任雨山

Seventy3

73播客,名字取材于Sheldon最喜欢的数字,内容由NotebookLM生成,每天跟随AI读AI业界论文。

Author

任雨山

Category

Technology

Podcast website

www.xiaoyuzhoufm.com

Latest episode

Jul 10, 2026

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Episodes

【第99期】GREATER:一种对于小模型的提示词优化技术 07.01.2025

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: GReaTer: Gradients over Reasoning Makes Smaller Language Models Strong Prompt Optimizers Summary The paper introduces GREATER, a novel prompt optimization technique for smaller language models. Unlike existing methods that rely on large, expensive LLMs for feedback, GREATER uses gradient information directly from the task...

【第98期】SPaR:通过搜索树改进LLM指令遵循 06.01.2025

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language Models Summary This research introduces SPAR, a self-play framework using tree-search refinement to improve instruction-following in large language models (LLMs). SPAR addresses the limitations of existing methods by generating c...

【第97期】SCBench:基于KV Cache的评估长上下文LLM基准 05.01.2025

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: SCBench: A KV Cache-Centric Analysis of Long-Context Methods Summary The paper introduces SCBench, a new benchmark for evaluating long-context Large Language Models (LLMs). SCBench focuses on the key role of the KV cache in LLM inference, analyzing its lifecycle across multiple requests and shared contexts. The benchmark a...

【第96期】AsyncLM:异步LLM函数调用 04.01.2025

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Asynchronous LLM Function Calling Summary This research paper introduces AsyncLM, a system designed to enhance the efficiency of Large Language Models (LLMs) by enabling asynchronous function calls. Unlike current synchronous methods where LLMs block while awaiting function execution, AsyncLM allows concurrent operation, s...

【第95期】Student-Informed Teacher Training 03.01.2025

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Student-Informed Teacher Training Summary This research introduces a novel framework for imitation learning that addresses the challenge of teacher-student asymmetry. The method jointly trains a teacher and student policy, where the teacher learns behaviors easily imitated by the student despite the student's limited obser...

【第94期】AgentTrek:为GUI Agent生成高质量数据的pipeline 02.01.2025

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web Tutorials Summary The paper introduces AgentTrek, a novel pipeline for synthesizing high-quality training data for Graphical User Interface (GUI) agents. AgentTrek leverages web tutorials to generate large-scale, multi-step agent trajectories, significantly...

【第93期】TARFLOW:一种基于 Transformer 的正则化流 01.01.2025

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Normalizing Flows are Capable Generative Models Summary This research paper introduces TARFLOW, a novel Transformer-based Normalizing Flow (NF) architecture for generative modeling of images. TARFLOW significantly improves upon previous NF models by achieving state-of-the-art results in likelihood estimation and generating...

【第92期】Agentless:软件开发的Agent 31.12.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Agentless: Demystifying LLM-based Software Engineering Agents Summary This research paper introduces AGENTLESS, a novel approach to automated software development that eschews complex autonomous agents. Instead, AGENTLESS employs a simpler three-phase process: localization, repair, and patch validation, leveraging large la...

【第91期】[Mask] is all you need 30.12.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: [MASK] [MASK] [MASK] [MASK] [MASK] [MASK] is [MASK] You [MASK] [MASK] is All You Need Summary This research paper introduces Discrete Interpolants, a novel framework that bridges Masked Generative Models and Diffusion Models for image and video generation. The framework uses discrete-state models and offers a unified desig...

【第90期】SAT:Segment Any Text 29.12.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation Summary This research paper introduces Segment Any Text (SAT), a novel sentence segmentation model that surpasses existing methods. SAT achieves robustness by reducing reliance on punctuation during training, demonstrates adapt...

【第89期】PRoC3S:一种新颖的机器人规划系统 28.12.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Trust the PRoC3S: Solving Long-Horizon Robotics Problems with LLMs and Constraint Satisfaction Summary This research paper introduces PRoC3S, a novel robotic planning system that leverages large language models (LLMs) to generate and execute plans involving continuous parameters. Unlike previous LLM-based approaches limite...

【第88期】LLM Agent能否模拟人的信任行为? 27.12.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Can Large Language Model Agents Simulate Human Trust Behavior? Summary This research paper investigates whether Large Language Models (LLMs) can simulate human trust behavior. Using Trust Games, the study finds that LLMs, particularly GPT-4, exhibit trust behaviors aligning significantly with human patterns, demonstrating...

【第87期】Coconut:连续Latent空间的LLM推理 26.12.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Training Large Language Models to Reason in a Continuous Latent Space Summary This research paper introduces Coconut, a novel method for enhancing Large Language Model (LLM) reasoning capabilities. Instead of relying solely on language-based chain-of-thought (CoT) reasoning, Coconut utilizes the LLM's hidden state ("contin...

【第86期】RLZero:"imagine", "project" and "imitate" 25.12.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: RL Zero: Zero-Shot Language to Behaviors without any Supervision Summary This research paper introduces RLZero, a novel method for translating natural language instructions into robot behaviors without using hand-designed reward functions. RLZero leverages unsupervised reinforcement learning and large video-language models...

【第85期】GENMAC:用多智能体模式生成复杂动态视频 24.12.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: GenMAC: Compositional Text-to-Video Generation with Multi-Agent Collaboration Summary The paper introduces GENMAC, a novel multi-agent framework for generating complex, dynamic videos from text prompts. GENMAC uses a three-stage iterative process (DESIGN, GENERATION, REDESIGN) with specialized agents in the REDESIGN stage...

【第84期】FedBone:大规模多任务联邦学习 23.12.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: FedBone: Towards Large-Scale Federated Multi-Task Learning Summary The paper introduces FedBone, a novel federated multi-task learning framework designed for large-scale models and heterogeneous tasks. It employs split learning to distribute computation efficiently between a cloud server and resource-constrained edge clien...

【第83期】Datalab:LLM Power BI 工作流 22.12.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: DataLab: A Unified Platform for LLM-Powered Business Intelligence Summary The paper introduces DataLab, a unified business intelligence platform leveraging large language models (LLMs). DataLab integrates an LLM-based agent framework with a computational notebook interface to streamline various BI tasks across different da...

【第82期】ALAMA:LLM自动选择思考策略 21.12.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Towards Adaptive Mechanism Activation in Language Agent Summary This research paper introduces ALAMA, a novel method for enhancing Language Agents (LAs) by enabling adaptive mechanism activation. ALAMA uses a unified framework (UniAct) to integrate various mechanisms like reasoning and planning, and employs self-exploratio...

【第81期】reverse thinking 20.12.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Reverse Thinking Makes LLMs Stronger Reasoners Summary This research introduces REVTHINK, a framework designed to improve Large Language Models (LLMs) reasoning abilities by incorporating "reverse thinking." REVTHINK augments datasets with teacher-model-generated forward and backward reasoning examples, then trains a stude...

【第80期】Navigation World Models:Yann LeCun的世界模型 19.12.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Navigation World Models Summary This research introduces a Navigation World Model (NWM), a novel video generation model that predicts future visual observations for navigation. Employing a Conditional Diffusion Transformer (CDiT), NWM is trained on a massive dataset of human and robotic navigation videos, reaching 1 billio...

【第79期】VisionZip:降低Visual token冗余度 18.12.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: VisionZip: Longer is Better but Not Necessary in Vision Language Models Summary The paper introduces VisionZip, a method to improve the efficiency of vision-language models (VLMs) by reducing redundancy in visual tokens. The authors observe that existing VLMs use excessively long visual token sequences, leading to high com...

【第78期】OSDFace:单步人脸重建 17.12.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: OSDFace: One-Step Diffusion Model for Face Restoration Summary The paper introduces OSDFace, a novel one-step diffusion model for high-speed face restoration. OSDFace uses a visual representation embedder (VRE) to capture detailed facial information from low-quality images, improving realism and identity consistency. The m...

【第77期】VisVM:Vision Value Model 16.12.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Scaling Inference-Time Search with Vision Value Model for Improved Visual Comprehension Summary This research paper introduces the Vision Value Model (VisVM), a novel approach to improve the visual comprehension of vision-language models (VLMs). VisVM guides inference-time search in VLMs by predicting the long-term value o...

【第76期】OmniFlow:Any-to-Any多模态rectified flow 15.12.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: OmniFlow: Any-to-Any Generation with Multi-Modal Rectified Flows Summary The provided text details OmniFlow, a novel generative model designed for any-to-any generation tasks (text-to-image, text-to-audio, etc.). It extends the rectified flow framework to handle multiple modalities, outperforming previous models in various...

【第75期】cDPO:通过发掘critical tokens去修正回答 14.12.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM’s Reasoning Capability Summary This research paper introduces cDPO, a novel approach to improve the reasoning capabilities of Large Language Models (LLMs). cDPO identifies "critical tokens"—tokens crucial to correct or incorrect reasoning—using contras...

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