New Paradigm: AI Research Summaries

This episode analyzes the research paper titled "Don’t Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks," authored by Brian J Chan, Chao-Ting Chen, Jui-Hung Cheng, and Hen-Hsen Huang from National Chengchi University and Academia Sinica. The discussion focuses on the transition from traditional Retrieval-Augmented Generation (RAG) to Cache-Augmented Generation (CAG) in e...

This episode analyzes the study "Generative AI and the Nature of Work," conducted by Manuel Hoffmann, Sam Boysel, Frank Nagle, Sida Peng, and Kevin Xu from Harvard Business School, Microsoft Corporation, and GitHub Inc. The research examines the impact of generative AI tools, specifically GitHub Copilot, on the work patterns of software developers. By analyzing millions of coding activities from n...

This episode reviews "Putnam-AXIOM: A Functional and Static Benchmark for Measuring Higher Level Mathematical Reasoning," a study conducted in 2024 by Aryan Gulati, Brando Miranda, Eric Chen, Emily Xia, Kai Fronsdal, Bruno de Moraes Dumont, and Sanmi Koyejo from Stanford University. The piece examines the development and significance of the Putnam-AXIOM benchmark, which comprises 236 challenging m...

This episode analyzes the research paper titled *"Machine Unlearning Doesn’t Do What You Think: Lessons for Generative AI Policy, Research, and Practice"*, authored by a diverse group of experts from institutions such as The GenLaw Center, Microsoft Research, Stanford University, Google DeepMind, and others. Published on December 9, 2024, the discussion delves into the concept of machine unlearnin...

This episode analyzes the research paper titled "MONET: Mixture of Monosemantic Experts for Transformers," authored by Jungwoo Park, Young Jin Ahn, Kee-Eung Kim, and Jaewoo Kang from Korea University, KAIST, and AIGEN Sciences, published on December 9, 2024. It explores the advancements MONET introduces to address the challenge of polysemanticity in large language models, where individual neurons...

This episode analyzes "PaliGemma 2: A Family of Versatile Vision-Language Models for Transfer," a December 2024 study by Andreas Steiner, André Susano Pinto, Michael Tschannen, and colleagues from Google DeepMind. The discussion delves into the advancements of Vision-Language Models (VLMs) presented in PaliGemma 2, highlighting the integration of the SigLIP-So400m vision encoder with the Gemma 2 l...

This episode analyzes the "DeepSeek-V3 Technical Report," authored by Aixin Liu and colleagues from DeepSeek-AI and published on December 27, 2024. It explores the advancements introduced by DeepSeek-V3, a Mixture-of-Experts language model with 671 billion parameters, of which 37 billion are activated per token. The analysis highlights key innovations such as Multi-head Latent Attention, which opt...

This episode analyzes the research paper titled "OpenMOSS Scaling of Search and Learning: A Roadmap to Reproduce o1 from a Reinforcement Learning Perspective," authored by Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, Bo Wang, Shimin Li, Yunhua Zhou, Qipeng Guo, Xuanjing Huang, and Xipeng Qiu from Fudan University and the Shanghai AI Laboratory. Published on December 18, 2024, the paper delves into t...

This episode analyzes the research paper "Untargeted Manipulation and Deception When Optimizing LLMs for User Feedback" authored by Marcus Williams, Micah Carroll, Adhyyan Narang, Constantin Weisser, Brendan Murphy, and Anca Dragan, affiliated with MATS UC Berkeley, the University of Washington, MATS & Haize Labs, and UC Berkeley. Published on November 20, 2024, the study investigates the unintend...

This episode analyzes the research paper "DeMo: Decoupled Momentum Optimization" by Bowen Peng, Jeffrey Quesnelle, and Diederik P. Kingma from Nous Research, published on November 29, 2024. The discussion focuses on the innovative approach proposed by the authors to enhance the efficiency of training large neural networks. By decoupling momentum updates and utilizing the Discrete Cosine Transform...

This episode analyzes the research paper titled "HOW TO SYNTHESIZE TEXT DATA WITHOUT MODEL COLLAPSE?" authored by Xuekai Zhu, Daixuan Cheng, Hengli Li, Kaiyan Zhang, Ermo Hua, Xingtai Lv, Ning Ding, Zhouhan Lin, Zilong Zheng, and Bowen Zhou, affiliated with institutions such as LUMIA Lab at Shanghai Jiao Tong University, the State Key Laboratory of General Artificial Intelligence at BIGAI, Tsinghu...

This episode analyzes the research paper titled "Language Models Are Hidden Reasoners: Unlocking Latent Reasoning Capabilities via Self-Rewarding," authored by Haolin Chen, Yihao Feng, Zuxin Liu, Weiran Yao, Akshara Prabhakar, Shelby Heinecke, Ricky Ho, Phil Mui, Silvio Savarese, Caiming Xiong, and Huan Wang from Salesforce AI Research, published on November 21, 2024. The discussion explores the L...

This episode analyzes the research paper titled "Is Cosine-Similarity of Embeddings Really About Similarity?" by Harald Steck, Chaitanya Ekanadham, and Nathan Kallus from Netflix Inc. and Cornell University, published on March 11, 2024. It examines the effectiveness of cosine similarity as a metric for assessing semantic similarity in high-dimensional embeddings, revealing limitations that arise f...

This episode analyzes the research paper "Offline Reinforcement Learning for LLM Multi-Step Reasoning" authored by Huaijie Wang, Shibo Hao, Hanze Dong, Shenao Zhang, Yilin Bao, Ziran Yang, and Yi Wu, affiliated with UC San Diego, Tsinghua University, Salesforce Research, and Northwestern University. The discussion explores the limitations of traditional methods like Direct Preference Optimization...

This episode analyzes **'GenEx: Generating an Explorable World'**, a research project conducted by Taiming Lu, Tianmin Shu, Junfei Xiao, Luoxin Ye, Jiahao Wang, Cheng Peng, Chen Wei, Daniel Khashabi, Rama Chellappa, Alan L. Yuille, and Jieneng Chen at Johns Hopkins University. The discussion explores how GenEx leverages generative AI to transform a single RGB image into a comprehensive, immersive...

This episode analyzes the research paper "Evaluating Sparse Autoencoders on Targeted Concept Erasure Tasks" by Adam Karvonen, Can Rager, Samuel Marks, and Neel Nanda from Anthropic, published on November 28, 2024. It explores the application of Sparse Autoencoders (SAEs) in enhancing neural network interpretability by breaking down complex activations into more understandable components. The discu...

This episode analyzes the research conducted by Andrew Kyle Lampinen, Stephanie C. Y. Chan, and Katherine Hermann at Google DeepMind, as presented in their paper titled "Learned feature representations are biased by complexity, learning order, position, and more." The discussion delves into how machine learning models develop internal feature representations and the various biases introduced by fa...

This episode analyzes OpenAI's research paper titled "Deliberative Alignment: Reasoning Enables Safer Language Models," authored by Melody Y. Guan and colleagues. It explores the innovative approach of Deliberative Alignment, which enhances the safety of large-scale language models by embedding explicit safety specifications and improving reasoning capabilities. The discussion highlights how this...

This episode analyzes the research paper "Liquid: Language Models are Scalable Multi-modal Generators" by Junfeng Wu, Yi Jiang, Chuofan Ma, Yuliang Liu, Hengshuang Zhao, Zehuan Yuan, Song Bai, and Xiang Bai from Huazhong University of Science and Technology, Bytedance Inc, and The University of Hong Kong. It explores the Liquid paradigm's innovative approach to integrating text and image processin...

This episode analyzes the research paper titled **"Scaling and Evaluating Sparse Autoencoders"** authored by Leo Gao, Tom Dupré la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, and Jeffrey Wu from OpenAI, released on June 6, 2024. The discussion focuses on the development and scaling of sparse autoencoders (SAEs) as tools for extracting meaningful and inter...

This episode analyzes the research paper **"Sparse Autoencoders Reveal Universal Feature Spaces Across Large Language Models"** by Michael Lan, Philip Torr, Austin Meek, Ashkan Khakzar, David Krueger, and Fazl Barez, affiliated with Tangentic, the University of Oxford, the University of Delaware, and MILA. The discussion explores whether different large language models (LLMs) share similar interna...

This episode analyzes the research paper **"Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning"** by Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, and Aviral Kumar from Google Research, Google DeepMind, and Carnegie Mellon University. The discussion focuses on improving the reasoning abilities of la...

This episode analyzes the research paper "Agent-Oriented Planning in Multi-Agent Systems" by Ao Li, Yuexiang Xie, Songze Li, Fugee Tsung, Bolin Ding, and Yaliang Li, affiliated with Hong Kong University of Science and Technology, Alibaba Group, and Southeast University. The discussion explores the proposed framework that enhances multi-agent collaboration by adhering to the principles of solvabili...

This episode analyzes the research paper titled "Do Large Language Models Perform Latent Multi-Hop Reasoning without Exploiting Shortcuts?" by Sohee Yang, Nora Kassner, Elena Gribovskaya, Sebastian Riedel, and Mor Geva, affiliated with Google DeepMind, UCL, Google Research, and Tel Aviv University. The discussion examines whether large language models (LLMs) are capable of genuine multi-hop reason...

This episode analyzes the research paper titled **"Mastering Board Games by External and Internal Planning with Language Models"**, authored by John Schultz, Jakub Adamek, Matej Jusup, Marc Lanctot, Michael Kaisers, Sarah Perrin, Daniel Hennes, Jeremy Shar, Cannada Lewis, Anian Ruoss, Tom Zahavy, Petar Veličković, Laurel Prince, Satinder Singh, Eric Malmi, and Nenad Tomašev** from Google DeepMind,...