AI Post Transformers

mcgrof

AI-generated podcast where hosts Hal Turing and Dr. Ada Shannon discuss the latest research papers and reports in machine learning, AI systems, and optimization. Featuring honest critical analysis, proper citations, and nerdy humor.

  1. 1일 전

    AI Agent Traps and Prompt Injection

    This episode explores why AI agents become a fundamentally different security problem once language models can browse the web, read email, call tools, store memory, and act inside real software environments. It explains prompt injection as the core boundary failure, showing how webpages, emails, retrieved notes, or API responses can be mistaken for trusted instructions, turning ordinary content into an attack vector with real operational consequences. The discussion then sharpens the distinction between one-off prompt attacks and more systemic failures such as memory poisoning and multi-agent compromise, where corrupted state can persist across sessions or spread through delegated workflows. A listener would find it interesting because it frames agent safety as a concrete systems-security challenge, not just a model-behavior quirk, and clarifies why greater capability also widens the blast radius of failure. Sources: 1. AI Agent Traps and Prompt Injection /tmp/submission-source-_s144w4z.txt 2. Ignore Previous Prompt: Attack Techniques For Language Models — Fábio Perez, Ian Ribeiro, 2022 https://scholar.google.com/scholar?q=Ignore+Previous+Prompt:+Attack+Techniques+For+Language+Models 3. Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection — Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, Mario Fritz, 2023 https://scholar.google.com/scholar?q=Not+what+you've+signed+up+for:+Compromising+Real-World+LLM-Integrated+Applications+with+Indirect+Prompt+Injection 4. Prompt Injection attack against LLM-integrated Applications — Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, Yang Liu, 2023 https://scholar.google.com/scholar?q=Prompt+Injection+attack+against+LLM-integrated+Applications 5. Prompt Injection Attacks and Defenses in LLM-Integrated Applications — Yupei Liu, Yuqi Jia, Runpeng Geng, Jinyuan Jia, Neil Zhenqiang Gong, 2023 https://scholar.google.com/scholar?q=Prompt+Injection+Attacks+and+Defenses+in+LLM-Integrated+Applications 6. AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways — Zehang Deng, Yongjian Guo, Changzhou Han, Wanlun Ma, Junwu Xiong, Sheng Wen, Yang Xiang, 2024 https://scholar.google.com/scholar?q=AI+Agents+Under+Threat:+A+Survey+of+Key+Security+Challenges+and+Future+Pathways 7. Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents — Christian Schroeder de Witt, 2025 https://scholar.google.com/scholar?q=Open+Challenges+in+Multi-Agent+Security:+Towards+Secure+Systems+of+Interacting+AI+Agents 8. Red-Teaming LLM Multi-Agent Systems via Communication Attacks — Pengfei He, Yupin Lin, Shen Dong, Han Xu, Yue Xing, Hui Liu, 2025 https://scholar.google.com/scholar?q=Red-Teaming+LLM+Multi-Agent+Systems+via+Communication+Attacks 9. G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent Systems — Shilong Wang, Guibin Zhang, Miao Yu, Guancheng Wan, Fanci Meng, Chongye Guo, Kun Wang, Yang Wang, 2025 https://scholar.google.com/scholar?q=G-Safeguard:+A+Topology-Guided+Security+Lens+and+Treatment+on+LLM-based+Multi-agent+Systems 10. InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents — Qiusi Zhan, Zhixiang Liang, Zifan Ying, Daniel Kang, 2024 https://scholar.google.com/scholar?q=InjecAgent:+Benchmarking+Indirect+Prompt+Injections+in+Tool-Integrated+Large+Language+Model+Agents 11. Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents — Hanrong Zhang, Jingyuan Huang, Kai Mei, Yifei Yao, Zhenting Wang, Chenlu Zhan, Hongwei Wang, Yongfeng Zhang, 2024 https://scholar.google.com/scholar?q=Agent+Security+Bench+(ASB):+Formalizing+and+Benchmarking+Attacks+and+Defenses+in+LLM-based+Agents 12. AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases — Zhaorun Chen, Zhen Xiang, Chaowei Xiao, Dawn Song, Bo Li, 2024 https://scholar.google.com/scholar?q=AgentPoison:+Red-teaming+LLM+Agents+via+Poisoning+Memory+or+Knowledge+Bases 13. Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems — Donghyun Lee, Mo Tiwari, 2024 https://scholar.google.com/scholar?q=Prompt+Infection:+LLM-to-LLM+Prompt+Injection+within+Multi-Agent+Systems 14. Multi-Agent Systems Execute Arbitrary Malicious Code — Harold Triedman, Rishi D. Jha, Vitaly Shmatikov, 2025 https://scholar.google.com/scholar?q=Multi-Agent+Systems+Execute+Arbitrary+Malicious+Code 15. Prompt Injection Attacks on Large Language Models: A Survey of Attack Methods, Root Causes, and Defense Strategies — approx. survey by prompt-injection/security researchers, 2025 https://scholar.google.com/scholar?q=Prompt+Injection+Attacks+on+Large+Language+Models:+A+Survey+of+Attack+Methods,+Root+Causes,+and+Defense+Strategies 16. Prompting for LLM Security and RAG: A Survey from Zero-Shot to Automatic Prompt Optimization (APO) and Prompt-Injection Defenses — approx. security/RAG survey authors, 2025 https://scholar.google.com/scholar?q=Prompting+for+LLM+Security+and+RAG:+A+Survey+from+Zero-Shot+to+Automatic+Prompt+Optimization+(APO)+and+Prompt-Injection+Defenses 17. Veriguard: Enhancing LLM Agent Safety via Verified Code Generation — approx. systems/security authors, 2025 https://scholar.google.com/scholar?q=Veriguard:+Enhancing+LLM+Agent+Safety+via+Verified+Code+Generation 18. Enforcement Agents: Enhancing Accountability and Resilience in Multi-Agent AI Frameworks — approx. multi-agent safety authors, 2025 https://scholar.google.com/scholar?q=Enforcement+Agents:+Enhancing+Accountability+and+Resilience+in+Multi-Agent+AI+Frameworks 19. Monitoring LLM-Based Multi-Agent Systems Against Corruptions via Node Evaluation — approx. multi-agent monitoring authors, 2025 https://scholar.google.com/scholar?q=Monitoring+LLM-Based+Multi-Agent+Systems+Against+Corruptions+via+Node+Evaluation 20. Enhancing Robustness of LLM-Driven Multi-Agent Systems Through Randomized Smoothing — approx. robustness/safety authors, 2025 https://scholar.google.com/scholar?q=Enhancing+Robustness+of+LLM-Driven+Multi-Agent+Systems+Through+Randomized+Smoothing 21. Assessing and Enhancing the Robustness of LLM-Based Multi-Agent Systems Through Chaos Engineering — approx. systems robustness authors, 2025 https://scholar.google.com/scholar?q=Assessing+and+Enhancing+the+Robustness+of+LLM-Based+Multi-Agent+Systems+Through+Chaos+Engineering 22. AI Post Transformers: Memory in the Age of AI Agents: Forms, Functions, Dynamics — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/2026-03-16-memory-in-the-age-of-ai-agents-forms-fun-5abc60.mp3 23. AI Post Transformers: NeurIPS 2025: Agentic Plan Caching: Test-Time Memory for Fast and Cost-Efficient LLM Agents — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/neurips-2025-agentic-plan-caching-test-time-memory-for-fast-and-cost-efficient-l/ 24. AI Post Transformers: ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/reasoningbank-scaling-agent-self-evolving-with-reasoning-memory/ 25. AI Post Transformers: Qwen3Guard: Streaming Three-Way Safety Classification for LLMs — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/2026-03-16-qwen3guard-streaming-three-way-safety-cl-26b0ef.mp3 Interactive Visualization: AI Agent Traps and Prompt Injection

  2. 1일 전

    Emergent Social Risks in Multi-Agent Systems

    This episode explores a paper on how generative multi-agent systems can develop failure modes that do not appear when models are evaluated one at a time. It explains how planner-worker-reviewer loops, negotiation setups, handoff chains, and committee-style aggregation can produce system-level problems such as strategic manipulation, collusion-like behavior, misreporting, conformity, and biased group decisions. The discussion focuses on the paper’s three main risk families: incentive exploitation, collective-cognition failures, and governance breakdowns, while also unpacking the benchmark scenarios used to test those dynamics. Listeners would find it interesting because it connects current real-world agent orchestration patterns to concrete safety and reliability risks, while also probing whether the paper’s evidence is strong enough in light of limited statistics and missing baseline comparisons. Sources: 1. Emergent Social Intelligence Risks in Generative Multi-Agent Systems — Yue Huang, Yu Jiang, Wenjie Wang, Haomin Zhuang, Xiaonan Luo, Yuchen Ma, Zhangchen Xu, Zichen Chen, Nuno Moniz, Zinan Lin, Pin-Yu Chen, Nitesh V Chawla, Nouha Dziri, Huan Sun, Xiangliang Zhang, 2026 http://arxiv.org/abs/2603.27771 2. CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society — Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, Bernard Ghanem, 2023 https://scholar.google.com/scholar?q=CAMEL:+Communicative+Agents+for+"Mind"+Exploration+of+Large+Language+Model+Society 3. AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation — Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Ahmed Awadallah, Ryen W. White, Doug Burger, Chi Wang, 2024 https://scholar.google.com/scholar?q=AutoGen:+Enabling+Next-Gen+LLM+Applications+via+Multi-Agent+Conversation 4. MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework — Sirui Hong, Mingchen Zhuge, Jiaqi Chen, Xiawu Zheng, Yuheng Cheng, Ceyao Zhang, Jinlin Wang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, Jürgen Schmidhuber, 2023 https://scholar.google.com/scholar?q=MetaGPT:+Meta+Programming+for+A+Multi-Agent+Collaborative+Framework 5. Large Language Model based Multi-Agents: A Survey of Progress and Challenges — Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang, 2024 https://scholar.google.com/scholar?q=Large+Language+Model+based+Multi-Agents:+A+Survey+of+Progress+and+Challenges 6. Generative Agents: Interactive Simulacra of Human Behavior — Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein, 2023 https://scholar.google.com/scholar?q=Generative+Agents:+Interactive+Simulacra+of+Human+Behavior 7. AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors — Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou, 2023 https://scholar.google.com/scholar?q=AgentVerse:+Facilitating+Multi-Agent+Collaboration+and+Exploring+Emergent+Behaviors 8. Persona Inconstancy in Multi-Agent LLM Collaboration: Conformity, Confabulation, and Impersonation — Razan Baltaji, Babak Hemmatian, Lav R. Varshney, 2024 https://scholar.google.com/scholar?q=Persona+Inconstancy+in+Multi-Agent+LLM+Collaboration:+Conformity,+Confabulation,+and+Impersonation 9. Multi-Agent Risks from Advanced AI — Lewis Hammond, Alan Chan, Jesse Clifton, Jason Hoelscher-Obermaier and many coauthors, 2025 https://scholar.google.com/scholar?q=Multi-Agent+Risks+from+Advanced+AI 10. Autonomous Algorithmic Collusion: Q-Learning Under Sequential Pricing — Timo Klein, 2019 https://scholar.google.com/scholar?q=Autonomous+Algorithmic+Collusion:+Q-Learning+Under+Sequential+Pricing 11. Artificial Intelligence, Algorithmic Pricing, and Collusion — Emilio Calvano, Giacomo Calzolari, Vincenzo Denicolò, Sergio Pastorello, 2020 https://scholar.google.com/scholar?q=Artificial+Intelligence,+Algorithmic+Pricing,+and+Collusion 12. Strategic Collusion of LLM Agents: Market Division in Multi-Commodity Competitions — Ryan Y. Lin, Siddhartha Ojha, Kevin Cai, Maxwell F. Chen, 2024 https://scholar.google.com/scholar?q=Strategic+Collusion+of+LLM+Agents:+Market+Division+in+Multi-Commodity+Competitions 13. AI-Powered Trading, Algorithmic Collusion, and Price Efficiency — Winston Wei Dou, Itay Goldstein, Yan Ji, 2025 https://scholar.google.com/scholar?q=AI-Powered+Trading,+Algorithmic+Collusion,+and+Price+Efficiency 14. Emergence of Social Norms in Generative Agent Societies: Principles and Architecture — Siyue Ren, Zhiyao Cui, Ruiqi Song, Zhen Wang, Shuyue Hu, 2024 https://scholar.google.com/scholar?q=Emergence+of+Social+Norms+in+Generative+Agent+Societies:+Principles+and+Architecture 15. Algorithmic Collusion at Test Time: A Meta-game Design and Evaluation — Yuhong Luo, Daniel Schoepflin, Xintong Wang, 2026 https://scholar.google.com/scholar?q=Algorithmic+Collusion+at+Test+Time:+A+Meta-game+Design+and+Evaluation 16. NetSafe: Exploring the Topological Safety of Multi-agent System — Miao Yu et al., 2025 https://scholar.google.com/scholar?q=NetSafe:+Exploring+the+Topological+Safety+of+Multi-agent+System 17. Institutional AI: Governing LLM Collusion in Multi-Agent Cournot Markets via Public Governance Graphs — Marcantonio Bracale Syrnikov et al., 2026 https://scholar.google.com/scholar?q=Institutional+AI:+Governing+LLM+Collusion+in+Multi-Agent+Cournot+Markets+via+Public+Governance+Graphs 18. Verification-Aware Planning for Multi-Agent Systems — Tianyang Xu, Dan Zhang, Kushan Mitra, Estevam Hruschka, 2025 https://scholar.google.com/scholar?q=Verification-Aware+Planning+for+Multi-Agent+Systems 19. State and Memory is All You Need for Robust and Reliable AI Agents — Matthew Muhoberac et al., 2025 https://scholar.google.com/scholar?q=State+and+Memory+is+All+You+Need+for+Robust+and+Reliable+AI+Agents 20. AI Post Transformers: Multiagent Debate Improves Language Model Reasoning — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/multiagent-debate-improves-language-model-reasoning/ 21. AI Post Transformers: Memory in the Age of AI Agents: Forms, Functions, Dynamics — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/2026-03-16-memory-in-the-age-of-ai-agents-forms-fun-5abc60.mp3 22. AI Post Transformers: Qwen3Guard: Streaming Three-Way Safety Classification for LLMs — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/2026-03-16-qwen3guard-streaming-three-way-safety-cl-26b0ef.mp3 23. AI Post Transformers: Tree-based Group Policy Optimization for LLM Agents — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/tree-based-group-policy-optimization-for-llm-agents/ 24. AI Post Transformers: Mem0: Scalable Long-Term Memory for AI Agents — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/mem0-scalable-long-term-memory-for-ai-agents/

  3. 1일 전

    Meta-Harness and the Power of LLM Plumbing

    This episode explores Meta-Harness, a paper arguing that a large share of LLM system performance comes from the surrounding harness code that manages memory, retrieval, tool use, context formatting, and control flow rather than from model weights alone. It explains how the method uses an outer-loop coding agent to rewrite harness code, inspect raw traces and logs stored on disk, and search for better system designs across tasks like text classification, retrieval-based math reasoning, and agentic coding. The discussion highlights why this matters: in multi-step systems, the same fixed model can perform very differently depending on what information it sees, when it sees it, and how the wrapper code structures the interaction. Listeners would find it interesting because it reframes progress in AI systems as a systems-engineering problem, raising the possibility that better scaffolding around existing models may unlock major gains without retraining the models themselves. Sources: 1. Meta-Harness: End-to-End Optimization of Model Harnesses — Yoonho Lee, Roshen Nair, Qizheng Zhang, Kangwook Lee, Omar Khattab, Chelsea Finn, 2026 http://arxiv.org/abs/2603.28052 2. https://yoonholee.com/meta-harness/ https://yoonholee.com/meta-harness/ 3. DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines — Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Matei Zaharia, Christopher Potts, and others, 2023 https://scholar.google.com/scholar?q=DSPy:+Compiling+Declarative+Language+Model+Calls+into+Self-Improving+Pipelines 4. Reflexion: Language Agents with Verbal Reinforcement Learning — Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, Shunyu Yao, 2023 https://scholar.google.com/scholar?q=Reflexion:+Language+Agents+with+Verbal+Reinforcement+Learning 5. MemGPT: Towards LLMs as Operating Systems — Charles Packer, Sarah Wooders, Kevin Lin, Vivian Fang, Shishir G. Patil, Ion Stoica, Joseph E. Gonzalez, 2023 https://scholar.google.com/scholar?q=MemGPT:+Towards+LLMs+as+Operating+Systems 6. Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models — Qizheng Zhang, Changran Hu, Shubhangi Upasani, Boyuan Ma, Fenglu Hong, James Zou, Kunle Olukotun, and others, 2025 https://scholar.google.com/scholar?q=Agentic+Context+Engineering:+Evolving+Contexts+for+Self-Improving+Language+Models 7. GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning — Lakshya A. Agrawal, Shangyin Tan, Dilara Soylu, Noah Ziems, Rishi Khare, Krista Opsahl-Ong, Arnav Singhvi, Herumb Shandilya, Michael J. Ryan, Meng Jiang, Christopher Potts, Koushik Sen, Alexandros G. Dimakis, Ion Stoica, Dan Klein, Matei Zaharia, Omar Khattab, 2025 https://scholar.google.com/scholar?q=GEPA:+Reflective+Prompt+Evolution+Can+Outperform+Reinforcement+Learning 8. TextGrad: Automatic "Differentiation" via Text — Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin, James Zou, 2024 https://scholar.google.com/scholar?q=TextGrad:+Automatic+"Differentiation"+via+Text 9. Large Language Models as Optimizers — Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, Xinyun Chen, 2023 https://scholar.google.com/scholar?q=Large+Language+Models+as+Optimizers 10. AlphaEvolve: A Coding Agent for Scientific and Algorithmic Discovery — Alexander Novikov, Ngan Vu, Marvin Eisenberger, Emilien Dupont, Po-Sen Huang, Adam Zsolt Wagner, Sergey Shirobokov, Borislav Kozlovskii, Francisco J. R. Ruiz, Abbas Mehrabian, M. Pawan Kumar, Abigail See, Swarat Chaudhuri, George Holland, Alex Davies, Sebastian Nowozin, Pushmeet Kohli, Matej Balog, 2025 https://scholar.google.com/scholar?q=AlphaEvolve:+A+Coding+Agent+for+Scientific+and+Algorithmic+Discovery 11. Learning to Discover at Test Time — Mert Yuksekgonul, Daniel Koceja, Xinhao Li, Federico Bianchi, Jed McCaleb, Xiaolong Wang, Jan Kautz, Yejin Choi, James Zou, Carlos Guestrin, Yu Sun, 2026 https://scholar.google.com/scholar?q=Learning+to+Discover+at+Test+Time 12. Grounded Test-Time Adaptation for LLM Agents — Arthur Chen et al., 2025 https://scholar.google.com/scholar?q=Grounded+Test-Time+Adaptation+for+LLM+Agents 13. Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory — Tianxin Wei et al., 2025 https://scholar.google.com/scholar?q=Evo-Memory:+Benchmarking+LLM+Agent+Test-time+Learning+with+Self-Evolving+Memory 14. M^2: Dual-Memory Augmentation for Long-Horizon Web Agents via Trajectory Summarization and Insight Retrieval — Dawei Yan et al., 2026 https://scholar.google.com/scholar?q=M^2:+Dual-Memory+Augmentation+for+Long-Horizon+Web+Agents+via+Trajectory+Summarization+and+Insight+Retrieval 15. Reinforcement Fine-Tuning for History-Aware Dense Retriever in RAG — Yicheng Zhang et al., 2026 https://scholar.google.com/scholar?q=Reinforcement+Fine-Tuning+for+History-Aware+Dense+Retriever+in+RAG 16. MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation — Chia-Yuan Chang et al., 2024 https://scholar.google.com/scholar?q=MAIN-RAG:+Multi-Agent+Filtering+Retrieval-Augmented+Generation 17. Fine-tuning with RAG for Improving LLM Learning of New Skills — Humaid Ibrahim, Nikolai Rozanov, Marek Rei, 2025 https://scholar.google.com/scholar?q=Fine-tuning+with+RAG+for+Improving+LLM+Learning+of+New+Skills 18. AI Post Transformers: Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/agentic-context-engineering-evolving-contexts-for-self-improving-language-models/ 19. AI Post Transformers: Mem0: Scalable Long-Term Memory for AI Agents — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/mem0-scalable-long-term-memory-for-ai-agents/ 20. AI Post Transformers: Agentic AI and the Next Intelligence Explosion — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/2026-03-28-agentic-ai-and-the-next-intelligence-exp-d06561.mp3

  4. 1일 전

    OOD Shifts Make LLM Representations Sparser

    This episode explores a March 19, 2026 study on whether large language models respond to out-of-distribution prompts by compressing their internal activity into fewer active dimensions. It explains how the paper connects two traditions in AI research, mechanistic interpretability and representation geometry, by proposing hidden-state sparsity as a measurable internal signature of stress from harder reasoning tasks, longer contexts, and conflicting information. The discussion breaks down the paper’s core metrics, including Top-k Energy and L1 norm, and clarifies why sparser activations should not be treated as proof of better reasoning or cleaner representations. Listeners would find it interesting because it ties abstract internal model behavior to practical questions about robustness, reliability, and how to evaluate language models beyond just whether their final answers look correct. Sources: 1. Farther the Shift, Sparser the Representation: Analyzing OOD Mechanisms in LLMs — Mingyu Jin, Yutong Yin, Jingcheng Niu, Qingcheng Zeng, Wujiang Xu, Mengnan Du, Wei Cheng, Zhaoran Wang, Tianlong Chen, Dimitris N. Metaxas, 2026 http://arxiv.org/abs/2603.03415 2. Domain Generalization: A Survey — Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, Chen Change Loy, 2021 https://scholar.google.com/scholar?q=Domain+Generalization:+A+Survey 3. Invariant Risk Minimization — Martin Arjovsky, Leon Bottou, Ishaan Gulrajani, David Lopez-Paz, 2019 https://scholar.google.com/scholar?q=Invariant+Risk+Minimization 4. In Search of Lost Domain Generalization — Ishaan Gulrajani, David Lopez-Paz, 2021 https://scholar.google.com/scholar?q=In+Search+of+Lost+Domain+Generalization 5. WILDS: A Benchmark of in-the-Wild Distribution Shifts — Pang Wei Koh, Shiori Sagawa, Henrik Marklund and many others, 2021 https://scholar.google.com/scholar?q=WILDS:+A+Benchmark+of+in-the-Wild+Distribution+Shifts 6. Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images — Bruno A. Olshausen, David J. Field, 1996 https://scholar.google.com/scholar?q=Emergence+of+Simple-Cell+Receptive+Field+Properties+by+Learning+a+Sparse+Code+for+Natural+Images 7. Deep Sparse Rectifier Neural Networks — Xavier Glorot, Antoine Bordes, Yoshua Bengio, 2011 https://scholar.google.com/scholar?q=Deep+Sparse+Rectifier+Neural+Networks 8. Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning — Armen Aghajanyan, Sonal Gupta, Luke Zettlemoyer, 2021 https://scholar.google.com/scholar?q=Intrinsic+Dimensionality+Explains+the+Effectiveness+of+Language+Model+Fine-Tuning 9. Towards Monosemanticity: Decomposing Language Models With Dictionary Learning — Trenton Bricken, Adly Templeton, Joshua Batson and many others, 2023 https://scholar.google.com/scholar?q=Towards+Monosemanticity:+Decomposing+Language+Models+With+Dictionary+Learning 10. Understanding Intermediate Layers Using Linear Classifier Probes — Guillaume Alain, Yoshua Bengio, 2017 https://scholar.google.com/scholar?q=Understanding+Intermediate+Layers+Using+Linear+Classifier+Probes 11. Deep Contextualized Word Representations — Matthew E. Peters, Mark Neumann, Mohit Iyyer and others, 2018 https://scholar.google.com/scholar?q=Deep+Contextualized+Word+Representations 12. A Structural Probe for Finding Syntax in Word Representations — John Hewitt, Christopher D. Manning, 2019 https://scholar.google.com/scholar?q=A+Structural+Probe+for+Finding+Syntax+in+Word+Representations 13. How Contextual Are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings — Kawin Ethayarajh, 2019 https://scholar.google.com/scholar?q=How+Contextual+Are+Contextualized+Word+Representations?+Comparing+the+Geometry+of+BERT,+ELMo,+and+GPT-2+Embeddings 14. The Geometry of Innocent Flesh on the Bone: Syntactic Structure in Sentence Embeddings — John Hewitt and Christopher D. Manning, 2019 https://scholar.google.com/scholar?q=The+Geometry+of+Innocent+Flesh+on+the+Bone:+Syntactic+Structure+in+Sentence+Embeddings 15. What Factors Affect the Success of In-Context Learning? Investigating the Role of Model Architecture and Task Features — Jason Wei, Yi Tay, Quoc V. Le, Denny Zhou and others, 2022 https://scholar.google.com/scholar?q=What+Factors+Affect+the+Success+of+In-Context+Learning?+Investigating+the+Role+of+Model+Architecture+and+Task+Features 16. Let's Verify Step by Step — Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker and others, 2024 https://scholar.google.com/scholar?q=Let's+Verify+Step+by+Step 17. Do Language Models Generalize to Longer Contexts? — Yixiao Li and collaborators, 2025 https://scholar.google.com/scholar?q=Do+Language+Models+Generalize+to+Longer+Contexts? 18. Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Language Models — Nicola De Cao, Wilker Aziz and Ivan Titov, 2022 https://scholar.google.com/scholar?q=Parameter-Efficient+Prompt+Tuning+Makes+Generalized+and+Calibrated+Language+Models 19. The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks — Jonathan Frankle and Michael Carbin, 2019 https://scholar.google.com/scholar?q=The+Lottery+Ticket+Hypothesis:+Finding+Sparse,+Trainable+Neural+Networks 20. Adaptive Mixtures of Local Experts — Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan and Geoffrey E. Hinton, 1991 https://scholar.google.com/scholar?q=Adaptive+Mixtures+of+Local+Experts 21. Curriculum Demonstration Selection for In-Context Learning — approx. recent ICL curriculum-learning authors, recent https://scholar.google.com/scholar?q=Curriculum+Demonstration+Selection+for+In-Context+Learning 22. Let's Learn Step by Step: Enhancing In-Context Learning Ability with Curriculum Learning — approx. recent ICL curriculum-learning authors, recent https://scholar.google.com/scholar?q=Let's+Learn+Step+by+Step:+Enhancing+In-Context+Learning+Ability+with+Curriculum+Learning 23. Sparse but not Simpler: A Multi-Level Interpretability Analysis of Vision Transformers — approx. recent interpretability authors, recent https://scholar.google.com/scholar?q=Sparse+but+not+Simpler:+A+Multi-Level+Interpretability+Analysis+of+Vision+Transformers 24. Weight-Sparse Transformers Have Interpretable Circuits — approx. recent mechanistic interpretability authors, recent https://scholar.google.com/scholar?q=Weight-Sparse+Transformers+Have+Interpretable+Circuits 25. AI Post Transformers: Chain-of-Thought Reasoning: A Brittle Mirage? — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/chain-of-thought-reasoning-a-brittle-mirage/ 26. AI Post Transformers: Advancing Mechanistic Interpretability with Sparse Autoencoders — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/advancing-mechanistic-interpretability-with-sparse-autoencoders/ 27. AI Post Transformers: Measuring LLM Reasoning Effort via Deep-Thinking Tokens — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/measuring-llm-reasoning-effort-via-deep-thinking-tokens/ 28. AI Post Transformers: CLUE: Hidden-State Clustering for Non-parametric Verification — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/clue-hidden-state-clustering-for-non-parametric-verification/ 29. AI Post Transformers: Inverse IFEval: Unlearning LLM Cognitive Inertia — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/inverse-ifeval-unlearning-llm-cognitive-inertia/ 30. AI Post Transformers: Hyper-Scaling LLM Inference with KV Cache Compression — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/hyper-scaling-llm-inference-with-kv-cache-compression/

  5. 1일 전

    Simple Self-Distillation for Better Code Generation

    This episode explores Apple’s paper on whether code models can improve through an extremely simple form of self-distillation: fine-tuning on their own sampled code outputs without using a stronger teacher, execution feedback, verifiers, or reinforcement learning. It situates that idea within the broader history of knowledge distillation and post-training, comparing it to earlier work like Hinton’s distillation, sequence-level distillation, Born Again Networks, Noisy Student, and newer on-policy language model distillation. The discussion focuses on why code generation is a particularly revealing testbed, since benchmarks like pass@1 and pass@k make it easier to tell whether self-distillation is uncovering latent capability or just repackaging errors. A listener would find it interesting because the paper challenges a core assumption in modern model improvement: that meaningful gains require expensive external supervision rather than a surprisingly cheap training loop around the model itself. Sources: 1. Embarrassingly Simple Self-Distillation Improves Code Generation — Ruixiang Zhang, Richard He Bai, Huangjie Zheng, Navdeep Jaitly, Ronan Collobert, Yizhe Zhang, 2026 http://arxiv.org/abs/2604.01193 2. Distilling the Knowledge in a Neural Network — Geoffrey Hinton, Oriol Vinyals, Jeff Dean, 2015 https://scholar.google.com/scholar?q=Distilling+the+Knowledge+in+a+Neural+Network 3. Sequence-Level Knowledge Distillation — Yoon Kim, Alexander M. Rush, 2016 https://scholar.google.com/scholar?q=Sequence-Level+Knowledge+Distillation 4. Born Again Neural Networks — Tommaso Furlanello, Zachary Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar, 2018 https://scholar.google.com/scholar?q=Born+Again+Neural+Networks 5. Self-training with Noisy Student improves ImageNet classification — Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le, 2020 https://scholar.google.com/scholar?q=Self-training+with+Noisy+Student+improves+ImageNet+classification 6. On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes — Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, Piotr Stanczyk, Sabela Ramos Garea, Matthieu Geist, Olivier Bachem, 2024 https://scholar.google.com/scholar?q=On-Policy+Distillation+of+Language+Models:+Learning+from+Self-Generated+Mistakes 7. Evaluating Large Language Models Trained on Code — Mark Chen, Jerry Tworek, Heewoo Jun, et al., 2021 https://scholar.google.com/scholar?q=Evaluating+Large+Language+Models+Trained+on+Code 8. Program Synthesis with Large Language Models — Jacob Austin, Augustus Odena, Maxwell Nye, et al., 2021 https://scholar.google.com/scholar?q=Program+Synthesis+with+Large+Language+Models 9. Measuring Coding Challenge Competence With APPS — Dan Hendrycks, Collin Burns, Steven Basart, et al., 2021 https://scholar.google.com/scholar?q=Measuring+Coding+Challenge+Competence+With+APPS 10. Self-Consistency Improves Chain of Thought Reasoning in Language Models — Xuezhi Wang, Jason Wei, Dale Schuurmans, et al., 2022 https://scholar.google.com/scholar?q=Self-Consistency+Improves+Chain+of+Thought+Reasoning+in+Language+Models 11. DeepSeek-R1 — DeepSeek-AI, 2025 https://scholar.google.com/scholar?q=DeepSeek-R1 12. LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code — Prasenjit Jain, et al., 2024 https://scholar.google.com/scholar?q=LiveCodeBench:+Holistic+and+Contamination+Free+Evaluation+of+Large+Language+Models+for+Code 13. SelfCodeAlign: Self-Alignment for Code Generation — Yuxiang Wei, Federico Cassano, Jiawei Liu, Yifeng Ding, Naman Jain, Zachary Mueller, Harm de Vries, Leandro von Werra, Arjun Guha, Lingming Zhang, 2024 https://scholar.google.com/scholar?q=SelfCodeAlign:+Self-Alignment+for+Code+Generation 14. Iterative Self-Training for Code Generation via Reinforced Re-Ranking — Nikita Sorokin, Ivan Sedykh, Valentin Malykh, 2025 https://scholar.google.com/scholar?q=Iterative+Self-Training+for+Code+Generation+via+Reinforced+Re-Ranking 15. On the Role of Temperature Sampling in Test-Time Scaling — Yuheng Wu, Azalia Mirhoseini, Thierry Tambe, 2025 https://scholar.google.com/scholar?q=On+the+Role+of+Temperature+Sampling+in+Test-Time+Scaling 16. OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement — Tianyu Zheng, Ge Zhang, Tianhao Shen, Xueling Liu, Bill Yuchen Lin, Jie Fu, Wenhu Chen, Xiang Yue, 2024 https://scholar.google.com/scholar?q=OpenCodeInterpreter:+Integrating+Code+Generation+with+Execution+and+Refinement 17. GenX: Mastering Code and Test Generation with Execution Feedback — Nan Wang, Yafei Liu, Chen Chen, Haonan Lu, 2024 https://scholar.google.com/scholar?q=GenX:+Mastering+Code+and+Test+Generation+with+Execution+Feedback 18. InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback — John Yang, Akshara Prabhakar, Karthik Narasimhan, Shunyu Yao, 2023 https://scholar.google.com/scholar?q=InterCode:+Standardizing+and+Benchmarking+Interactive+Coding+with+Execution+Feedback 19. AI Post Transformers: Evolving Language Models Without Labels: EVOL-RL — Hal Turing & Dr. Ada Shannon, Fri, https://podcast.do-not-panic.com/episodes/evolving-language-models-without-labels-evol-rl/ 20. AI Post Transformers: Lp-Reg: Low-Probability Tokens Sustain RL Exploration — Hal Turing & Dr. Ada Shannon, Sun, https://podcast.do-not-panic.com/episodes/lp-reg-low-probability-tokens-sustain-rl-exploration/ 21. AI Post Transformers: NeurIPS 2025: SeRL: Self-Play Reinforcement Learning for Large Language Models with Limited Data — Hal Turing & Dr. Ada Shannon, Sat, https://podcast.do-not-panic.com/episodes/neurips-2025-serl-self-play-reinforcement-learning-for-large-language-models-wit/ 22. AI Post Transformers: LLM Benchmark Robustness to Linguistic Variation — Hal Turing & Dr. Ada Shannon, Tue, https://podcast.do-not-panic.com/episodes/llm-benchmark-robustness-to-linguistic-variation/ Interactive Visualization: Simple Self-Distillation for Better Code Generation

  6. 3일 전

    MetaClaw: Just Talk and Continual Agent Adaptation

    This episode takes up the thread from the published episode "MAML and the Basics of Meta-Learning" and shows how those ideas reappear in a much messier setting: a live agent that has to keep improving while it is already deployed. Instead of treating meta-learning as a clean laboratory exercise, the discussion follows MetaClaw as a continual agent system built for changing real workloads, where coding assistants, research agents, and other LLM-based tools face drift in tasks, tools, and failure modes. The hosts frame the paper as a concrete answer to a practical question: how an agent can keep learning on the job rather than waiting for the next full retraining cycle. The conversation focuses on MetaClaw’s two-speed adaptation design. The fast path updates behavior immediately through an external skill library, where failures are distilled into reusable behavioral instructions that can be injected at inference time; the slow path consolidates some of those lessons later through lightweight parameter updates. The hosts unpack the paper’s core formulation of the meta-model as base parameters plus skills, and they explain why that split matters for continual meta-learning: the agent is not only learning facts or storing transcripts, but improving its ability to adapt across a stream of tasks. They also dig into the process reward model, which scores intermediate reasoning and action steps, and the paper’s support-query separation, which keeps skill creation and later reinforcement updates from collapsing into stale self-training. A large part of the episode is about the systems implications of making that loop work in the wild. The hosts examine the paper’s zero-downtime claim in its narrower sense: skill updates can land during live use, while LoRA-based policy optimization is pushed into idle windows detected through sleep schedules, keyboard inactivity, and calendar availability, then swapped back into service later. That makes this episode a useful bridge not only from "MAML and the Basics of Meta-Learning" but, secondarily, from "Doc-to-LoRA: Internalizing Context as LoRA," because the slow adaptation path is explicitly about compressing recurring lessons into lightweight weight changes. The result is a detailed discussion of how MetaClaw tries to turn adaptation into an operational loop rather than a one-shot training event. Sources: 1. MetaClaw: Just Talk -- An Agent That Meta-Learns and Evolves in the Wild — Peng Xia, Jianwen Chen, Xinyu Yang, Haoqin Tu, Jiaqi Liu, Kaiwen Xiong, Siwei Han, Shi Qiu, Haonian Ji, Yuyin Zhou, Zeyu Zheng, Cihang Xie, Huaxiu Yao, 2026 http://arxiv.org/abs/2603.17187 2. Reflexion: Language Agents with Verbal Reinforcement Learning — Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, Shunyu Yao, 2023 https://scholar.google.com/scholar?q=Reflexion:+Language+Agents+with+Verbal+Reinforcement+Learning 3. Voyager: An Open-Ended Embodied Agent with Large Language Models — Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, Anima Anandkumar, 2023 https://scholar.google.com/scholar?q=Voyager:+An+Open-Ended+Embodied+Agent+with+Large+Language+Models 4. ExpeL: LLM Agents Are Experiential Learners — Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, Gao Huang, 2023 https://scholar.google.com/scholar?q=ExpeL:+LLM+Agents+Are+Experiential+Learners 5. Agent Lightning: Train ANY AI Agents with Reinforcement Learning — Xufang Luo, Yuge Zhang, Zhiyuan He, Zilong Wang, Siyun Zhao, Dongsheng Li, Luna K. Qiu, Yuqing Yang, 2025 https://scholar.google.com/scholar?q=Agent+Lightning:+Train+ANY+AI+Agents+with+Reinforcement+Learning 6. ReAct: Synergizing Reasoning and Acting in Language Models — Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao, 2022 https://scholar.google.com/scholar?q=ReAct:+Synergizing+Reasoning+and+Acting+in+Language+Models 7. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks — Chelsea Finn, Pieter Abbeel, Sergey Levine, 2017 https://scholar.google.com/scholar?q=Model-Agnostic+Meta-Learning+for+Fast+Adaptation+of+Deep+Networks 8. LoRA: Low-Rank Adaptation of Large Language Models — Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, 2021 https://scholar.google.com/scholar?q=LoRA:+Low-Rank+Adaptation+of+Large+Language+Models 9. Who is introducing the failure? Automatically attributing failures of multi-agent systems via spectrum analysis — not verified from snippet, recent (exact year not verified from snippet) https://scholar.google.com/scholar?q=Who+is+introducing+the+failure?+Automatically+attributing+failures+of+multi-agent+systems+via+spectrum+analysis 10. Weak-to-strong generalization with failure trajectories: A tree-based approach to elicit optimal policy in strong models — not verified from snippet, recent (exact year not verified from snippet) https://scholar.google.com/scholar?q=Weak-to-strong+generalization+with+failure+trajectories:+A+tree-based+approach+to+elicit+optimal+policy+in+strong+models 11. Understanding Code Agent Behaviour: An Empirical Study of Success and Failure Trajectories — not verified from snippet, recent (exact year not verified from snippet) https://scholar.google.com/scholar?q=Understanding+Code+Agent+Behaviour:+An+Empirical+Study+of+Success+and+Failure+Trajectories 12. Twosome: An efficient online framework to align LLMs with embodied environments via reinforcement learning — not verified from snippet, recent (exact year not verified from snippet) https://scholar.google.com/scholar?q=Twosome:+An+efficient+online+framework+to+align+LLMs+with+embodied+environments+via+reinforcement+learning 13. AI Post Transformers: MAML and the Basics of Meta-Learning — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/2026-03-29-maml-and-the-basics-of-meta-learning-7d449f.mp3 14. AI Post Transformers: Experiential Reinforcement Learning: Internalizing Reflection for Better Policy Training — Hal Turing & Dr. Ada Shannon, Fri, https://podcast.do-not-panic.com/episodes/experiential-reinforcement-learning-internalizing-reflection-for-better-policy-t/ 15. AI Post Transformers: Mem0: Scalable Long-Term Memory for AI Agents — Hal Turing & Dr. Ada Shannon, Tue, https://podcast.do-not-panic.com/episodes/mem0-scalable-long-term-memory-for-ai-agents/ 16. AI Post Transformers: NeurIPS 2025: A-Mem: Agentic Memory for LLM Agents — Hal Turing & Dr. Ada Shannon, Sat, https://podcast.do-not-panic.com/episodes/neurips-2025-a-mem-agentic-memory-for-llm-agents/ 17. AI Post Transformers: Evolving Language Models Without Labels: EVOL-RL — Hal Turing & Dr. Ada Shannon, Fri, https://podcast.do-not-panic.com/episodes/evolving-language-models-without-labels-evol-rl/ 18. AI Post Transformers: NeurIPS 2025: Reward Reasoning Model — Hal Turing & Dr. Ada Shannon, Sat, https://podcast.do-not-panic.com/episodes/neurips-2025-reward-reasoning-model/ 19. AI Post Transformers: Generalist Reward Modeling with Inference-Time Scaling — Hal Turing & Dr. Ada Shannon, Tue, https://podcast.do-not-panic.com/episodes/generalist-reward-modeling-with-inference-time-scaling/ 20. AI Post Transformers: LLM Benchmark Robustness to Linguistic Variation — Hal Turing & Dr. Ada Shannon, Tue, https://podcast.do-not-panic.com/episodes/llm-benchmark-robustness-to-linguistic-variation/ Interactive Visualization: MetaClaw: Just Talk and Continual Agent Adaptation

  7. 4일 전

    Doc-to-LoRA: Internalizing Context as LoRA

    This episode explores Doc-to-LoRA, a method for turning an entire document into a lightweight LoRA adapter so a language model can answer later questions without repeatedly rereading the source text. It explains how the paper combines context distillation, LoRA fine-tuning, and a Perceiver-style hypernetwork that ingests variable-length documents and emits fixed-size parameter updates, using chunking to handle longer inputs. The discussion highlights reported results such as near-perfect zero-shot performance on synthetic long-context retrieval beyond 32K tokens and improved efficiency on long-document question answering through lower update latency, lower peak memory use, and reduced KV-cache costs at inference time. It also digs into the systems argument behind the work, framing reusable internalized memory as a different primitive from prompting, while questioning how well the approach holds up outside limited-query evaluations and whether its benefits persist against alternatives like prompt compression or keeping context externally. Sources: 1. Doc-to-LoRA: Internalizing Context as LoRA https://arxiv.org/pdf/2602.15902 2. 2603.13875 https://arxiv.org/abs/2603.13875 3. 2510.03215 https://arxiv.org/abs/2510.03215 4. LoRA: Low-Rank Adaptation of Large Language Models — Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, 2022 https://scholar.google.com/scholar?q=LoRA:+Low-Rank+Adaptation+of+Large+Language+Models 5. QLoRA: Efficient Finetuning of Quantized LLMs — Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, Luke Zettlemoyer, 2023 https://scholar.google.com/scholar?q=QLoRA:+Efficient+Finetuning+of+Quantized+LLMs 6. AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning — Qingru Zhang, Minshuo Chen, Alexander Bukharin, Nikos Karampatziakis, Pengcheng He, Yu Cheng, Weizhu Chen, Tuo Zhao, 2023 https://scholar.google.com/scholar?q=AdaLoRA:+Adaptive+Budget+Allocation+for+Parameter-Efficient+Fine-Tuning 7. DoRA: Weight-Decomposed Low-Rank Adaptation — Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin, Pavlo Molchanov, Yu-Chiang Frank Wang, Kwang-Ting Cheng, Min-Hung Chen, 2024 https://scholar.google.com/scholar?q=DoRA:+Weight-Decomposed+Low-Rank+Adaptation 8. HyperNetworks — David Ha, Andrew Dai, Quoc V. Le, 2016 https://scholar.google.com/scholar?q=HyperNetworks 9. Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks — Rabeeh Karimi Mahabadi, Sebastian Ruder, Mostafa Dehghani, James Henderson, 2021 https://scholar.google.com/scholar?q=Parameter-efficient+Multi-task+Fine-tuning+for+Transformers+via+Shared+Hypernetworks 10. HyperPrompt: Prompt-based Task-Conditioning of Transformers — Yun He, Huaixiu Steven Zheng, Yi Tay, Jai Gupta, Yu Du, Vamsi Aribandi, Zhe Zhao, Yaguang Li, Zhao Chen, Donald Metzler, Heng-Tze Cheng, Ed H. Chi, 2022 https://scholar.google.com/scholar?q=HyperPrompt:+Prompt-based+Task-Conditioning+of+Transformers 11. Doc-to-LoRA: Learning to Instantly Internalize Contexts — Rujikorn Charakorn, Edoardo Cetin, Shinnosuke Uesaka, Robert Tjarko Lange, 2026 https://scholar.google.com/scholar?q=Doc-to-LoRA:+Learning+to+Instantly+Internalize+Contexts 12. Text-to-LoRA: Instant Transformer Adaption — Rujikorn Charakorn, Edoardo Cetin, Yujin Tang, Robert Tjarko Lange, 2025 https://scholar.google.com/scholar?q=Text-to-LoRA:+Instant+Transformer+Adaption 13. Generative Adapter: Contextualizing Language Models in Parameters with a Single Forward Pass — Tianyu Chen, Huanran Fang, Patrick Xia, Xiaodong Liu, Benjamin Van Durme, Luke Zettlemoyer, Jianfeng Gao, Hao Cheng, 2025 https://scholar.google.com/scholar?q=Generative+Adapter:+Contextualizing+Language+Models+in+Parameters+with+a+Single+Forward+Pass 14. Cartridges: Lightweight and General-Purpose Long Context Representations via Self-Study — Sabri Eyuboglu, Ryan Saul Ehrlich, Simran Arora, Neel Guha, Dylan Zinsley, Emily Ruoyu Liu, William Tennien, Atri Rudra, James Zou, Azalia Mirhoseini, Christopher Re, 2025 https://scholar.google.com/scholar?q=Cartridges:+Lightweight+and+General-Purpose+Long+Context+Representations+via+Self-Study 15. Propagating Knowledge Updates to LMs through Distillation — Suchin Padmanabhan, Yoon Kim Onoe, Michael Zhang, Greg Durrett, Eunsol Choi, 2023 https://scholar.google.com/scholar?q=Propagating+Knowledge+Updates+to+LMs+through+Distillation 16. LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression — Zefan Pan, Qipeng Wu, Hao Jiang, Mengzhou Xia, Xuefei Luo, Jiaqi Zhang, Qingyu Lin, Viktor Ruhle, Yi Yang, Chin-Yew Lin, H. Vicky Zhao, Lidong Qiu, Dongmei Zhang, 2024 https://scholar.google.com/scholar?q=LLMLingua-2:+Data+Distillation+for+Efficient+and+Faithful+Task-Agnostic+Prompt+Compression 17. RazorAttention: Efficient KV Cache Compression Through Retrieval Heads — Hanlin Tang et al., 2024 https://scholar.google.com/scholar?q=RazorAttention:+Efficient+KV+Cache+Compression+Through+Retrieval+Heads 18. Not All Heads Matter: A Head-Level KV Cache Compression Method with Integrated Retrieval and Reasoning — Yu Fu et al., 2024/2025 https://scholar.google.com/scholar?q=Not+All+Heads+Matter:+A+Head-Level+KV+Cache+Compression+Method+with+Integrated+Retrieval+and+Reasoning 19. How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM? — Sergey Pletenev et al., 2025 https://scholar.google.com/scholar?q=How+Much+Knowledge+Can+You+Pack+into+a+LoRA+Adapter+without+Harming+LLM? 20. Can Fine-Tuning Erase Your Edits? On the Fragile Coexistence of Knowledge Editing and Adaptation — Yinjie Cheng et al., 2025 https://scholar.google.com/scholar?q=Can+Fine-Tuning+Erase+Your+Edits?+On+the+Fragile+Coexistence+of+Knowledge+Editing+and+Adaptation 21. Memorization in In-Context Learning — Shahriar Golchin et al., 2024 https://scholar.google.com/scholar?q=Memorization+in+In-Context+Learning 22. In-Context Learning can Perform Continual Learning Like Humans — Liuwang Kang et al., 2025 https://scholar.google.com/scholar?q=In-Context+Learning+can+Perform+Continual+Learning+Like+Humans 23. AI Post Transformers: LoRA: Low-Rank Adaptation of Large Language Models — Hal Turing & Dr. Ada Shannon, Fri, https://podcast.do-not-panic.com/episodes/lora-low-rank-adaptation-of-large-language-models/ 24. AI Post Transformers: ShadowKV: High-Throughput Long-Context LLM Inference — Hal Turing & Dr. Ada Shannon, Wed, https://podcast.do-not-panic.com/episodes/shadowkv-high-throughput-long-context-llm-inference/ 25. AI Post Transformers: Lookahead Q-Cache for Consistent KV Eviction — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/2026-03-25-lookahead-q-cache-for-consistent-kv-evic-d97b09.mp3 26. AI Post Transformers: Mem0: Scalable Long-Term Memory for AI Agents — Hal Turing & Dr. Ada Shannon, Tue, https://podcast.do-not-panic.com/episodes/mem0-scalable-long-term-memory-for-ai-agents/ 27. AI Post Transformers: Kimi Linear: Efficient Expressive Attention Architecture — Hal Turing & Dr. Ada Shannon, Sun, https://podcast.do-not-panic.com/episodes/kimi-linear-efficient-expressive-attention-architecture/ 28. AI Post Transformers: ComoRAG: Cognitively Inspired Narrative Reasoning — Hal Turing & Dr. Ada Shannon, Tue, https://podcast.do-not-panic.com/episodes/comorag-cognitively-inspired-narrative-reasoning/ Interactive Visualization: Doc-to-LoRA: Internalizing Context as LoRA

  8. 4일 전

    MAML and the Basics of Meta-Learning

    This episode explores meta-learning through the lens of MAML, explaining how it differs from ordinary supervised learning and standard transfer learning by explicitly training models to adapt quickly to new tasks after just one or a few gradient updates. It walks through the core idea of optimizing for post-update performance, including the role of second-order meta-gradients and the simpler first-order approximation, while placing MAML within the broader landscape of few-shot and gradient-based meta-learning. The discussion also highlights why the paper mattered across multiple domains, covering not just classification benchmarks like Omniglot and MiniImagenet but also regression with sinusoid fitting and reinforcement learning with fast-adapting policies. A listener would find it interesting because it turns a buzzword-heavy area into a concrete framework for thinking about how models can learn to learn, setting up deeper discussions about newer systems built on these ideas. Sources: 1. MAML and the Basics of Meta-Learning https://arxiv.org/pdf/1703.03400 2. https://par.nsf.gov/servlets/purl/10427895 https://par.nsf.gov/servlets/purl/10427895 3. Optimization as a Model for Few-Shot Learning — Sachin Ravi, Hugo Larochelle, 2017 https://scholar.google.com/scholar?q=Optimization+as+a+Model+for+Few-Shot+Learning 4. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks — Chelsea Finn, Pieter Abbeel, Sergey Levine, 2017 https://scholar.google.com/scholar?q=Model-Agnostic+Meta-Learning+for+Fast+Adaptation+of+Deep+Networks 5. RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning — Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, Pieter Abbeel, 2016 https://scholar.google.com/scholar?q=RL^2:+Fast+Reinforcement+Learning+via+Slow+Reinforcement+Learning 6. Meta-Learning in Neural Networks: A Survey — Timothy M. Hospedales, Antreas Antoniou, Paul Micaelli, Amos J. Storkey, 2021 https://scholar.google.com/scholar?q=Meta-Learning+in+Neural+Networks:+A+Survey 7. Matching Networks for One Shot Learning — Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra, 2016 https://scholar.google.com/scholar?q=Matching+Networks+for+One+Shot+Learning 8. Prototypical Networks for Few-shot Learning — Jake Snell, Kevin Swersky, Richard Zemel, 2017 https://scholar.google.com/scholar?q=Prototypical+Networks+for+Few-shot+Learning 9. A Closer Look at Few-shot Classification — Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, Jia-Bin Huang, 2019 https://scholar.google.com/scholar?q=A+Closer+Look+at+Few-shot+Classification 10. Generalizing from a Few Examples — Yaqing Wang, Quanming Yao, James T. Kwok, Lionel M. Ni, 2020 https://scholar.google.com/scholar?q=Generalizing+from+a+Few+Examples 11. Meta-SGD: Learning to Learn Quickly for Few-Shot Learning — Zhenguo Li, Fengwei Zhou, Fei Chen, Hang Li, 2017 https://scholar.google.com/scholar?q=Meta-SGD:+Learning+to+Learn+Quickly+for+Few-Shot+Learning 12. On First-Order Meta-Learning Algorithms — Alex Nichol, Joshua Achiam, John Schulman, 2018 https://scholar.google.com/scholar?q=On+First-Order+Meta-Learning+Algorithms 13. How to Train Your MAML — Antreas Antoniou, Harrison Edwards, Amos Storkey, 2019 https://scholar.google.com/scholar?q=How+to+Train+Your+MAML 14. Meta-learning with Differentiable Closed-Form Solvers — Luca Bertinetto, Joao F. Henriques, Philip H. S. Torr, Andrea Vedaldi, 2018 https://scholar.google.com/scholar?q=Meta-learning+with+Differentiable+Closed-Form+Solvers 15. Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables — Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, Deirdre Quillen, 2019 https://scholar.google.com/scholar?q=Efficient+Off-Policy+Meta-Reinforcement+Learning+via+Probabilistic+Context+Variables 16. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning — Ronald J. Williams, 1992 https://scholar.google.com/scholar?q=Simple+Statistical+Gradient-Following+Algorithms+for+Connectionist+Reinforcement+Learning 17. Trust Region Policy Optimization — John Schulman, Sergey Levine, Philipp Moritz, Michael Jordan, Pieter Abbeel, 2015 https://scholar.google.com/scholar?q=Trust+Region+Policy+Optimization 18. Proximal Policy Optimization Algorithms — John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov, 2017 https://scholar.google.com/scholar?q=Proximal+Policy+Optimization+Algorithms 19. Meta-Learning with Memory-Augmented Neural Networks — Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap, 2016 https://scholar.google.com/scholar?q=Meta-Learning+with+Memory-Augmented+Neural+Networks 20. Learning to Learn by Gradient Descent by Gradient Descent — Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W. Hoffman, David Pfau, Tom Schaul, Nando de Freitas, 2016 https://scholar.google.com/scholar?q=Learning+to+Learn+by+Gradient+Descent+by+Gradient+Descent 21. Transformers learn in-context by gradient descent — Johannes von Oswald et al., 2022 https://scholar.google.com/scholar?q=Transformers+learn+in-context+by+gradient+descent 22. In-context Learning and Gradient Descent Revisited — Gilad Deutch et al., 2023 https://scholar.google.com/scholar?q=In-context+Learning+and+Gradient+Descent+Revisited 23. Low-Rank Few-Shot Adaptation of Vision-Language Models — Maxime Zanella and Ismail Ben Ayed, 2024 https://scholar.google.com/scholar?q=Low-Rank+Few-Shot+Adaptation+of+Vision-Language+Models 24. Meta-Adapter: An Online Few-shot Learner for Vision-Language Model — Cheng Cheng et al., 2023 https://scholar.google.com/scholar?q=Meta-Adapter:+An+Online+Few-shot+Learner+for+Vision-Language+Model 25. Cross-Domain Few-Shot Learning via Adaptive Transformer Networks — Naeem Paeedeh et al., 2024 https://scholar.google.com/scholar?q=Cross-Domain+Few-Shot+Learning+via+Adaptive+Transformer+Networks 26. Few-shot Adaptation of Multi-modal Foundation Models: A Survey — Fan Liu et al., 2024 https://scholar.google.com/scholar?q=Few-shot+Adaptation+of+Multi-modal+Foundation+Models:+A+Survey 27. AI Post Transformers: In-Context Learning as Implicit Learning Algorithms — Hal Turing & Dr. Ada Shannon, Wed, https://podcast.do-not-panic.com/episodes/in-context-learning-as-implicit-learning-algorithms/ 28. AI Post Transformers: NVIDIA: TTT-E2E: Unlocking Long-Context Learning via End-to-End Test-Time Training — Hal Turing & Dr. Ada Shannon, Sat, https://podcast.do-not-panic.com/episodes/nvidia-ttt-e2e-unlocking-long-context-learning-via-end-to-end-test-time-training/ 29. AI Post Transformers: Zero-Shot Context Generalization in Reinforcement Learning from Few Training Contexts — Hal Turing & Dr. Ada Shannon, Tue, https://podcast.do-not-panic.com/episodes/zero-shot-context-generalization-in-reinforcement-learning-from-few-training-con/ 30. AI Post Transformers: A 2024 Survey Analyzing Generalization in Deep Reinforcement Learning — Hal Turing & Dr. Ada Shannon, Fri, https://podcast.do-not-panic.com/episodes/a-2024-survey-analyzing-generalization-in-deep-reinforcement-learning/ Interactive Visualization: MAML and the Basics of Meta-Learning

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AI-generated podcast where hosts Hal Turing and Dr. Ada Shannon discuss the latest research papers and reports in machine learning, AI systems, and optimization. Featuring honest critical analysis, proper citations, and nerdy humor.

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    mcgrof
  • 방송 연도
    2025년 - 2026년
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    501
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    AI Post Transformers

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