AI Post Transformers

By 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, pr... more

· Technology

Download on the App Store

Download on the App Store

Get it on Google Play

FAQs about AI Post Transformers:

How many episodes does AI Post Transformers have?

The podcast currently has 807 episodes available.

AI Post Transformers episodes:

June 10, 2026 Predictive Query Language for Relational Databases
This episode explores Predictive Query Language (PQL), a SQL-shaped domain-specific language for defining supervised learning tasks directly over relational databases by specifying the prediction target, entity, and future time horizon in one declarative statement. It explains why training label generation is often the real bottleneck in applied machine learning, unpacking concepts like prediction entities, relational learning, anchor times, point-in-time consistency, and information leakage. The discussion compares PQL to earlier work on prediction engineering and relational deep learning, arguing that its main contribution is not a new model but a more disciplined way to construct temporally valid prediction problems from messy, multi-table operational data. Listeners interested in real-world ML systems will find it interesting because it focuses on the part most papers skip: how to ask the predictive question correctly when database history is incomplete, revised, and easy to misuse.
Sources:
1. Predictive Query Language for Relational Databases
https://arxiv.org/pdf/2602.09572
2. Declarative Machine Learning - A Classification of Basic Properties and Types — Matthias Boehm, Alexandre V. Evfimievski, Niketan Pansare, Berthold Reinwald, 2016
https://arxiv.org/abs/1605.05826
3. The MADlib Analytics Library or MAD Skills, the SQL — Joseph M. Hellerstein, Christopher Re, Florian Schoppmann, Daisy Zhe Wang, et al., 2012
https://arxiv.org/abs/1208.4165
4. MLog: Towards Declarative In-Database Machine Learning — Xupeng Li, Bin Cui, Yiru Chen, Wentao Wu, Ce Zhang, 2017
https://www.vldb.org/pvldb/vol10/p1933-zhang.pdf
5. sql4ml A declarative end-to-end workflow for machine learning — Nantia Makrynioti, Ruy Ley-Wild, Vasilis Vassalos, 2019
https://arxiv.org/abs/1907.12415
6. Deep Feature Synthesis: Towards Automating Data Science Endeavors — James Max Kanter, Kalyan Veeramachaneni, 2015
https://doi.org/10.1109/DSAA.2015.7344858
7. Label, Segment, Featurize: A Cross Domain Framework for Prediction Engineering — James Max Kanter, Owen Gillespie, Kalyan Veeramachaneni, 2016
https://www.maxkanter.com/papers/DSAA_LSF_2016.pdf
8. Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases — Vid Kocijan, Jinu Sunil, Jan Eric Lenssen, Viman Deb, et al., 2026
https://arxiv.org/abs/2602.09572
9. RelBench: A Benchmark for Deep Learning on Relational Databases — Joshua Robinson, Rishabh Ranjan, Weihua Hu, Kexin Huang, et al., 2024
https://arxiv.org/abs/2407.20060
10. Temporal features in SQL:2011 — Krishna Kulkarni, Jan-Eike Michels, 2012
https://sigmodrecord.org/publications/sigmodRecord/1209/pdfs/07.industry.kulkarni.pdf
11. Time Travel and Provenance for Machine Learning Pipelines — Alexandru A. Ormenisan, Moritz Meister, Fabio Buso, Robin Andersson, Seif Haridi, Jim Dowling, 2020
https://www.usenix.org/conference/opml20/presentation/ormenisan
12. Optimizing Data Pipelines for Machine Learning in Feature Stores — Rui Liu, Kwanghyun Park, Fotis Psallidas, Xiaoyong Zhu, et al., 2023
https://www.microsoft.com/en-us/research/publication/optimizing-data-pipelines-for-machine-learning-in-feature-stores/
13. The Hopsworks Feature Store for Machine Learning — Javier de la Rua Martinez, Fabio Buso, Antonios Kouzoupis, Alexandru A. Ormenisan, et al., 2024
https://content.hopsworks.ai/hubfs/The_Hopsworks_Feature_Store_for_Machine_Learning.pdf
14. Leakage in Data Mining: Formulation, Detection, and Avoidance — Shachar Kaufman, Saharon Rosset, Claudia Perlich, 2012
https://doi.org/10.1145/2020408.2020496
15. How to avoid machine learning pitfalls: a guide for academic researchers — Michael A. Lones, 2021
https://arxiv.org/abs/2108.02497
16. Leakage and the reproducibility crisis in machine-learning-based science — Sayash Kapoor, Arvind Narayanan, 2023
https://doi.org/10.1016/j.patter.2023.100804
17. MLearn: A Declarative Machine Learning Language for Database Systems — Maximilian E. Schuele, Matthias Bungeroth, Alfons Kemper, Stephan Guennemann, Thomas Neumann, 2019
https://scholar.google.com/scholar?q=MLearn%3A+A+Declarative+Machine+Learning+Language+for+Database+Systems
18. End-to-end Optimization of Machine Learning Prediction Queries — Kwanghyun Park, Karla Saur, Dalitso Banda, Rathijit Sen, Matteo Interlandi, Konstantinos Karanasos, 2022
https://scholar.google.com/scholar?q=End-to-end+Optimization+of+Machine+Learning+Prediction+Queries
19. Relational Deep Learning: Graph Representation Learning on Relational Databases — Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, Jure Leskovec, 2023
https://scholar.google.com/scholar?q=Relational+Deep+Learning%3A+Graph+Representation+Learning+on+Relational+Databases
20. KumoRFM: A Foundation Model for In-Context Learning on Relational Data — Matthias Fey, Vid Kocijan, Federico Lopez, Jan Eric Lenssen, Jure Leskovec, 2025
https://scholar.google.com/scholar?q=KumoRFM%3A+A+Foundation+Model+for+In-Context+Learning+on+Relational+Data
21. Toward a Declarative Query Language for Machine Learning — Hasan M. Jamil, 2024
https://vldb.org/workshops/2024/proceedings/TaDA/TaDA.2.pdf
22. Implementing a Declarative Query Language for High Level Machine Learning Application Design — Hasan Rahman, Hasan M. Jamil, 2025
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5210204
23. LinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQL — Yihan Wang, Peiyu Liu, Xin Yang, 2025
https://arxiv.org/abs/2503.18596
24. E-SQL: Direct Schema Linking via Question Enrichment in Text-to-SQL — Hasan Alp Caferoglu, Ozgur Ulusoy, 2024
https://arxiv.org/abs/2409.16751
25. AutoLink: Autonomous Schema Exploration and Expansion for Scalable Schema Linking in Text-to-SQL at Scale — Ziyang Wang et al., 2025
https://arxiv.org/abs/2511.17190
26. From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment — Yu Zhao et al., 2023
https://arxiv.org/abs/2305.11501
27. AI Post Transformers: SGLang for Faster Structured LLM Programs — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-06-sglang-for-faster-structured-llm-program-c59f1c.mp3
28. AI Post Transformers: Caffe and the Rise of CNN Frameworks — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-06-caffe-and-the-rise-of-cnn-frameworks-cf15f3.mp3
...more
14min
June 10, 2026 KumoRFM-2 for Relational Learning at Scale
This episode explores KumoRFM-2, a relational foundation model designed to learn directly from connected database tables instead of flattening customers, orders, products, and tickets into a single feature table. It explains why relational learning matters for enterprise tasks such as churn, fraud, and demand prediction, arguing that flattening often erases multi-hop relationships, repeated interactions, and temporal patterns that carry the real signal. The discussion centers on KumoRFM-2’s main technical claim: a two-stage, task-conditioned attention pipeline that first selects relevant information within each table and then aggregates evidence across foreign-key neighborhoods and labeled in-context examples derived from predictive queries. Listeners would find it interesting because it connects a very practical data-engineering pain point to a broader question about whether pretrained, database-native models can beat hand-built tabular pipelines without cheating on time-aware prediction.
Sources:
1. KumoRFM-2: Scaling Foundation Models for Relational Learning — Valter Hudovernik, Federico López, Vid Kocijan, Akihiro Nitta, Jan Eric Lenssen, Jure Leskovec, Matthias Fey, 2026
http://arxiv.org/abs/2604.12596
2. Learning Probabilistic Relational Models — Nir Friedman, Lise Getoor, Daphne Koller, Avi Pfeffer, 1999
https://scholar.google.com/scholar?q=Learning+Probabilistic+Relational+Models
3. Relational Inductive Biases, Deep Learning, and Graph Networks — Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Yujia Li, Razvan Pascanu, et al., 2018
https://scholar.google.com/scholar?q=Relational+Inductive+Biases%2C+Deep+Learning%2C+and+Graph+Networks
4. Relational Deep Learning: Graph Representation Learning on Relational Databases — Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, Jure Leskovec, 2023
https://scholar.google.com/scholar?q=Relational+Deep+Learning%3A+Graph+Representation+Learning+on+Relational+Databases
5. RelBench: A Benchmark for Deep Learning on Relational Databases — Joshua Robinson, Rishabh Ranjan, Weihua Hu, Kexin Huang, Jiaqi Han, Alejandro Dobles, Matthias Fey, Jan E. Lenssen, Jure Leskovec, et al., 2024
https://scholar.google.com/scholar?q=RelBench%3A+A+Benchmark+for+Deep+Learning+on+Relational+Databases
6. Position: Why Tabular Foundation Models Should Be a Research Priority — Boris Van Breugel, Mihaela Van Der Schaar, 2024
https://scholar.google.com/scholar?q=Position%3A+Why+Tabular+Foundation+Models+Should+Be+a+Research+Priority
7. Accurate Predictions on Small Data with a Tabular Foundation Model — Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter, et al., 2025
https://scholar.google.com/scholar?q=Accurate+Predictions+on+Small+Data+with+a+Tabular+Foundation+Model
8. TabICL: A Tabular Foundation Model for In-Context Learning on Large Data — Jingang Qu, David Holzmüller, Gaël Varoquaux, Marine Le Morvan, 2025
https://scholar.google.com/scholar?q=TabICL%3A+A+Tabular+Foundation+Model+for+In-Context+Learning+on+Large+Data
9. KumoRFM: A Foundation Model for In-Context Learning on Relational Data — Matthias Fey, Vid Kocijan, Federico Lopez, Jan Eric Lenssen, Jure Leskovec, 2025
https://scholar.google.com/scholar?q=KumoRFM%3A+A+Foundation+Model+for+In-Context+Learning+on+Relational+Data
10. PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models — Vignesh Kothapalli et al., 2026
https://scholar.google.com/scholar?q=PluRel%3A+Synthetic+Data+unlocks+Scaling+Laws+for+Relational+Foundation+Models
11. Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data — Rishabh Ranjan et al., 2026
https://scholar.google.com/scholar?q=Relational+Transformer%3A+Toward+Zero-Shot+Foundation+Models+for+Relational+Data
12. No Need to Train Your RDB Foundation Model — Linjie Xu, Yanlin Zhang, Quan Gan, Minjie Wang, and David Wipf, 2026
https://scholar.google.com/scholar?q=No+Need+to+Train+Your+RDB+Foundation+Model
13. TabICLv2: A better, faster, scalable, and open tabular foundation model — Jingang Qu, David Holzmuller, Gael Varoquaux, and Marine Le Morvan, 2026
https://scholar.google.com/scholar?q=TabICLv2%3A+A+better%2C+faster%2C+scalable%2C+and+open+tabular+foundation+model
14. Griffin: Towards a Graph-Centric Relational Database Foundation Model — Yanbo Wang, Xiyuan Wang, Quan Gan, Minjie Wang, Qibin Yang, David Wipf, and Muhan Zhang, 2025
https://scholar.google.com/scholar?q=Griffin%3A+Towards+a+Graph-Centric+Relational+Database+Foundation+Model
15. Graph Machine Learning Meets Multi-Table Relational Data — Quan Gan, Minjie Wang, David Wipf, Christos Faloutsos, 2024
https://scholar.google.com/scholar?q=Graph+Machine+Learning+Meets+Multi-Table+Relational+Data
16. Large Scale Transfer Learning for Tabular Data via Language Modeling — Josh Gardner, Juan C. Perdomo, Ludwig Schmidt, 2024
https://scholar.google.com/scholar?q=Large+Scale+Transfer+Learning+for+Tabular+Data+via+Language+Modeling
17. Towards Synthetic Data for Fine-tuning Tabular Foundation Models — Magnus Buhler, Lennart Purucker, Frank Hutter, 2025
https://scholar.google.com/scholar?q=Towards+Synthetic+Data+for+Fine-tuning+Tabular+Foundation+Models
18. Range-limited Augmentation for Few-shot Learning in Tabular Data with Comprehensive Benchmark — Kyungeun Lee et al., 2025
https://scholar.google.com/scholar?q=Range-limited+Augmentation+for+Few-shot+Learning+in+Tabular+Data+with+Comprehensive+Benchmark
19. AI Post Transformers: Muon Is Scalable for LLM Training — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-25-muon-is-scalable-for-llm-training-587ed8.mp3
20. AI Post Transformers: Scaling Laws for Multilingual Code Pretraining — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-15-scaling-laws-for-multilingual-code-pretr-7d220e.mp3
...more
14min
June 10, 2026 Unified Neural Scaling Laws Across Regimes
This episode explores Unified Neural Scaling Laws, a framework for predicting model performance when parameter count, data volume, training steps, inference compute, and training-recipe choices all change at once. It explains how the paper moves beyond classic smooth power-law curves by introducing broken multivariate scaling laws with regime shifts, including hyperbreaks, bottleneck versus non-bottleneck components, and joint interaction surfaces across training variables. The discussion highlights the paper’s argument that good forecasting must capture both beneficial scaling effects and harmful effects such as overfitting, bad hyperparameter regimes, and data or compute limits, rather than assuming one clean trend forever. A listener would find it interesting because it ties abstract scaling-law math directly to expensive real-world training decisions and to the question of whether pretraining gains actually transfer to downstream benchmarks.
Sources:
1. Unified Neural Scaling Laws — Ethan Caballero, Priyank Jaini, David Krueger, Irina Rish, 2026
http://arxiv.org/abs/2605.26248
2. A Constructive Prediction of the Generalization Error Across Scales — Jonathan S. Rosenfeld, Amir Rosenfeld, Yonatan Belinkov, Nir Shavit, 2020
https://scholar.google.com/scholar?q=A+Constructive+Prediction+of+the+Generalization+Error+Across+Scales
3. Scaling Laws for Neural Language Models — Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, et al., 2020
https://scholar.google.com/scholar?q=Scaling+Laws+for+Neural+Language+Models
4. Training Compute-Optimal Large Language Models — Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, et al., 2022
https://scholar.google.com/scholar?q=Training+Compute-Optimal+Large+Language+Models
5. Unified Neural Scaling Laws — Ethan Caballero, Priyank Jaini, David Krueger, Irina Rish, 2026
https://scholar.google.com/scholar?q=Unified+Neural+Scaling+Laws
6. Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour — Priya Goyal, Piotr Dollar, Ross Girshick, Kaiming He, et al., 2017
https://scholar.google.com/scholar?q=Accurate%2C+Large+Minibatch+SGD%3A+Training+ImageNet+in+1+Hour
7. An Empirical Model of Large-Batch Training — Sam McCandlish, Jared Kaplan, Dario Amodei, OpenAI Dota Team, 2018
https://scholar.google.com/scholar?q=An+Empirical+Model+of+Large-Batch+Training
8. Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer — Greg Yang, Edward J. Hu, Igor Babuschkin, Szymon Sidor, et al., 2022
https://scholar.google.com/scholar?q=Tensor+Programs+V%3A+Tuning+Large+Neural+Networks+via+Zero-Shot+Hyperparameter+Transfer
9. Resolving Discrepancies in Compute-Optimal Scaling of Language Models — Tomer Porian, Mitchell Wortsman, Jenia Jitsev, Ludwig Schmidt, Yair Carmon, 2024
https://scholar.google.com/scholar?q=Resolving+Discrepancies+in+Compute-Optimal+Scaling+of+Language+Models
10. Reconciling modern machine learning practice and the bias-variance trade-off — Mikhail Belkin, Daniel Hsu, Siyuan Ma, Soumik Mandal, 2019
https://scholar.google.com/scholar?q=Reconciling+modern+machine+learning+practice+and+the+bias-variance+trade-off
11. Deep Double Descent: Where Bigger Models and More Data Hurt — Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever, 2020
https://scholar.google.com/scholar?q=Deep+Double+Descent%3A+Where+Bigger+Models+and+More+Data+Hurt
12. Broken Neural Scaling Laws — Ethan Caballero, Kshitij Gupta, Irina Rish, David Krueger, 2023
https://scholar.google.com/scholar?q=Broken+Neural+Scaling+Laws
13. Scaling Data-Constrained Language Models — Niklas Muennighoff, Alexander M. Rush, Boaz Barak, et al., 2023
https://scholar.google.com/scholar?q=Scaling+Data-Constrained+Language+Models
14. Observational Scaling Laws and the Predictability of Language Model Performance — Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto, 2024
https://scholar.google.com/scholar?q=Observational+Scaling+Laws+and+the+Predictability+of+Language+Model+Performance
15. Scaling Laws for Data Filtering -- Data Curation cannot be Compute Agnostic — Sachin Goyal et al., 2024
https://scholar.google.com/scholar?q=Scaling+Laws+for+Data+Filtering+--+Data+Curation+cannot+be+Compute+Agnostic
16. Revisiting Scaling Laws for Language Models: The Role of Data Quality and Training Strategies — Zhengyu Chen et al., 2025
https://scholar.google.com/scholar?q=Revisiting+Scaling+Laws+for+Language+Models%3A+The+Role+of+Data+Quality+and+Training+Strategies
17. Scaling Laws for Optimal Data Mixtures — Mustafa Shukor et al., 2025
https://scholar.google.com/scholar?q=Scaling+Laws+for+Optimal+Data+Mixtures
18. Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient — Jan Ludziejewski et al., 2025
https://scholar.google.com/scholar?q=Joint+MoE+Scaling+Laws%3A+Mixture+of+Experts+Can+Be+Memory+Efficient
19. Towards Thinking-Optimal Scaling of Test-Time Compute for LLM Reasoning — Wenkai Yang et al., 2025
https://scholar.google.com/scholar?q=Towards+Thinking-Optimal+Scaling+of+Test-Time+Compute+for+LLM+Reasoning
20. Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach — Jonas Geiping et al., 2025
https://scholar.google.com/scholar?q=Scaling+up+Test-Time+Compute+with+Latent+Reasoning%3A+A+Recurrent+Depth+Approach
21. Towards Robust Scaling Laws for Optimizers — Alexandra Volkova et al., 2026
https://scholar.google.com/scholar?q=Towards+Robust+Scaling+Laws+for+Optimizers
22. Neural Scaling Laws Rooted in the Data Distribution — Ari Brill, 2024
https://scholar.google.com/scholar?q=Neural+Scaling+Laws+Rooted+in+the+Data+Distribution
23. The Quantization Model of Neural Scaling — Eric J. Michaud et al., 2023
https://scholar.google.com/scholar?q=The+Quantization+Model+of+Neural+Scaling
24. AI Post Transformers: Muon Is Scalable for LLM Training — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-25-muon-is-scalable-for-llm-training-587ed8.mp3
25. AI Post Transformers: TMAS: Scaling Test-Time Compute with Multi-Agent Synergy — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-14-tmas-scaling-test-time-compute-with-mult-3abe7a.mp3
26. AI Post Transformers: Trajectory Summaries for Long-Horizon Coding Agents — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-24-trajectory-summaries-for-long-horizon-co-0194be.mp3
Interactive Visualization: Unified Neural Scaling Laws Across Regimes
...more
0min
June 10, 2026 RAGEN-2: Reasoning Collapse in Agentic RL
This episode explores the RAGEN-2 paper’s claim that agentic reinforcement learning can produce reasoning traces that look active and diverse while losing real dependence on the input. It explains the paper’s central distinction between ordinary entropy, which measures diversity within a single prompt, and template collapse, where traces across many different prompts become generic variations of the same pattern. The discussion also covers the proposed mutual-information-style monitoring approach, which rescoring traces against other prompts to test whether reasoning remains identifiable to its source, and links the failure mode to weak reward signal, PPO-style regularization, and sparse long-horizon feedback. Listeners would find it interesting because it reframes a core question in reasoning RL: not whether an agent looks busy, but whether its reasoning is still actually about the problem in front of it.
Sources:
1. RAGEN-2: Reasoning Collapse in Agentic RL — Zihan Wang, Chi Gui, Xing Jin, Qineng Wang, Licheng Liu, Kangrui Wang, Shiqi Chen, Linjie Li, Zhengyuan Yang, Pingyue Zhang, Yiping Lu, Jiajun Wu, Li Fei-Fei, Lijuan Wang, Yejin Choi, Manling Li, 2026
http://arxiv.org/abs/2604.06268
2. MINE: Mutual Information Neural Estimation — Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, R. Devon Hjelm, 2018
https://scholar.google.com/scholar?q=MINE%3A+Mutual+Information+Neural+Estimation
3. Representation Learning with Contrastive Predictive Coding — Aaron van den Oord, Yazhe Li, Oriol Vinyals, 2018
https://scholar.google.com/scholar?q=Representation+Learning+with+Contrastive+Predictive+Coding
4. Learning deep representations by mutual information estimation and maximization — R. Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Phil Bachman, Adam Trischler, Yoshua Bengio, 2019
https://scholar.google.com/scholar?q=Learning+deep+representations+by+mutual+information+estimation+and+maximization
5. RAGEN-2: Reasoning Collapse in Agentic RL — Zihan Wang, Chi Gui, Xing Jin, Qineng Wang, Manling Li, et al., 2026
https://scholar.google.com/scholar?q=RAGEN-2%3A+Reasoning+Collapse+in+Agentic+RL
6. 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
7. Signal-to-Noise Ratio Analysis of Policy Gradient Algorithms — John W. Roberts, Russ Tedrake, 2008
https://scholar.google.com/scholar?q=Signal-to-Noise+Ratio+Analysis+of+Policy+Gradient+Algorithms
8. Understanding the Impact of Entropy on Policy Optimization — Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans, 2019
https://scholar.google.com/scholar?q=Understanding+the+Impact+of+Entropy+on+Policy+Optimization
9. Prioritized Experience Replay — Tom Schaul, John Quan, Ioannis Antonoglou, David Silver, 2016
https://scholar.google.com/scholar?q=Prioritized+Experience+Replay
10. DAPO: An Open-Source LLM Reinforcement Learning System at Scale — Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, et al., 2025
https://scholar.google.com/scholar?q=DAPO%3A+An+Open-Source+LLM+Reinforcement+Learning+System+at+Scale
11. No Prompt Left Behind: Exploiting Zero-Variance Prompts in LLM Reinforcement Learning via Entropy-Guided Advantage Shaping — Thanh-Long V. Le, Myeongho Jeon, Kim Vu, Viet Lai, Eunho Yang, 2025
https://scholar.google.com/scholar?q=No+Prompt+Left+Behind%3A+Exploiting+Zero-Variance+Prompts+in+LLM+Reinforcement+Learning+via+Entropy-Guided+Advantage+Shaping
12. RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning — Zihan Wang et al., 2025
https://scholar.google.com/scholar?q=RAGEN%3A+Understanding+Self-Evolution+in+LLM+Agents+via+Multi-Turn+Reinforcement+Learning
13. The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models — Ganqu Cui et al., 2025
https://scholar.google.com/scholar?q=The+Entropy+Mechanism+of+Reinforcement+Learning+for+Reasoning+Language+Models
14. ASTER: Agentic Scaling with Tool-integrated Extended Reasoning — Xuqin Zhang, Quan He, Zhenrui Zheng, Zongzhang Zhang, Xu He, and Dong Li, 2026
https://scholar.google.com/scholar?q=ASTER%3A+Agentic+Scaling+with+Tool-integrated+Extended+Reasoning
15. Demystifying Reinforcement Learning in Agentic Reasoning — Zhaochen Yu, Ling Yang, Jiaru Zou, Shuicheng Yan, and Mengdi Wang, 2025
https://scholar.google.com/scholar?q=Demystifying+Reinforcement+Learning+in+Agentic+Reasoning
16. The Price of Format: Diversity Collapse in LLMs — Longfei Yun, Chenyang An, Zilong Wang, Letian Peng, and Jingbo Shang, 2025
https://scholar.google.com/scholar?q=The+Price+of+Format%3A+Diversity+Collapse+in+LLMs
17. Revisiting Entropy in Reinforcement Learning for Large Reasoning Models — Renren Jin et al., 2025
https://scholar.google.com/scholar?q=Revisiting+Entropy+in+Reinforcement+Learning+for+Large+Reasoning+Models
18. ERPO: Token-Level Entropy-Regulated Policy Optimization for Large Reasoning Models — Song Yu and Li Li, 2026
https://scholar.google.com/scholar?q=ERPO%3A+Token-Level+Entropy-Regulated+Policy+Optimization+for+Large+Reasoning+Models
19. Demystifying Reasoning Dynamics with Mutual Information: Thinking Tokens are Information Peaks in LLM Reasoning — Chen Qian et al., 2025
https://scholar.google.com/scholar?q=Demystifying+Reasoning+Dynamics+with+Mutual+Information%3A+Thinking+Tokens+are+Information+Peaks+in+LLM+Reasoning
20. MITS: Enhanced Tree Search Reasoning for LLMs via Pointwise Mutual Information — Jiaxi Li et al., 2025
https://scholar.google.com/scholar?q=MITS%3A+Enhanced+Tree+Search+Reasoning+for+LLMs+via+Pointwise+Mutual+Information
21. On the Design of KL-Regularized Policy Gradient Algorithms for LLM Reasoning — Yifan Zhang et al., 2025
https://scholar.google.com/scholar?q=On+the+Design+of+KL-Regularized+Policy+Gradient+Algorithms+for+LLM+Reasoning
22. Rethinking KL Regularization in RLHF: From Value Estimation to Gradient Optimization — Kezhao Liu et al., 2025
https://scholar.google.com/scholar?q=Rethinking+KL+Regularization+in+RLHF%3A+From+Value+Estimation+to+Gradient+Optimization
23. What Makes a Reward Model a Good Teacher? An Optimization Perspective — Noam Razin et al., 2025
https://scholar.google.com/scholar?q=What+Makes+a+Reward+Model+a+Good+Teacher%3F+An+Optimization+Perspective
24. Accelerating RLHF Training with Reward Variance Increase — Zonglin Yang et al., 2025
https://scholar.google.com/scholar?q=Accelerating+RLHF+Training+with+Reward+Variance+Increase
25. Efficient RLVR Training via Weighted Mutual Information Data Selection — Xinyu Zhou et al., 2026
https://scholar.google.com/scholar?q=Efficient+RLVR+Training+via+Weighted+Mutual+Information+Data+Selection
26. AI Post Transformers: Stabilizing Efficient Reasoning with Step-Level Advantage Selection — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-29-stabilizing-efficient-reasoning-with-ste-1e589d.mp3
27. AI Post Transformers: Reasoning Theater and Unfaithful Chain-of-Thought — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-05-reasoning-theater-and-unfaithful-chain-o-a4507e.mp3
28. AI Post Transformers: Benchmarking Test-Time Scaling for General LLM Agents — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-22-benchmarking-test-time-scaling-for-gener-8f14f9.mp3
29. AI Post Transformers: Can Models Learn from Long Context? — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-05-can-models-learn-from-long-context-77533e.mp3
...more
0min
June 10, 2026 Latent Reasoning with Normalizing Flows
This episode explores Latent Reasoning with Normalizing Flows, a paper that asks whether a standard left-to-right transformer can do its intermediate reasoning in continuous latent states instead of spelling every step out as text. It explains how the method uses a frozen VAE during training to compress written rationales into short latent sequences, then uses shallow normalizing flows so the same autoregressive backbone can predict both latent thought slots and normal answer tokens while preserving exact likelihoods, sampling, and KV-cache-friendly decoding. The discussion highlights matched coding results on Qwen3-8B-Base, where the reported benchmark average rises from 55.8 for the base model to 68.8 for NF-CoT Unified and 70.1 after latent-space reinforcement learning, with strong pass@k gains that suggest better exploration of multiple solution paths. Listeners would find it interesting because it frames latent reasoning as a practical alternative to verbose chain-of-thought, while also noting the current evidence is still narrow, centered on one post-trained coding model and not uniformly better than diffusion baselines on every benchmark.
Sources:
1. Latent Reasoning with Normalizing Flows — Guancheng Tu, Xiangjun Fu, Suhao Yu, Yao Tang, Haoqiang Kang, Lianhui Qin, Yizhe Zhang, Jiatao Gu, 2026
http://arxiv.org/abs/2606.06447
2. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models — Jason Wei, Xuezhi Wang, Denny Zhou, Quoc Le, et al., 2022
https://scholar.google.com/scholar?q=Chain-of-Thought+Prompting+Elicits+Reasoning+in+Large+Language+Models
3. Training Large Language Models to Reason in a Continuous Latent Space — Shibo Hao, Sainbayar Sukhbaatar, Zhiting Hu, Jason Weston, Yuandong Tian, et al., 2024 preprint; COLM 2025
https://scholar.google.com/scholar?q=Training+Large+Language+Models+to+Reason+in+a+Continuous+Latent+Space
4. Reasoning Beyond Language: A Comprehensive Survey on Latent Chain-of-Thought Reasoning — Xinghao Chen, Anhao Zhao, Xiaoyu Shen, et al., 2025
https://scholar.google.com/scholar?q=Reasoning+Beyond+Language%3A+A+Comprehensive+Survey+on+Latent+Chain-of-Thought+Reasoning
5. LaDiR: Latent Diffusion Enhances LLMs for Text Reasoning — Haoqiang Kang, Yizhe Zhang, Navdeep Jaitly, Yi-An Ma, Lianhui Qin, et al., 2025
https://scholar.google.com/scholar?q=LaDiR%3A+Latent+Diffusion+Enhances+LLMs+for+Text+Reasoning
6. CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation — Zhenyi Shen, Hanqi Yan, Linhai Zhang, Zhanghao Hu, Yali Du, Yulan He, 2025
https://scholar.google.com/scholar?q=CODI%3A+Compressing+Chain-of-Thought+into+Continuous+Space+via+Self-Distillation
7. Normalizing Flows are Capable Generative Models — Shuangfei Zhai, Ruixiang Zhang, Preetum Nakkiran, David Berthelot, Jiatao Gu, Huangjie Zheng, Tianrong Chen, Miguel Angel Bautista, Navdeep Jaitly, Josh Susskind, 2024
https://scholar.google.com/scholar?q=Normalizing+Flows+are+Capable+Generative+Models
8. Reasoning by Superposition: A Theoretical Perspective on Chain of Continuous Thought — Hanlin Zhu, Shibo Hao, Zhiting Hu, Jiantao Jiao, Stuart Russell, Yuandong Tian, 2025
https://scholar.google.com/scholar?q=Reasoning+by+Superposition%3A+A+Theoretical+Perspective+on+Chain+of+Continuous+Thought
9. Dynamics Within Latent Chain-of-Thought: An Empirical Study of Causal Structure — Zirui Li, Xuefeng Bai, Kehai Chen, Yizhi Li, Jian Yang, Chenghua Lin, Min Zhang, 2026
https://scholar.google.com/scholar?q=Dynamics+Within+Latent+Chain-of-Thought%3A+An+Empirical+Study+of+Causal+Structure
10. Latent-GRPO: Group Relative Policy Optimization for Latent Reasoning — Jingcheng Deng, Zihao Wei, Liang Pang, Junhong Wu, Shicheng Xu, Zenghao Duan, Huawei Shen, 2026
https://scholar.google.com/scholar?q=Latent-GRPO%3A+Group+Relative+Policy+Optimization+for+Latent+Reasoning
11. Chain-of-Thought Matters: Improving Long-Context Language Models with Reasoning Path Supervision — Dawei Zhu et al., 2025
https://arxiv.org/abs/2502.20790
12. Supervised Chain of Thought — Xiang Zhang and Dujian Ding, 2024
https://arxiv.org/abs/2410.14198
13. Large language models can learn and generalize steganographic chain-of-thought under process supervision — Joey Skaf et al., 2025
https://arxiv.org/abs/2506.01926
14. Hybrid Latent Reasoning via Reinforcement Learning — Zhenrui Yue et al., 2025
https://arxiv.org/abs/2505.18454
15. Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach — Jonas Geiping et al., 2025
https://arxiv.org/abs/2502.05171
16. R-KV: Redundancy-aware KV Cache Compression for Reasoning Models — Zefan Cai et al., 2025
https://arxiv.org/abs/2505.24133
17. Not All Heads Matter: A Head-Level KV Cache Compression Method with Integrated Retrieval and Reasoning — Yu Fu et al., 2024
https://arxiv.org/abs/2410.19258
18. AI Post Transformers: Generative Recursive Reasoning in Latent Space — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-21-generative-recursive-reasoning-in-latent-a9371d.mp3
19. AI Post Transformers: MELT: Decoupling Compute From Memory — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-13-melt-decoupling-compute-from-memory-26430c.mp3
20. AI Post Transformers: Reasoning Theater and Unfaithful Chain-of-Thought — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-05-reasoning-theater-and-unfaithful-chain-o-a4507e.mp3
21. AI Post Transformers: Gradient Descent at Inference Time for LLM Reasoning — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-03-10-gradient-descent-at-inference-time-for-l-20617d.mp3
22. AI Post Transformers: Explicit Information Transmission for Context Compression — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-05-explicit-information-transmission-for-co-24e3c2.mp3
23. AI Post Transformers: Speculative Decoding in Real vLLM Serving — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-04-speculative-decoding-in-real-vllm-servin-6f4e2b.mp3
Interactive Visualization: Latent Reasoning with Normalizing Flows
...more
11min
June 08, 2026 EMO: Emergent Modularity in Sparse Language Models
This episode explores EMO, a Mixture-of-Experts language model that tries to turn token-level sparsity into real modularity by letting coherent expert groups emerge from document structure during pretraining. It explains why standard MoEs are not automatically deployable as smaller task-specific slices, then walks through EMO’s main idea: each document routes tokens through a learned document-specific pool of experts rather than the full expert set, with global load balancing to keep training stable. The discussion highlights that EMO was trained at substantial scale and reportedly matches a conventional MoE as a full model, while retaining surprisingly strong performance when only a fraction of experts are kept in memory for a given task. A listener would find it interesting because it connects a concrete systems problem, serving large models under tight memory budgets, to a plausible path toward more reusable, domain-specialized LLM components.
Sources:
1. EMO: Emergent Modularity in Sparse Language Models
https://allenai.org/papers/emo
2. Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer — Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean, 2017
https://scholar.google.com/scholar?q=Outrageously+Large+Neural+Networks%3A+The+Sparsely-Gated+Mixture-of-Experts+Layer
3. A Review of Sparse Expert Models in Deep Learning — William Fedus, Jeff Dean, Barret Zoph, 2022
https://scholar.google.com/scholar?q=A+Review+of+Sparse+Expert+Models+in+Deep+Learning
4. ST-MoE: Designing Stable and Transferable Sparse Expert Models — Barret Zoph, Irwan Bello, Sameer Kumar, Nan Du, Yanping Huang, Jeff Dean, Noam Shazeer, William Fedus, 2022
https://scholar.google.com/scholar?q=ST-MoE%3A+Designing+Stable+and+Transferable+Sparse+Expert+Models
5. DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models — Damai Dai, Chengqi Deng, Chenggang Zhao, Huazuo Gao, Deli Chen, Jiashi Li, Chong Ruan, Zhifang Sui, Wenfeng Liang, 2024
https://scholar.google.com/scholar?q=DeepSeekMoE%3A+Towards+Ultimate+Expert+Specialization+in+Mixture-of-Experts+Language+Models
6. Neural Module Networks — Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein, 2015
https://scholar.google.com/scholar?q=Neural+Module+Networks
7. AdapterFusion: Non-Destructive Task Composition for Transfer Learning — Jonas Pfeiffer, Aishwarya Kamath, Andreas Rucklé, Kyunghyun Cho, Iryna Gurevych, 2021
https://scholar.google.com/scholar?q=AdapterFusion%3A+Non-Destructive+Task+Composition+for+Transfer+Learning
8. Sparsely Activated Mixture-of-Experts are Robust Multi-Task Learners — Shashank Gupta, Subhabrata Mukherjee, Krishan Subudhi, Eduardo Gonzalez, Damien Jose, Ahmed H. Awadallah, Jianfeng Gao, 2022
https://scholar.google.com/scholar?q=Sparsely+Activated+Mixture-of-Experts+are+Robust+Multi-Task+Learners
9. GLaM: Efficient Scaling of Language Models with Mixture-of-Experts — Nan Du, Yanping Huang, Andrew Dai, Sebastian Goodman, Orhan Firat, Quoc Le, Yonghui Wu, Zhifeng Chen, Claire Cui, et al., 2021
https://scholar.google.com/scholar?q=GLaM%3A+Efficient+Scaling+of+Language+Models+with+Mixture-of-Experts
10. OLMoE: Open Mixture-of-Experts Language Models — Niklas Muennighoff et al., 2024
https://scholar.google.com/scholar?q=OLMoE%3A+Open+Mixture-of-Experts+Language+Models
11. Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM — Sainbayar Sukhbaatar et al., 2024
https://scholar.google.com/scholar?q=Branch-Train-MiX%3A+Mixing+Expert+LLMs+into+a+Mixture-of-Experts+LLM
12. FlexOlmo: Open Language Models for Flexible Data Use — Weijia Shi et al., 2025
https://scholar.google.com/scholar?q=FlexOlmo%3A+Open+Language+Models+for+Flexible+Data+Use
13. Emergent Modularity in Pre-trained Transformers — Zhengyan Zhang et al., 2023
https://scholar.google.com/scholar?q=Emergent+Modularity+in+Pre-trained+Transformers
14. Domain-Specific Pruning of Large Mixture-of-Experts Models with Few-shot Demonstrations — Zican Dong et al., 2025
https://scholar.google.com/scholar?q=Domain-Specific+Pruning+of+Large+Mixture-of-Experts+Models+with+Few-shot+Demonstrations
15. Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models — Xudong Lu et al., 2024
https://arxiv.org/abs/2402.14800
16. Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs — Enshu Liu et al., 2024
https://arxiv.org/abs/2407.00945
17. The Myth of Expert Specialization in MoEs: Why Routing Reflects Geometry, Not Necessarily Domain Expertise — Xi Wang, Soufiane Hayou, Eric Nalisnick, 2026
https://arxiv.org/abs/2604.09780
18. Dynamic Expert Specialization: Towards Catastrophic Forgetting-Free Multi-Domain MoE Adaptation — Junzhuo Li et al., 2025
https://arxiv.org/abs/2509.16882
19. AI Post Transformers: Batch-Aware Expert Routing for Faster MoE Decoding — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-04-batch-aware-expert-routing-for-faster-mo-683ab6.mp3
20. AI Post Transformers: JANUS for Scalable MoE Inference — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-15-janus-for-scalable-moe-inference-78ae30.mp3
21. AI Post Transformers: Nemotron 3 Super Hybrid Mamba-Transformer MoE — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-19-nemotron-3-super-hybrid-mamba-transforme-31ac75.mp3
...more
12min
June 05, 2026 When AI Builds Itself and Recursive Self-Improvement
This episode explores Marina Favaro et al.’s 2026 paper on whether AI is beginning to accelerate frontier AI development enough to hint at recursive self-improvement, laying out the ladder from chatbots and coding agents to systems that materially help build their own successors. It distinguishes progress on long-horizon, fixed-goal engineering tasks from the harder problem of genuine research judgment, using public evidence such as METR, CORE-Bench, and RE-Bench to argue that AI is clearly getting better at sustained execution but has not yet shown strong scientific taste or reliable autonomy. It then digs into Anthropic’s internal evidence, including claims that by May 2026 Claude was responsible for over 80% of merged production code, open-ended coding-task success had risen sharply, and fixed-goal research engineering tasks improved from roughly 3x to 52x over a year, while the speakers repeatedly stress that code volume and self-reported productivity overstate true impact. Listeners would find it interesting because the discussion ties concrete benchmark results and lab productivity numbers to the bigger question of whether faster AI development also compresses the timelines for safety, governance, and the arrival of more capable successor systems.
Sources:
1. When AI Builds Itself and Recursive Self-Improvement
https://www.anthropic.com/institute/recursive-self-improvement
2. Measuring AI Ability to Complete Long Tasks — Thomas Kwa, Ben West, Joel Becker, Amy Deng, et al., 2025
https://scholar.google.com/scholar?q=Measuring+AI+Ability+to+Complete+Long+Tasks
3. RE-Bench: Evaluating Frontier AI R&D Capabilities of Language Model Agents against Human Experts — Hjalmar Wijk, Tao Roa Lin, Joel Becker, Sami Jawhar, et al., 2025
https://scholar.google.com/scholar?q=RE-Bench%3A+Evaluating+Frontier+AI+R%26D+Capabilities+of+Language+Model+Agents+against+Human+Experts
4. CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark — Zachary S. Siegel, Sayash Kapoor, Nitya Nadgir, Benedikt Stroebl, Arvind Narayanan, 2024
https://scholar.google.com/scholar?q=CORE-Bench%3A+Fostering+the+Credibility+of+Published+Research+Through+a+Computational+Reproducibility+Agent+Benchmark
5. Automated Weak-to-Strong Researcher — Jiaxin Wen, Liang Qiu, Joe Benton, Jan Hendrik Kirchner, Jan Leike, 2026
https://scholar.google.com/scholar?q=Automated+Weak-to-Strong+Researcher
6. PaperBench: Evaluating AI's Ability to Replicate AI Research — Giulio Starace, Oliver Jaffe, Dane Sherburn, James Aung, et al., 2025
https://scholar.google.com/scholar?q=PaperBench%3A+Evaluating+AI%27s+Ability+to+Replicate+AI+Research
7. Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity — Joel Becker, Nate Rush, Elizabeth Barnes, David Rein, 2025
https://scholar.google.com/scholar?q=Measuring+the+Impact+of+Early-2025+AI+on+Experienced+Open-Source+Developer+Productivity
8. SWE-bench Goes Live! — Linghao Zhang et al., 2025
https://scholar.google.com/scholar?q=SWE-bench+Goes+Live%21
9. Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving — Daoguang Zan et al., 2025
https://scholar.google.com/scholar?q=Multi-SWE-bench%3A+A+Multilingual+Benchmark+for+Issue+Resolving
10. SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains? — John Yang et al., 2024
https://scholar.google.com/scholar?q=SWE-bench+Multimodal%3A+Do+AI+Systems+Generalize+to+Visual+Software+Domains%3F
11. Can AI Conduct Autonomous Scientific Research? Case Studies on Two Real-World Tasks — S. Agrawal et al., 2026
https://scholar.google.com/scholar?q=Can+AI+Conduct+Autonomous+Scientific+Research%3F+Case+Studies+on+Two+Real-World+Tasks
12. Collapse of Self-trained Language Models — David Herel and Tomas Mikolov, 2024
https://scholar.google.com/scholar?q=Collapse+of+Self-trained+Language+Models
13. Exploring Automation Bias in Human-AI Collaboration: A Review and Implications for Explainable AI — G. Romeo and D. Conti, 2025
https://scholar.google.com/scholar?q=Exploring+Automation+Bias+in+Human-AI+Collaboration%3A+A+Review+and+Implications+for+Explainable+AI
14. AI Post Transformers: ASI-Evolve for Data, Architectures, and RL — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-05-asi-evolve-for-data-architectures-and-rl-197b2b.mp3
15. AI Post Transformers: Trajectory Summaries for Long-Horizon Coding Agents — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-24-trajectory-summaries-for-long-horizon-co-0194be.mp3
16. AI Post Transformers: TMAS: Scaling Test-Time Compute with Multi-Agent Synergy — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-14-tmas-scaling-test-time-compute-with-mult-3abe7a.mp3
17. AI Post Transformers: Air Force One, Jensen Huang, and Anthropic's 2028 Memo — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-15-2028-scenarios-for-global-ai-leadership-d0ec29.mp3
Interactive Visualization: When AI Builds Itself and Recursive Self-Improvement
...more
0min
June 05, 2026 Mooncake for KV Cache-Centric LLM Serving
This episode explores Mooncake, a production LLM serving architecture that treats KV cache reuse and movement as the central challenge in long-context chat, not just raw GPU compute. It explains why prefill and decode stress hardware in different ways, how metrics like time to first token and time between tokens drive system design, and why separating those phases helps meet real latency targets. The discussion walks through Mooncake’s cache-first scheduler, tiered KV storage across GPU memory, CPU DRAM, and SSD, and its use of RDMA, chunked prefill, and layer-wise overlap to start decoding sooner while reusing existing state. It also argues that Mooncake’s interest lies less in a single breakthrough than in how it combines prefix-aware routing, overload-aware early rejection, and cross-node KV reuse into a practical serving stack for large-scale chat systems.
Sources:
1. Mooncake for KV Cache-Centric LLM Serving
https://arxiv.org/pdf/2407.00079
2. Efficient Memory Management for Large Language Model Serving with PagedAttention — Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Joseph E. Gonzalez, Hao Zhang, Ion Stoica, et al., 2023
https://scholar.google.com/scholar?q=Efficient+Memory+Management+for+Large+Language+Model+Serving+with+PagedAttention
3. SGLang: Efficient Execution of Structured Language Model Programs — Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, Chuyue Sun, Jeff Huang, Cody Hao Yu, Ying Sheng, et al., 2023
https://scholar.google.com/scholar?q=SGLang%3A+Efficient+Execution+of+Structured+Language+Model+Programs
4. DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving — Yinmin Zhong, Shengyu Liu, Junda Chen, Jianbo Hu, Yibo Zhu, Xuanzhe Liu, Xin Jin, Hao Zhang, 2024
https://scholar.google.com/scholar?q=DistServe%3A+Disaggregating+Prefill+and+Decoding+for+Goodput-optimized+Large+Language+Model+Serving
5. Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving — Ruoyu Qin, Zheming Li, Weiran He, Mingxing Zhang, Yongwei Wu, Weimin Zheng, Xinran Xu, 2024
https://scholar.google.com/scholar?q=Mooncake%3A+A+KVCache-centric+Disaggregated+Architecture+for+LLM+Serving
6. Overload Control for Scaling WeChat Microservices — Hao Zhou, Ming Chen, Qian Lin, Yong Wang, Xiaobin She, Sifan Liu, Rui Gu, Beng Chin Ooi, Junfeng Yang, 2018
https://scholar.google.com/scholar?q=Overload+Control+for+Scaling+WeChat+Microservices
7. Overload Control for microsecond-scale RPCs with Breakwater — Inho Cho, Ahmed Saeed, Joshua Fried, Seo Jin Park, Mohammad Alizadeh, Adam Belay, 2020
https://scholar.google.com/scholar?q=Overload+Control+for+microsecond-scale+RPCs+with+Breakwater
8. SCORPIO: Serving the Right Requests at the Right Time for Heterogeneous SLOs in LLM Inference — Yinghao Tang, Tingfeng Lan, Xiuqi Huang, Hui Lu, Wei Chen, 2025
https://scholar.google.com/scholar?q=SCORPIO%3A+Serving+the+Right+Requests+at+the+Right+Time+for+Heterogeneous+SLOs+in+LLM+Inference
9. Splitwise: Efficient Generative LLM Inference Using Phase Splitting — Pratyush Patel, Esha Choukse, Chaojie Zhang, Inigo Goiri, Aashaka Shah, Saeed Maleki, Ricardo Bianchini, 2023
https://scholar.google.com/scholar?q=Splitwise%3A+Efficient+Generative+LLM+Inference+Using+Phase+Splitting
10. AttentionStore: Cost-Effective Attention Reuse Across Multi-Turn Conversations in Large Language Model Serving — Bin Gao, Zhuomin He, Puru Sharma, Qingxuan Kang, Djordje Jevdjic, Junbo Deng, Xingkun Yang, Zhou Yu, Pengfei Zuo, 2024
https://scholar.google.com/scholar?q=AttentionStore%3A+Cost-Effective+Attention+Reuse+Across+Multi-Turn+Conversations+in+Large+Language+Model+Serving
11. Preble: Efficient Distributed Prompt Scheduling for LLM Serving — Vikranth Srivatsa, Zijian He, Reyna Abhyankar, Dongming Li, Yiying Zhang, 2024
https://scholar.google.com/scholar?q=Preble%3A+Efficient+Distributed+Prompt+Scheduling+for+LLM+Serving
12. CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge Fusion — Jiayi Yao, Hanchen Li, Yuhan Liu, Siddhant Ray, Yihua Cheng, Qizheng Zhang, Kuntai Du, Shan Lu, Junchen Jiang, 2024
https://scholar.google.com/scholar?q=CacheBlend%3A+Fast+Large+Language+Model+Serving+for+RAG+with+Cached+Knowledge+Fusion
13. P/D-Serve: Serving Disaggregated Large Language Model at Scale — Yibo Jin et al., 2024
https://scholar.google.com/scholar?q=P%2FD-Serve%3A+Serving+Disaggregated+Large+Language+Model+at+Scale
14. POD-Attention: Unlocking Full Prefill-Decode Overlap for Faster LLM Inference — Aditya K. Kamath et al., 2025
https://scholar.google.com/scholar?q=POD-Attention%3A+Unlocking+Full+Prefill-Decode+Overlap+for+Faster+LLM+Inference
15. Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models — Siyan Zhao et al., 2024
https://scholar.google.com/scholar?q=Prepacking%3A+A+Simple+Method+for+Fast+Prefilling+and+Increased+Throughput+in+Large+Language+Models
16. Mustafar: Promoting Unstructured Sparsity for KV Cache Pruning in LLM Inference — Donghyeon Joo et al., 2025
https://scholar.google.com/scholar?q=Mustafar%3A+Promoting+Unstructured+Sparsity+for+KV+Cache+Pruning+in+LLM+Inference
17. SparK: Query-Aware Unstructured Sparsity with Recoverable KV Cache Channel Pruning — Huanxuan Liao et al., 2025/2026
https://scholar.google.com/scholar?q=SparK%3A+Query-Aware+Unstructured+Sparsity+with+Recoverable+KV+Cache+Channel+Pruning
18. More Tokens, Lower Precision: Towards the Optimal Token-Precision Trade-off in KV Cache Compression — Jiebin Zhang et al., 2024
https://scholar.google.com/scholar?q=More+Tokens%2C+Lower+Precision%3A+Towards+the+Optimal+Token-Precision+Trade-off+in+KV+Cache+Compression
19. KVShare: An LLM Service System with Efficient and Effective Multi-Tenant KV Cache Reuse — Huan Yang et al., 2025
https://scholar.google.com/scholar?q=KVShare%3A+An+LLM+Service+System+with+Efficient+and+Effective+Multi-Tenant+KV+Cache+Reuse
20. CacheSolidarity: Preventing Prefix Caching Side Channels in Multi-tenant LLM Serving Systems — Panagiotis Georgios Pennas et al., 2026
https://scholar.google.com/scholar?q=CacheSolidarity%3A+Preventing+Prefix+Caching+Side+Channels+in+Multi-tenant+LLM+Serving+Systems
21. AI Post Transformers: Prefill-as-a-Service for Cross-Datacenter KV Cache — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-19-prefill-as-a-service-for-cross-datacente-7560be.mp3
22. AI Post Transformers: Characterizing LLM KV Cache Workloads in Production — Hal Turing & Dr. Ada Shannon, 2025
https://podcast.do-not-panic.com/episodes/characterizing-llm-kv-cache-workloads-in-production/
23. AI Post Transformers: ContiguousKV for Faster LLM Prefill KV Reuse — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-20-contiguouskv-for-faster-llm-prefill-kv-r-59f545.mp3
24. AI Post Transformers: CacheFlow and 3D-Parallel KV Cache Restoration — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-01-cacheflow-and-3d-parallel-kv-cache-resto-8db883.mp3
25. AI Post Transformers: Beluga: CXL Memory Pooling for LLM KV Cache — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-27-beluga-cxl-memory-pooling-for-llm-kv-cac-b6142f.mp3
26. AI Post Transformers: Speculative Decoding in Real vLLM Serving — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-04-speculative-decoding-in-real-vllm-servin-6f4e2b.mp3
Interactive Visualization: Mooncake for KV Cache-Centric LLM Serving
...more
0min
June 05, 2026 Technical AGI Safety and Security Framework
This episode explores DeepMind’s paper on technical AGI safety and security, focusing on how labs might prevent severe, humanity-scale harm before highly capable systems are deployed. It breaks down the paper’s core distinctions between misuse and misalignment, explains what the authors mean by Exceptional AGI and the no-human-ceiling assumption, and examines dangerous capability evaluations in areas like cyber, biology, persuasion, and self-proliferation. The discussion highlights the paper’s main argument that safety measures such as refusal training, jailbreak hardening, access controls, monitoring, anomaly detection, and model-weight security only matter if they are explicitly tied to capability thresholds that trigger real deployment restrictions. Listeners would find it interesting because it turns abstract AGI risk debates into a concrete governance and engineering framework for deciding when a model is too dangerous to release under normal conditions.
Sources:
1. Technical AGI Safety and Security Framework
https://arxiv.org/pdf/2504.01849
2. The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation — Miles Brundage, Shahar Avin, Jack Clark, Helen Toner, et al., 2018
https://scholar.google.com/scholar?q=The+Malicious+Use+of+Artificial+Intelligence%3A+Forecasting%2C+Prevention%2C+and+Mitigation
3. Model evaluation for extreme risks — Toby Shevlane, Sebastian Farquhar, Ben Garfinkel, Mary Phuong, et al., 2023
https://scholar.google.com/scholar?q=Model+evaluation+for+extreme+risks
4. Frontier AI Regulation: Managing Emerging Risks to Public Safety — Markus Anderljung, Joslyn Barnhart, Anton Korinek, Jade Leung, et al., 2023
https://scholar.google.com/scholar?q=Frontier+AI+Regulation%3A+Managing+Emerging+Risks+to+Public+Safety
5. Evaluating Frontier Models for Dangerous Capabilities — Mary Phuong, Matthew Aitchison, Elliot Catt, Victoria Krakovna, et al., 2024
https://scholar.google.com/scholar?q=Evaluating+Frontier+Models+for+Dangerous+Capabilities
6. Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS) — Richard Hawkins, Colin Paterson, Chiara Picardi, Ibrahim Habli, et al., 2021
https://scholar.google.com/scholar?q=Guidance+on+the+Assurance+of+Machine+Learning+in+Autonomous+Systems+%28AMLAS%29
7. Safety Cases: How to Justify the Safety of Advanced AI Systems — Joshua Clymer, Nick Gabrieli, David Krueger, Thomas Larsen, 2024
https://scholar.google.com/scholar?q=Safety+Cases%3A+How+to+Justify+the+Safety+of+Advanced+AI+Systems
8. Safety case template for frontier AI: A cyber inability argument — Arthur Goemans, Marie Davidsen Buhl, Jonas Schuett, Geoffrey Irving, et al., 2024
https://scholar.google.com/scholar?q=Safety+case+template+for+frontier+AI%3A+A+cyber+inability+argument
9. The BIG Argument for AI Safety Cases — Ibrahim Habli, Richard Hawkins, Colin Paterson, Mark Sujan, et al., 2025
https://scholar.google.com/scholar?q=The+BIG+Argument+for+AI+Safety+Cases
10. Safety cases: Justifying the safety of advanced AI systems — J. Clymer, N. Gabrieli, D. Krueger, and T. Larsen, 2024
https://scholar.google.com/scholar?q=Safety+cases%3A+Justifying+the+safety+of+advanced+AI+systems
11. AI control: Improving safety despite intentional subversion — R. Greenblatt, B. Shlegeris, K. Sachan, and F. Roger, 2024
https://scholar.google.com/scholar?q=AI+control%3A+Improving+safety+despite+intentional+subversion
12. Alignment faking in large language models — R. Greenblatt, C. Denison, B. Wright, F. Roger, M. MacDiarmid, S. Marks, J. Treutlein, T. Belonax, J. Chen, D. Duvenaud, et al., 2024
https://scholar.google.com/scholar?q=Alignment+faking+in+large+language+models
13. Towards evaluations-based safety cases for AI scheming — M. Balesni, M. Hobbhahn, D. Lindner, A. Meinke, T. Korbak, J. Clymer, B. Shlegeris, J. Scheurer, C. Stix, R. Shah, et al., 2024
https://scholar.google.com/scholar?q=Towards+evaluations-based+safety+cases+for+AI+scheming
14. Generative AI misuse: A taxonomy of tactics and insights from real-world data — N. Marchal, R. Xu, R. Elasmar, I. Gabriel, B. Goldberg, and W. Isaac, 2024
https://scholar.google.com/scholar?q=Generative+AI+misuse%3A+A+taxonomy+of+tactics+and+insights+from+real-world+data
15. Stress-Testing Capability Elicitation With Password-Locked Models — Ryan Greenblatt et al., 2024
https://scholar.google.com/scholar?q=Stress-Testing+Capability+Elicitation+With+Password-Locked+Models
16. Noise Injection Reveals Hidden Capabilities of Sandbagging Language Models — Cameron Tice et al., 2024
https://scholar.google.com/scholar?q=Noise+Injection+Reveals+Hidden+Capabilities+of+Sandbagging+Language+Models
17. Benchmarking Misuse Mitigation Against Covert Adversaries — Davis Brown et al., 2025
https://scholar.google.com/scholar?q=Benchmarking+Misuse+Mitigation+Against+Covert+Adversaries
18. On scalable oversight with weak LLMs judging strong LLMs — Zachary Kenton et al., 2024
https://scholar.google.com/scholar?q=On+scalable+oversight+with+weak+LLMs+judging+strong+LLMs
19. Scaling Laws For Scalable Oversight — Joshua Engels et al., 2025
https://scholar.google.com/scholar?q=Scaling+Laws+For+Scalable+Oversight
20. Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs — Kyle O'Brien et al., 2025
https://scholar.google.com/scholar?q=Deep+Ignorance%3A+Filtering+Pretraining+Data+Builds+Tamper-Resistant+Safeguards+into+Open-Weight+LLMs
21. AI Post Transformers: When LLM Judges Become Coin Flips — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-05-when-llm-judges-become-coin-flips-8b43ef.mp3
22. AI Post Transformers: Reasoning Theater and Unfaithful Chain-of-Thought — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-05-reasoning-theater-and-unfaithful-chain-o-a4507e.mp3
23. AI Post Transformers: Split Personality Training Reveals Latent Knowledge — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-08-split-personality-training-reveals-laten-c84616.mp3
24. AI Post Transformers: Trajectory Summaries for Long-Horizon Coding Agents — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-24-trajectory-summaries-for-long-horizon-co-0194be.mp3
...more
13min
June 05, 2026 VeriCache: Lossless LLM Inference from Lossy KV Caches
This episode explores VeriCache, a systems paper that asks whether a large language model can draft tokens using a compressed, lossy KV cache and then verify them against the full cache to recover exactly the same greedy-decoding output. It explains why KV cache size has become a central bottleneck in long-context inference, unpacking token dropping, KV quantization, prefix caching, and the speculative decoding ideas that VeriCache turns into a closed-loop draft-and-verify scheme. The discussion argues that fluency is not enough for real deployments because even a single wrong token can break code, structured JSON, or tool calls, so exact token-by-token agreement is the real standard for safe acceleration. Listeners get a clear picture of where serving stacks such as vLLM, Hugging Face TGI, and NVIDIA already use neighboring optimizations, and why VeriCache’s specific lossless-verification approach is both technically appealing and operationally difficult.
Sources:
1. VeriCache: Turning Lossy KV Cache into Lossless LLM Inference — Jiayi Yao, Samuel Shen, Kuntai Du, Shaoting Feng, Dongjoo Seo, Rui Zhang, Yuyang Huang, Yuhan Liu, Shan Lu, Junchen Jiang, 2026
http://arxiv.org/abs/2605.17613
2. Fast Inference from Transformers via Speculative Decoding — Yaniv Leviathan, Matan Kalman, Yossi Matias, 2023
https://proceedings.mlr.press/v202/leviathan23a.html
3. H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models — Zhenyu Zhang, Ying Sheng, Tianyi Zhou, Tianlong Chen, Beidi Chen, et al., 2023
https://proceedings.neurips.cc/paper_files/paper/2023/hash/6ceefa7b15572587b78ecfcebb2827f8-Abstract-Conference.html
4. KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache — Zirui Liu, Jiayi Yuan, Hongye Jin, Shaochen Zhong, Beidi Chen, Xia Hu, et al., 2024
https://arxiv.org/abs/2402.02750
5. QuantSpec: Self-Speculative Decoding with Hierarchical Quantized KV Cache — Rishabh Tiwari, Haocheng Xi, Aditya Tomar, Coleman Hooper, Kurt Keutzer, Amir Gholami, et al., 2025
https://arxiv.org/abs/2502.10424
6. MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding — Ranajoy Sadhukhan, Jian Chen, Zhuoming Chen, et al., 2024
https://scholar.google.com/scholar?q=MagicDec%3A+Breaking+the+Latency-Throughput+Tradeoff+for+Long+Context+Generation+with+Speculative+Decoding
7. Accelerating Large-Scale Reasoning Model Inference: Self-Speculative Decoding with Sparse Attention — Yilong Zhao, Jiaming Tang, Kan Zhu, Zihao Ye, Chi-Chih Chang, Chaofan Lin, Jongseok Park, Guangxuan Xiao, Mohamed S. Abdelfattah, Mingyu Gao, Baris Kasikci, Song Han, and Ion Stoica, 2025
https://scholar.google.com/scholar?q=Accelerating+Large-Scale+Reasoning+Model+Inference%3A+Self-Speculative+Decoding+with+Sparse+Attention
8. CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving — Yuhan Liu, Hanchen Li, Yihua Cheng, Siddhant Ray, Yuyang Huang, Qizheng Zhang, Kuntai Du, Jiayi Yao, Shan Lu, Ganesh Ananthanarayanan, and Junchen Jiang, 2024
https://scholar.google.com/scholar?q=CacheGen%3A+KV+Cache+Compression+and+Streaming+for+Fast+Large+Language+Model+Serving
9. ShadowServe: Interference-Free KV Cache Fetching for Distributed Prefix Caching — Xingyu Xiang, Raj Joshi, Yuhan Liu, Jiayi Yao, Chenxingyu Zhao, Junchen Jiang, Yang Zhou, Eddie Kohler, and Minlan Yu, 2025
https://scholar.google.com/scholar?q=ShadowServe%3A+Interference-Free+KV+Cache+Fetching+for+Distributed+Prefix+Caching
10. LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inference — Yuhan Liu, Yihua Cheng, Jiayi Yao, Yuwei An, Xiaokun Chen, Shaoting Feng, et al., 2025
https://scholar.google.com/scholar?q=LMCache%3A+An+Efficient+KV+Cache+Layer+for+Enterprise-Scale+LLM+Inference
11. ARKV: Adaptive and Resource-Efficient KV Cache Management under Limited Memory Budget for Long-Context Inference in LLMs — Jianlong Lei and Shashikant Ilager, 2026
https://arxiv.org/abs/2603.08727
12. Don't Waste Bits! Adaptive KV-Cache Quantization for Lightweight On-Device LLMs — Sayed Pedram Haeri Boroujeni et al., 2026
https://arxiv.org/abs/2604.04722
13. LongSpec: Long-Context Lossless Speculative Decoding with Efficient Drafting and Verification — Penghui Yang et al., 2025
https://arxiv.org/abs/2502.17421
14. RAPID: Long-Context Inference with Retrieval-Augmented Speculative Decoding — Guanzheng Chen et al., 2025
https://arxiv.org/abs/2502.20330
15. LayerKV: Optimizing Large Language Model Serving with Layer-wise KV Cache Management — Yi Xiong et al., 2024
https://arxiv.org/abs/2410.00428
16. TokenLake: A Unified Segment-level Prefix Cache Pool for Fine-grained Elastic Long-Context LLM Serving — Bingyang Wu et al., 2025
https://arxiv.org/abs/2508.17219
17. KVFlow: Efficient Prefix Caching for Accelerating LLM-Based Multi-Agent Workflows — Zaifeng Pan et al., 2025
https://arxiv.org/abs/2507.07400
18. KVShare: An LLM Service System with Efficient and Effective Multi-Tenant KV Cache Reuse — Huan Yang et al., 2025
https://arxiv.org/abs/2503.16525
19. KV Cache Offloading for Context-Intensive Tasks — Andrey Bocharnikov et al., 2026
https://arxiv.org/abs/2604.08426
20. AI Post Transformers: KVzip for Query-Agnostic KV Cache Compression — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-29-kvzip-for-query-agnostic-kv-cache-compre-72afe5.mp3
21. AI Post Transformers: PackKV Lossy Compression for KV Caches — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-04-packkv-lossy-compression-for-kv-caches-b37bce.mp3
22. AI Post Transformers: Speculative Decoding in Real vLLM Serving — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-04-speculative-decoding-in-real-vllm-servin-6f4e2b.mp3
23. AI Post Transformers: Affordable Large-Scale Decoding Through Model-System Co-Design — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-19-affordable-large-scale-decoding-through-e1d7ed.mp3
24. AI Post Transformers: Stochastic KV Routing for Cache Sharing — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-29-stochastic-kv-routing-for-cache-sharing-5fef63.mp3
...more
14min

FAQs about AI Post Transformers:

How many episodes does AI Post Transformers have?

The podcast currently has 807 episodes available.