

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今天的主题是：Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation

Summary

This paper introduces Embodied-RAG, a novel framework designed to equip robots with enhanced memory and reasoning capabilities in complex environments. It tackles challenges in applying Retrieval-Augmented Generation (RAG) to robotics by constructing a hierarchical semantic forest for efficient knowledge storage and retrieval. Embodied-RAG integrates multimodal data and spatial awareness, outperforming existing RAG methods in navigation and explanation tasks. A new dataset, the Embodied-Experiences Dataset, is introduced to facilitate further research in this area. The core innovation lies in the system's ability to build and utilize a hierarchical spatial memory, enabling robots to navigate and communicate more effectively across diverse environments and query types. This work provides a foundation for developing generalist robot agents with language-based non-parametric memories.

本文介绍了Embodied-RAG，这是一种新型框架，旨在赋予机器人在复杂环境中更强的记忆和推理能力。它通过构建一个层次化的语义森林来解决将检索增强生成（RAG）应用于机器人领域的挑战，从而实现高效的知识存储和检索。Embodied-RAG 集成了多模态数据和空间意识，在导航和解释任务中优于现有的RAG方法。文中还引入了一个新的数据集——Embodied-Experiences 数据集，以促进该领域的进一步研究。该系统的核心创新在于其构建和利用层次化空间记忆的能力，使机器人能够更有效地在不同的环境和查询类型中进行导航和交流。这项工作为开发基于语言的非参数记忆的通用机器人智能体奠定了基础。

原文链接：https://arxiv.org/abs/2409.18313

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今天的主题是：VideoRAG: Retrieval-Augmented Generation over Video Corpus

Summary

VideoRAG is a novel framework that enhances Retrieval-Augmented Generation (RAG) by incorporating video content. Unlike traditional RAG, which primarily uses text, VideoRAG dynamically retrieves relevant videos and integrates both visual and textual information from them to generate more accurate and contextually rich answers. This approach leverages Large Video Language Models (LVLMs) to directly process video content and seamlessly combine it with queries. Experimental results demonstrate VideoRAG's superiority over existing RAG baselines, proving the effectiveness of using videos as a knowledge source. The study also addresses the challenge of missing video subtitles by generating auxiliary text using automatic speech recognition. Finally, the exploration of different modalities and their combinations underscores the importance of both visual and textual features in video-based RAG.

VideoRAG 是一种新型框架，通过引入视频内容增强了检索增强生成（RAG）。与传统的RAG主要依赖文本不同，VideoRAG 动态地检索相关视频，并从中整合视觉和文本信息，以生成更准确、更具上下文丰富性的答案。这一方法利用大型视频语言模型（LVLMs）直接处理视频内容，并将其与查询无缝结合。实验结果表明，VideoRAG 优于现有的RAG基准，证明了使用视频作为知识来源的有效性。该研究还解决了缺失视频字幕的问题，通过自动语音识别生成辅助文本。最后，不同模态及其组合的探索强调了视觉和文本特征在基于视频的RAG中的重要性。

原文链接：https://arxiv.org/abs/2501.05874

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今天的主题是：How to Train Your Energy-Based Models

Summary

Energy-Based Models (EBMs) offer a flexible approach to probabilistic modeling by specifying probability up to a normalizing constant, enabling the use of versatile architectures. The challenge lies in training these models due to the intractable normalizing constant. This document introduces and compares modern EBM training methods, focusing on Maximum Likelihood with Markov Chain Monte Carlo (MCMC), Score Matching (SM), and Noise Contrastive Estimation (NCE). The document elucidates the theoretical connections among these techniques and briefly explores alternative training methodologies. It also highlights the application of these techniques to score-based generative models. Finally, it discusses minimizing differences or derivatives of KL Divergences, minimizing the Stein discrepancy, and adversarial training.

能量基模型（EBMs）通过指定概率直到归一化常数，提供了一种灵活的概率建模方法，从而能够使用多种架构。训练这些模型的挑战在于归一化常数难以计算。本文介绍并比较了现代EBM训练方法，重点讨论了最大似然估计结合马尔可夫链蒙特卡洛（MCMC）、评分匹配（SM）和噪声对比估计（NCE）。文章阐明了这些技术之间的理论联系，并简要探讨了其他替代训练方法。同时，文章还重点介绍了这些技术在基于评分的生成模型中的应用。最后，本文讨论了最小化KL散度的差异或导数、最小化Stein差异性和对抗性训练的相关内容。

原文链接：https://arxiv.org/abs/2101.03288

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今天的主题是：Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps

Summary

This research explores enhancing diffusion models by scaling inference-time computation beyond simply increasing denoising steps. The authors propose a search framework that identifies better noises for the diffusion sampling process. This framework considers verifiers for feedback and algorithms to find noise candidates. Experiments on image generation show that increasing inference-time compute through this search framework improves sample quality. The study also analyzes the alignment between verifiers and generation tasks, revealing inherent biases. Ultimately, findings demonstrate substantial improvements in sample generation by diffusion models with increased computing power and a carefully chosen search setup.

这项研究探讨了通过扩大推理时计算量来提升扩散模型的表现，而不仅仅是增加去噪步骤。作者提出了一个搜索框架，用于识别更适合扩散采样过程的噪声。该框架考虑了反馈验证器和算法，用于寻找噪声候选项。图像生成实验表明，通过这一搜索框架增加推理时计算量能够提升样本质量。研究还分析了验证器与生成任务之间的对齐情况，揭示了固有的偏差。最终，研究结果表明，通过增加计算能力和精心选择搜索设置，扩散模型在样本生成方面实现了显著的提升。

原文链接：https://arxiv.org/abs/2501.09732

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