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. HACE 3 DÍAS

    FlatAttention for Tile-Based Accelerator Inference

    This episode explores a 2026 paper on “FlatAttention,” which argues that attention inference should be co-designed with on-chip communication primitives to fully exploit tile-based accelerators rather than reusing GPU-style kernels. It explains how these accelerators differ from GPUs: computation is spread across many tiles with local SRAM and an on-chip network, making data placement, multicast, and reduction central to performance. The discussion highlights why attention has become a growing inference bottleneck—especially for long-context models and MoE systems—and contrasts prefill vs. decode behavior, KV-cache movement costs, and variants like MHA, MQA, GQA, and MLA. Listeners would find it interesting for its careful framing of both the promise and the fairness concerns of hardware-software co-design, especially in comparison to FlashAttention’s IO-aware optimization on GPUs. Sources: 1. FlatAttention: Dataflow and Fabric Collectives Co-Optimization for Large Attention-Based Model Inference on Tile-Based Accelerators — Chi Zhang, Luca Colagrande, Renzo Andri, Luca Benini, 2026 http://arxiv.org/abs/2604.02110 2. In-Datacenter Performance Analysis of a Tensor Processing Unit — Norman P. Jouppi, Cliff Young, Nishant Patil, David Patterson, and others, 2017 https://scholar.google.com/scholar?q=In-Datacenter+Performance+Analysis+of+a+Tensor+Processing+Unit 3. A Domain-Specific Supercomputer for Training Deep Neural Networks — Norman P. Jouppi, George Kurian, Sheng Li, and others, 2021 https://scholar.google.com/scholar?q=A+Domain-Specific+Supercomputer+for+Training+Deep+Neural+Networks 4. A Wafer-Scale Engine for Deep Learning — Sean Lie, Andrew H. Putnam, David Firestone, and Cerebras Systems team, 2021 https://scholar.google.com/scholar?q=A+Wafer-Scale+Engine+for+Deep+Learning 5. Scaling Graph Neural Networks with the Graphcore IPU — James H. Smith, et al. (Graphcore-affiliated authors in IPU architecture/application literature), 2022 https://scholar.google.com/scholar?q=Scaling+Graph+Neural+Networks+with+the+Graphcore+IPU 6. Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices — Yu-Hsin Chen, Tushar Krishna, Joel S. Emer, Vivienne Sze, 2019 https://scholar.google.com/scholar?q=Eyeriss+v2:+A+Flexible+Accelerator+for+Emerging+Deep+Neural+Networks+on+Mobile+Devices 7. In-Network Computing for Machine Learning: Opportunities and Challenges — various survey authors in networking/ML systems literature; representative surveys include works by Mohammad Alizadeh, Yibo Zhu, and collaborators, 2021 https://scholar.google.com/scholar?q=In-Network+Computing+for+Machine+Learning:+Opportunities+and+Challenges 8. Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism — Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, Bryan Catanzaro, 2019 https://scholar.google.com/scholar?q=Megatron-LM:+Training+Multi-Billion+Parameter+Language+Models+Using+Model+Parallelism 9. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness — Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, Christopher Ré, 2022 https://scholar.google.com/scholar?q=FlashAttention:+Fast+and+Memory-Efficient+Exact+Attention+with+IO-Awareness 10. FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning — Tri Dao, 2023 https://scholar.google.com/scholar?q=FlashAttention-2:+Faster+Attention+with+Better+Parallelism+and+Work+Partitioning 11. FlashAttention-3 — Tri Dao and collaborators, 2024 https://scholar.google.com/scholar?q=FlashAttention-3 12. FlashMLA — DeepSeek team, 2025 https://scholar.google.com/scholar?q=FlashMLA 13. GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints — Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebron, Sumit Sanghai, 2023 https://scholar.google.com/scholar?q=GQA:+Training+Generalized+Multi-Query+Transformer+Models+from+Multi-Head+Checkpoints 14. Fast Transformer Decoding: One Write-Head is All You Need — Noam Shazeer, 2019 https://scholar.google.com/scholar?q=Fast+Transformer+Decoding:+One+Write-Head+is+All+You+Need 15. DeepSeek-V3 Technical Report — DeepSeek-AI, 2024 https://scholar.google.com/scholar?q=DeepSeek-V3+Technical+Report 16. Attention Is All You Need — Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin, 2017 https://scholar.google.com/scholar?q=Attention+Is+All+You+Need 17. Wafer-Scale Deep Learning — Daniel Lie, Gary Lauterbach, Sean Lie and collaborators at Cerebras, 2021 https://scholar.google.com/scholar?q=Wafer-Scale+Deep+Learning 18. Distributed Deep Learning on a Wafer-Scale Engine — Cerebras Systems authors, 2022 https://scholar.google.com/scholar?q=Distributed+Deep+Learning+on+a+Wafer-Scale+Engine 19. LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inference — approx. enterprise systems / LLM serving authors, 2024 https://scholar.google.com/scholar?q=LMCache:+An+Efficient+KV+Cache+Layer+for+Enterprise-Scale+LLM+Inference 20. HotPrefix: Hotness-Aware KV Cache Scheduling for Efficient Prefix Sharing in LLM Inference Systems — approx. LLM systems authors, 2024 https://scholar.google.com/scholar?q=HotPrefix:+Hotness-Aware+KV+Cache+Scheduling+for+Efficient+Prefix+Sharing+in+LLM+Inference+Systems 21. Selective KV-Cache Sharing to Mitigate Timing Side-Channels in LLM Inference — approx. security / systems authors, 2024 https://scholar.google.com/scholar?q=Selective+KV-Cache+Sharing+to+Mitigate+Timing+Side-Channels+in+LLM+Inference 22. MoE-Gen: High-Throughput MoE Inference on a Single GPU with Module-Based Batching — approx. MoE inference systems authors, 2024 https://scholar.google.com/scholar?q=MoE-Gen:+High-Throughput+MoE+Inference+on+a+Single+GPU+with+Module-Based+Batching 23. Accelerating Distributed MoE Training and Inference with Lina — approx. distributed systems / ML systems authors, 2024 https://scholar.google.com/scholar?q=Accelerating+Distributed+MoE+Training+and+Inference+with+Lina 24. Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference — approx. MoE deployment authors, 2024 https://scholar.google.com/scholar?q=Towards+MoE+Deployment:+Mitigating+Inefficiencies+in+Mixture-of-Expert+(MoE)+Inference 25. MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices — approx. accelerator architecture authors, 2024 https://scholar.google.com/scholar?q=MAS-Attention:+Memory-Aware+Stream+Processing+for+Attention+Acceleration+on+Resource-Constrained+Edge+Devices 26. REATA: An Efficient Vision Transformer Accelerator Featuring a Resource-Optimized Attention Design on Versal ACAP — approx. FPGA / accelerator authors, 2024 https://scholar.google.com/scholar?q=REATA:+An+Efficient+Vision+Transformer+Accelerator+Featuring+a+Resource-Optimized+Attention+Design+on+Versal+ACAP 27. Concerto: Automatic Communication Optimization and Scheduling for Large-Scale Deep Learning — approx. systems / compiler authors, 2024 https://scholar.google.com/scholar?q=Concerto:+Automatic+Communication+Optimization+and+Scheduling+for+Large-Scale+Deep+Learning 28. AI Post Transformers: Splitwise: Phase-Split LLM Inference — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/2026-03-26-splitwise-phase-split-llm-inference-e8945b.mp3 29. AI Post Transformers: SolidAttention: Co-Designing Sparse Attention and SSD I/O — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/2026-03-18-solidattention-co-designing-sparse-atten-5a8622.mp3 30. AI Post Transformers: LookaheadKV: Fast and Accurate KV Cache Eviction by Glimpsing into the Future without Generation — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/2026-03-25-lookaheadkv-fast-and-accurate-kv-9cfc9f.mp3 31. AI Post Transformers: From Prefix Cache to Fusion RAG Cache: Accelerating LLM Inference in Retrieval-Augmented Generation — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/2026-03-22-from-prefix-cache-to-fusion-rag-9c5d39.mp3 32. AI Post Transformers: Continuous Batching for LLM Inference: Throughput and Latency Gains — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/continuous-batching-for-llm-inference-throughput-and-latency-gains/ 33. AI Post Transformers: SGLang: Efficient Language Model Program Execution — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/sglang-efficient-language-model-program-execution/ 34. AI Post Transformers: Speculative Speculative Decoding — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/2026-03-25-speculative-speculative-decoding-1b7a10.mp3 35. AI Post Transformers: Jet-Nemotron and PostNAS for Faster Long Context — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/2026-03-24-jet-nemotron-and-postnas-for-faster-long-436381.mp3 36. AI Post Transformers: FlexGen: High-Throughput LLM Inference on a Single GPU — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/flexgen-high-throughput-llm-inference-on-a-single-gpu/ Interactive Visualization: FlatAttention for Tile-Based Accelerator Inference

  2. HACE 3 DÍAS

    IMO-Bench for Robust Mathematical Reasoning

    This episode explores a new benchmark suite, IMO-Bench, designed to test whether AI systems can do genuinely robust mathematical reasoning at Olympiad difficulty rather than merely produce correct final answers. It breaks down the benchmark into three distinct tasks—short-answer problem solving, full proof generation, and automatic proof grading—and argues that this decomposition better captures real mathematical competence than answer-centric evaluations like GSM8K or MATH, which may now be saturated or overly teachable. The discussion highlights why IMO-style problems are especially revealing: they require discovering invariants, constructions, and contradiction arguments that resist routine pattern matching and expose whether models can sustain long-horizon reasoning and self-correction. Listeners would find it interesting because it tackles a central question in AI evaluation—whether current benchmarks are measuring true reasoning or just benchmark-specific performance—and examines the promise and risks of using model-based autograders to scale proof assessment. Sources: 1. Towards Robust Mathematical Reasoning — Thang Luong, Dawsen Hwang, Hoang H. Nguyen, Golnaz Ghiasi, Yuri Chervonyi, Insuk Seo, Junsu Kim, Garrett Bingham, Jonathan Lee, Swaroop Mishra, Alex Zhai, Clara Huiyi Hu, Henryk Michalewski, Jimin Kim, Jeonghyun Ahn, Junhwi Bae, Xingyou Song, Trieu H. Trinh, Quoc V. Le, Junehyuk Jung, 2025 http://arxiv.org/abs/2511.01846 2. Training Verifiers to Solve Math Word Problems — Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, John Schulman, 2021 https://scholar.google.com/scholar?q=Training+Verifiers+to+Solve+Math+Word+Problems 3. Measuring Mathematical Problem Solving With the MATH Dataset — Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, Jacob Steinhardt, 2021 https://scholar.google.com/scholar?q=Measuring+Mathematical+Problem+Solving+With+the+MATH+Dataset 4. Solving Quantitative Reasoning Problems with Language Models — Aakanksha Chowdhery and collaborators at Google Research, 2022 https://scholar.google.com/scholar?q=Solving+Quantitative+Reasoning+Problems+with+Language+Models 5. FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI — Elliot Glazer and collaborators, 2024 https://scholar.google.com/scholar?q=FrontierMath:+A+Benchmark+for+Evaluating+Advanced+Mathematical+Reasoning+in+AI 6. Beyond the Imitation Game: Quantifying and Extrapolating the Capabilities of Language Models — Suzgun Mirac, et al. (BIG-bench collaboration), 2022 https://scholar.google.com/scholar?q=Beyond+the+Imitation+Game:+Quantifying+and+Extrapolating+the+Capabilities+of+Language+Models 7. Holistic Evaluation of Language Models — Percy Liang, Rishi Bommasani, Tony Lee, Dmitriy Turbiner, and collaborators, 2022 https://scholar.google.com/scholar?q=Holistic+Evaluation+of+Language+Models 8. Dynabench: Rethinking Benchmarking in NLP — Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, and collaborators, 2021 https://scholar.google.com/scholar?q=Dynabench:+Rethinking+Benchmarking+in+NLP 9. Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena — Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Tianle Li, et al., 2023 https://scholar.google.com/scholar?q=Judging+LLM-as-a-Judge+with+MT-Bench+and+Chatbot+Arena 10. G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment — Jun Gao, Huanle Liu, et al., 2023 https://scholar.google.com/scholar?q=G-Eval:+NLG+Evaluation+using+GPT-4+with+Better+Human+Alignment 11. Automatic Evaluation of Mathematical Proofs in Natural Language: A Survey — Various survey authors in educational technology and AI, 2020-2024 https://scholar.google.com/scholar?q=Automatic+Evaluation+of+Mathematical+Proofs+in+Natural+Language:+A+Survey 12. Towards Robust Mathematical Reasoning — Thang Luong, Dawsen Hwang, Hoang H. Nguyen, Golnaz Ghiasi, Yuri Chervonyi, Insuk Seo, Junsu Kim, Garrett Bingham, Jonathan Lee, Swaroop Mishra, Alex Zhai, Clara Huiyi Hu, Henryk Michalewski, Jimin Kim, Jeonghyun Ahn, Junhwi Bae, Xingyou Song, Trieu H. Trinh, Quoc V. Le, Junehyuk Jung, 2025 https://scholar.google.com/scholar?q=Towards+Robust+Mathematical+Reasoning 13. Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs — Various authors in neural theorem proving and autoformalization, 2022-2024 https://scholar.google.com/scholar?q=Draft,+Sketch,+and+Prove:+Guiding+Formal+Theorem+Provers+with+Informal+Proofs 14. Solving Olympiad Geometry without Human Demonstrations — Trieu H. Trinh, Yuhuai Wu, Quoc V. Le, He He, et al., 2024 https://scholar.google.com/scholar?q=Solving+Olympiad+Geometry+without+Human+Demonstrations 15. LeanDojo: Theorem Proving with Retrieval-Augmented Language Models — Kaiyu Yang, Aidan O'Gara, et al., 2023 https://scholar.google.com/scholar?q=LeanDojo:+Theorem+Proving+with+Retrieval-Augmented+Language+Models 16. FrontierMath — Glazer et al., 2024 https://scholar.google.com/scholar?q=FrontierMath 17. Humanity's Last Exam — Phan et al., 2025 https://scholar.google.com/scholar?q=Humanity's+Last+Exam 18. GSM8K: Training Verifiers to Solve Math Word Problems — Cobbe et al., 2021 https://scholar.google.com/scholar?q=GSM8K:+Training+Verifiers+to+Solve+Math+Word+Problems 19. Gemini Deep Think at IMO 2025 — Luong and Lockhart, 2025 https://scholar.google.com/scholar?q=Gemini+Deep+Think+at+IMO+2025 20. Reasoning or Memorization? Unreliable Results of Reinforcement Learning Due to Data Contamination — approx. 2025, authors unclear from snippet, 2025 https://scholar.google.com/scholar?q=Reasoning+or+Memorization?+Unreliable+Results+of+Reinforcement+Learning+Due+to+Data+Contamination 21. Right Is Not Enough: The Pitfalls of Outcome Supervision in Training LLMs for Math Reasoning — approx. 2025, authors unclear from snippet, 2025 https://scholar.google.com/scholar?q=Right+Is+Not+Enough:+The+Pitfalls+of+Outcome+Supervision+in+Training+LLMs+for+Math+Reasoning 22. Improve Mathematical Reasoning in Language Models by Automated Process Supervision — approx. 2025, authors unclear from snippet, 2025 https://scholar.google.com/scholar?q=Improve+Mathematical+Reasoning+in+Language+Models+by+Automated+Process+Supervision 23. MM-PRM: Enhancing Multimodal Mathematical Reasoning with Scalable Step-Level Supervision — approx. 2025, authors unclear from snippet, 2025 https://scholar.google.com/scholar?q=MM-PRM:+Enhancing+Multimodal+Mathematical+Reasoning+with+Scalable+Step-Level+Supervision 24. Solving Inequality Proofs with Large Language Models — approx. 2025, authors unclear from snippet, 2025 https://scholar.google.com/scholar?q=Solving+Inequality+Proofs+with+Large+Language+Models 25. Beyond Gold Standards: Epistemic Ensemble of LLM Judges for Formal Mathematical Reasoning — approx. 2025, authors unclear from snippet, 2025 https://scholar.google.com/scholar?q=Beyond+Gold+Standards:+Epistemic+Ensemble+of+LLM+Judges+for+Formal+Mathematical+Reasoning 26. A Survey on Deep Learning for Theorem Proving — approx. survey authors unclear from snippet, recent https://scholar.google.com/scholar?q=A+Survey+on+Deep+Learning+for+Theorem+Proving 27. Proving Theorems Recursively — approx. 2025, authors unclear from snippet, 2025 https://scholar.google.com/scholar?q=Proving+Theorems+Recursively 28. DICE: Detecting In-distribution Contamination in LLM's Fine-tuning Phase for Math Reasoning — approx. 2025, authors unclear from snippet, 2025 https://scholar.google.com/scholar?q=DICE:+Detecting+In-distribution+Contamination+in+LLM's+Fine-tuning+Phase+for+Math+Reasoning 29. AI Post Transformers: Schoenfeld Theory Applied to Large Reasoning Models — Hal Turing & Dr. Ada Shannon, Sat, https://podcast.do-not-panic.com/episodes/schoenfeld-theory-applied-to-large-reasoning-models/ 30. 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/ 31. 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/ 32. 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/ Interactive Visualization: IMO-Bench for Robust Mathematical Reasoning

  3. HACE 3 DÍAS

    Internal Safety Collapse in Frontier LLMs

    This episode explores a 2026 paper arguing that frontier language models can undergo “Internal Safety Collapse,” a failure mode where they stop merely slipping once and instead sustain harmful output when a task is framed as legitimate professional work. It explains how refusal-based alignment may function more like a behavioral wrapper than a removal of dangerous capabilities, allowing harmful knowledge to re-emerge when task objectives and safety objectives conflict. The discussion contrasts classic jailbreaks and prompt-centric red teaming with workflow-level risks in agents, copilots, and enterprise systems, where tools, memory, validators, and multi-step tasks can make unsafe content the “correct” way to complete a job. Listeners would find it interesting because it reframes AI safety from isolated bad prompts to deeper system-design vulnerabilities that could matter in real deployments. Sources: 1. Internal Safety Collapse in Frontier LLMs https://arxiv.org/pdf/2603.23509 2. Concrete Problems in AI Safety — Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, Dan Mané, 2016 https://scholar.google.com/scholar?q=Concrete+Problems+in+AI+Safety 3. Challenges in Deploying Machine Learning: A Survey of Case Studies — Saleema Amershi, Andrew Begel, Christian Bird, Robert DeLine, Harald Gall, Ece Kamar, Nachiappan Nagappan, Besmira Nushi, Thomas Zimmermann, 2019 https://scholar.google.com/scholar?q=Challenges+in+Deploying+Machine+Learning:+A+Survey+of+Case+Studies 4. Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned — Deep Ganguli and collaborators, 2022 https://scholar.google.com/scholar?q=Red+Teaming+Language+Models+to+Reduce+Harms:+Methods,+Scaling+Behaviors,+and+Lessons+Learned 5. LLM Agents: A Survey — Xiaoge Wang and collaborators, 2024 https://scholar.google.com/scholar?q=LLM+Agents:+A+Survey 6. RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models — Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, Noah A. Smith, 2020 https://scholar.google.com/scholar?q=RealToxicityPrompts:+Evaluating+Neural+Toxic+Degeneration+in+Language+Models 7. Universal and Transferable Adversarial Attacks on Aligned Language Models — Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J. Zico Kolter, Matt Fredrikson, 2023 https://scholar.google.com/scholar?q=Universal+and+Transferable+Adversarial+Attacks+on+Aligned+Language+Models 8. HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal — Researchers from the Center for AI Safety and collaborators, 2024 https://scholar.google.com/scholar?q=HarmBench:+A+Standardized+Evaluation+Framework+for+Automated+Red+Teaming+and+Robust+Refusal 9. JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models — Researchers from the jailbreak evaluation community, 2024 https://scholar.google.com/scholar?q=JailbreakBench:+An+Open+Robustness+Benchmark+for+Jailbreaking+Large+Language+Models 10. Constitutional AI: Harmlessness from AI Feedback — Yuntao Bai et al., 2022 https://scholar.google.com/scholar?q=Constitutional+AI:+Harmlessness+from+AI+Feedback 11. Training language models to follow instructions with human feedback — Long Ouyang et al., 2022 https://scholar.google.com/scholar?q=Training+language+models+to+follow+instructions+with+human+feedback 12. The False Promise of Imitating Proprietary LLMs — Tianle Li et al., 2024 https://scholar.google.com/scholar?q=The+False+Promise+of+Imitating+Proprietary+LLMs 13. Many-shot Jailbreaking — Various 2024 authors depending on cited version, 2024 https://scholar.google.com/scholar?q=Many-shot+Jailbreaking 14. AgentDojo — 2025 benchmark authors as cited in the paper's agent-systems discussion, 2025 https://scholar.google.com/scholar?q=AgentDojo 15. Open-source reasoning models can defeat their own safety training during chain-of-thought — Yong and Bach, 2025 https://scholar.google.com/scholar?q=Open-source+reasoning+models+can+defeat+their+own+safety+training+during+chain-of-thought 16. Token-level pattern memorization rather than principled safety reasoning in refusal behavior — Guo et al., 2026 https://scholar.google.com/scholar?q=Token-level+pattern+memorization+rather+than+principled+safety+reasoning+in+refusal+behavior 17. Towards Understanding Safety Alignment: A Mechanistic Perspective from Safety Neurons — approx. unknown authors, recent, likely 2024-2026 https://scholar.google.com/scholar?q=Towards+Understanding+Safety+Alignment:+A+Mechanistic+Perspective+from+Safety+Neurons 18. Interpretable Safety Alignment via SAE-Constructed Low-Rank Subspace Adaptation — approx. unknown authors, recent, likely 2024-2026 https://scholar.google.com/scholar?q=Interpretable+Safety+Alignment+via+SAE-Constructed+Low-Rank+Subspace+Adaptation 19. Safe Transformer: An Explicit Safety Bit For Interpretable And Controllable Alignment — approx. unknown authors, recent, likely 2024-2026 https://scholar.google.com/scholar?q=Safe+Transformer:+An+Explicit+Safety+Bit+For+Interpretable+And+Controllable+Alignment 20. Safety is not only about refusal: Reasoning-enhanced fine-tuning for interpretable llm safety — approx. unknown authors, recent, likely 2024-2026 https://scholar.google.com/scholar?q=Safety+is+not+only+about+refusal:+Reasoning-enhanced+fine-tuning+for+interpretable+llm+safety 21. Eraser: Jailbreaking defense in large language models via unlearning harmful knowledge — approx. unknown authors, recent, likely 2024-2026 https://scholar.google.com/scholar?q=Eraser:+Jailbreaking+defense+in+large+language+models+via+unlearning+harmful+knowledge 22. Towards safer large language models through machine unlearning — approx. unknown authors, recent, likely 2024-2026 https://scholar.google.com/scholar?q=Towards+safer+large+language+models+through+machine+unlearning 23. Beyond single-value metrics: Evaluating and enhancing llm unlearning with cognitive diagnosis — approx. unknown authors, recent, likely 2024-2026 https://scholar.google.com/scholar?q=Beyond+single-value+metrics:+Evaluating+and+enhancing+llm+unlearning+with+cognitive+diagnosis 24. When Refusals Fail: Unstable Safety Mechanisms in Long-Context LLM Agents — approx. unknown authors, recent, likely 2024-2026 https://scholar.google.com/scholar?q=When+Refusals+Fail:+Unstable+Safety+Mechanisms+in+Long-Context+LLM+Agents 25. LPS-Bench: Benchmarking Safety Awareness of Computer-Use Agents in Long-Horizon Planning under Benign and Adversarial Scenarios — approx. unknown authors, recent, likely 2024-2026 https://scholar.google.com/scholar?q=LPS-Bench:+Benchmarking+Safety+Awareness+of+Computer-Use+Agents+in+Long-Horizon+Planning+under+Benign+and+Adversarial+Scenarios 26. Beyond reactive safety: Risk-aware llm alignment via long-horizon simulation — approx. unknown authors, recent, likely 2024-2026 https://scholar.google.com/scholar?q=Beyond+reactive+safety:+Risk-aware+llm+alignment+via+long-horizon+simulation 27. AI Post Transformers: AI Agent Traps and Prompt Injection — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/2026-04-02-ai-agent-traps-and-prompt-injection-7ce4ba.mp3 28. AI Post Transformers: DeepSeek Safety Concerns — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/deepseek-safety-concerns/ 29. AI Post Transformers: Bloom: an open source tool for automated behavioral evaluations — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/bloom-an-open-source-tool-for-automated-behavioral-evaluations/ 30. AI Post Transformers: Probing Scientific General Intelligence of LLMs with Scientist-Aligned Workflows — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/probing-scientific-general-intelligence-of-llms-with-scientist-aligned-workflows/ 31. AI Post Transformers: Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path? — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/superintelligent-agents-pose-catastrophic-risks-can-scientist-ai-offer-a-safer-p/ 32. AI Post Transformers: LLM Benchmark Robustness to Linguistic Variation — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/llm-benchmark-robustness-to-linguistic-variation/ Interactive Visualization: Internal Safety Collapse in Frontier LLMs

  4. HACE 3 DÍAS

    Kosmos AI Scientist for Autonomous Discovery

    This episode explores a 2025 paper on “Kosmos,” an AI scientist designed to carry out long-horizon research by combining literature search, hypothesis generation, code-based data analysis, and persistent memory. The discussion argues that the real innovation is not a smarter standalone language model, but a software architecture that uses agentic workflows and a structured “world model” to preserve evidence, hypotheses, and task state across many steps. It also clarifies key distinctions often blurred in AI discourse, separating AI for scientific discovery from standard deep learning, and distinguishing this kind of world model from the latent simulators used in reinforcement learning. Listeners would find it interesting for its grounded look at what it would actually take for AI to function like a junior computational scientist—and where the genuine advances may lie beyond hype. Sources: 1. Kosmos: An AI Scientist for Autonomous Discovery — Ludovico Mitchener, Angela Yiu, Benjamin Chang, Mathieu Bourdenx, Tyler Nadolski, Arvis Sulovari, Eric C. Landsness, Daniel L. Barabasi, Siddharth Narayanan, Nicky Evans, Shriya Reddy, Martha Foiani, Aizad Kamal, Leah P. Shriver, Fang Cao, Asmamaw T. Wassie, Jon M. Laurent, Edwin Melville-Green, Mayk Caldas, Albert Bou, Kaleigh F. Roberts, Sladjana Zagorac, Timothy C. Orr, Miranda E. Orr, Kevin J. Zwezdaryk, Ali E. Ghareeb, Laurie McCoy, Bruna Gomes, Euan A. Ashley, Karen E. Duff, Tonio Buonassisi, Tom Rainforth, Randall J. Bateman, Michael Skarlinski, Samuel G. Rodriques, Michaela M. Hinks, Andrew D. White, 2025 http://arxiv.org/abs/2511.02824 2. The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery — Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, David Ha and collaborators at Sakana AI, 2024 https://scholar.google.com/scholar?q=The+AI+Scientist:+Towards+Fully+Automated+Open-Ended+Scientific+Discovery 3. Towards an AI co-scientist — Google Research collaborators including teams working on Gemini-based scientific reasoning systems, 2025 https://scholar.google.com/scholar?q=Towards+an+AI+co-scientist 4. Robin: an agentic system for automating scientific discovery in therapeutics — Andrew D. White, Samuel G. Rodriques and collaborators, 2024 https://scholar.google.com/scholar?q=Robin:+an+agentic+system+for+automating+scientific+discovery+in+therapeutics 5. Autonomous chemical research with large language models — Various groups; a representative line includes LLM-driven chemistry agents integrating planning, literature, and lab or simulation tools, 2023-2025 https://scholar.google.com/scholar?q=Autonomous+chemical+research+with+large+language+models 6. Robin — Not fully specified in the excerpt; cited as [1] and described as the authors' previous system, Unknown from excerpt https://scholar.google.com/scholar?q=Robin 7. The AI Scientist — Sakana AI team; cited as [2], Likely 2024 https://scholar.google.com/scholar?q=The+AI+Scientist 8. AI co-scientist — Google team; cited as [3], Likely 2025 https://scholar.google.com/scholar?q=AI+co-scientist 9. Virtual Lab — Cited as [4]; exact authors not given in excerpt, Unknown from excerpt https://scholar.google.com/scholar?q=Virtual+Lab 10. Edison Scientific data analysis agent — Cited as [5]; exact authors not given in excerpt, Unknown from excerpt https://scholar.google.com/scholar?q=Edison+Scientific+data+analysis+agent 11. Edison Scientific literature search agent — Cited as [6]; exact authors not given in excerpt, Unknown from excerpt https://scholar.google.com/scholar?q=Edison+Scientific+literature+search+agent 12. Planner Matters! An Efficient and Memory-Augmented Multi-agent Framework for Long-horizon GUI Planning — approx. recent multi-agent/planning paper, authors unclear from snippet, 2024/2025 https://scholar.google.com/scholar?q=Planner+Matters!+An+Efficient+and+Memory-Augmented+Multi-agent+Framework+for+Long-horizon+GUI+Planning 13. Memory-Driven Agent Planning for Long-Horizon Tasks via Hierarchical Encoding and Dynamic Retrieval — approx. recent agent-memory paper, authors unclear from snippet, 2024/2025 https://scholar.google.com/scholar?q=Memory-Driven+Agent+Planning+for+Long-Horizon+Tasks+via+Hierarchical+Encoding+and+Dynamic+Retrieval 14. Optimus-1: Hybrid multimodal memory empowered agents excel in long-horizon tasks — approx. Optimus-1 authors, exact names unclear, 2024 https://scholar.google.com/scholar?q=Optimus-1:+Hybrid+multimodal+memory+empowered+agents+excel+in+long-horizon+tasks 15. Hallucination mitigation for retrieval-augmented large language models: a review — approx. review authors unclear, 2024/2025 https://scholar.google.com/scholar?q=Hallucination+mitigation+for+retrieval-augmented+large+language+models:+a+review 16. Grounding fallacies misrepresenting scientific publications in evidence — approx. authors unclear from snippet, 2024/2025 https://scholar.google.com/scholar?q=Grounding+fallacies+misrepresenting+scientific+publications+in+evidence 17. Zero-shot scientific claim verification using LLMs and citation text — approx. authors unclear from snippet, 2024/2025 https://scholar.google.com/scholar?q=Zero-shot+scientific+claim+verification+using+LLMs+and+citation+text 18. Learning fine-grained grounded citations for attributed large language models — approx. authors unclear from snippet, 2024/2025 https://scholar.google.com/scholar?q=Learning+fine-grained+grounded+citations+for+attributed+large+language+models 19. The cost of dynamic reasoning: Demystifying AI agents and test-time scaling from an AI infrastructure perspective — approx. authors unclear from snippet, 2025 https://scholar.google.com/scholar?q=The+cost+of+dynamic+reasoning:+Demystifying+AI+agents+and+test-time+scaling+from+an+AI+infrastructure+perspective 20. The illusion of diminishing returns: Measuring long horizon execution in LLMs — approx. authors unclear from snippet, 2024/2025 https://scholar.google.com/scholar?q=The+illusion+of+diminishing+returns:+Measuring+long+horizon+execution+in+LLMs 21. 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 22. 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/ 23. AI Post Transformers: LeWorldModel: Stable Joint-Embedding World Models from Pixels — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/2026-03-25-leworldmodel-stable-joint-embedding-worl-650f9f.mp3 24. AI Post Transformers: Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Model — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/hallucination-to-truth-a-review-of-fact-checking-and-factuality-evaluation-in-la/ 25. AI Post Transformers: MetaGraph: knowledge graphs from financial NLP — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/metagraph-knowledge-graphs-from-financial-nlp/ 26. AI Post Transformers: Survey of Emerging Topics in AI and Robotics — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/survey-of-emerging-topics-in-ai-and-robotics/ 27. AI Post Transformers: The Endless Gym: Training Terminal Agents — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/the-endless-gym-training-terminal-agents/ 28. AI Post Transformers: Bloom: an open source tool for automated behavioral evaluations — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/bloom-an-open-source-tool-for-automated-behavioral-evaluations/ Interactive Visualization: Kosmos AI Scientist for Autonomous Discovery

  5. HACE 3 DÍAS

    MEMSEARCHER: Reinforcement Learning for LLM Memory Management

    This episode explores a 2025 paper on MemSearcher, an LLM search agent that replaces full trajectory replay with a compact learned memory, trained end-to-end with reinforcement learning. It explains how this approach targets a core weakness of ReAct-style agents—ever-growing context windows that increase cost, latency, and noise—and contrasts it with both vanilla ReAct and Search-R1, which improves search behavior without explicitly learning what to retain. The discussion connects reinforcement learning, retrieval-augmented generation, agent memory systems, and reasoning-budget control, arguing that context management should be treated as part of the learned policy rather than an afterthought. Listeners interested in AI agents will find it compelling because it frames memory compression not just as an efficiency trick, but as a potentially important source of better search and reasoning performance. Sources: 1. MemSearcher: Training LLMs to Reason, Search and Manage Memory via End-to-End Reinforcement Learning — Qianhao Yuan, Jie Lou, Zichao Li, Jiawei Chen, Yaojie Lu, Hongyu Lin, Le Sun, Debing Zhang, Xianpei Han, 2025 http://arxiv.org/abs/2511.02805 2. ReAct: Synergizing Reasoning and Acting in Language Models — Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao, 2023 https://scholar.google.com/scholar?q=ReAct:+Synergizing+Reasoning+and+Acting+in+Language+Models 3. Search-R1 — Jin et al., 2025 https://scholar.google.com/scholar?q=Search-R1 4. DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models — Daya Guo, Dejian Yang, Haowei Zhang, et al., 2024 https://scholar.google.com/scholar?q=DeepSeekMath:+Pushing+the+Limits+of+Mathematical+Reasoning+in+Open+Language+Models 5. 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 6. Training Language Models to Self-Correct via Reinforcement Learning — Tianjun Zhang, et al., 2024 https://scholar.google.com/scholar?q=Training+Language+Models+to+Self-Correct+via+Reinforcement+Learning 7. 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 8. Generative Agents: Interactive Simulacra of Human Behavior — Joon Sung Park, Joseph O'Brien, Carrie Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein, Michael Terry, 2023 https://scholar.google.com/scholar?q=Generative+Agents:+Interactive+Simulacra+of+Human+Behavior 9. ACon: Optimizing Context Compression for Long-Horizon LLM Agents — approx. unknown from snippet, 2025 https://scholar.google.com/scholar?q=ACon:+Optimizing+Context+Compression+for+Long-Horizon+LLM+Agents 10. Active Context Compression: Autonomous Memory Management in LLM Agents — approx. unknown from snippet, 2025 https://scholar.google.com/scholar?q=Active+Context+Compression:+Autonomous+Memory+Management+in+LLM+Agents 11. From Lossy to Verified: A Provenance-Aware Tiered Memory for Agents — approx. unknown from snippet, 2025 https://scholar.google.com/scholar?q=From+Lossy+to+Verified:+A+Provenance-Aware+Tiered+Memory+for+Agents 12. GRPO-: Credit Assignment Improves LLM Reasoning — approx. unknown from snippet, 2025 https://scholar.google.com/scholar?q=GRPO-:+Credit+Assignment+Improves+LLM+Reasoning 13. InT: Self-Proposed Interventions Enable Credit Assignment in LLM Reasoning — approx. unknown from snippet, 2025 https://scholar.google.com/scholar?q=InT:+Self-Proposed+Interventions+Enable+Credit+Assignment+in+LLM+Reasoning 14. CAPO: Towards Enhancing LLM Reasoning through Generative Credit Assignment — approx. unknown from snippet, 2025 https://scholar.google.com/scholar?q=CAPO:+Towards+Enhancing+LLM+Reasoning+through+Generative+Credit+Assignment 15. AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning — approx. unknown from snippet, 2025 https://scholar.google.com/scholar?q=AgentGym-RL:+Training+LLM+Agents+for+Long-Horizon+Decision+Making+through+Multi-Turn+Reinforcement+Learning 16. 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/ 17. 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 18. 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/ 19. 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/ 20. AI Post Transformers: Doc-to-LoRA: Internalizing Context as LoRA — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/2026-03-29-doc-to-lora-internalizing-context-as-lor-8dd5ec.mp3 Interactive Visualization: MEMSEARCHER: Reinforcement Learning for LLM Memory Management

  6. HACE 3 DÍAS

    QVCache for Semantic Caching in ANN Search

    This episode explores QVCache, a query-aware semantic cache designed to sit in front of any approximate nearest neighbor (ANN) backend and speed up vector search without significantly hurting recall. It explains why exact-match caching fails for embeddings, introduces the idea of temporal-semantic locality—where nearby-in-time queries are also nearby in embedding space—and argues that this pattern can let systems reuse recent ANN results instead of repeatedly paying the full latency and I/O cost of high-recall search. The discussion also grounds the paper in the broader vector retrieval landscape, covering recall@k, HNSW, Product Quantization, DiskANN, FAISS, and the role of vector databases in RAG and large-scale serving. Listeners would find it interesting for its practical systems focus: rather than proposing yet another index, the paper asks whether a backend-agnostic cache can deliver real speedups for production retrieval workloads. Sources: 1. QVCache: A Query-Aware Vector Cache — Anıl Eren Göçer, Ioanna Tsakalidou, Hamish Nicholson, Kyoungmin Kim, Anastasia Ailamaki, 2026 http://arxiv.org/abs/2602.02057 2. A Survey on Nearest Neighbor Search Methods — Mohammad A. N. Arefin, et al. (survey literature varies by edition; commonly cited broad surveys include authors such as Li, Amsaleg, Houle, and others in the NNS literature), 2018 https://scholar.google.com/scholar?q=A+Survey+on+Nearest+Neighbor+Search+Methods 3. Product Quantization for Nearest Neighbor Search — Hervé Jégou, Matthijs Douze, Cordelia Schmid, 2011 https://scholar.google.com/scholar?q=Product+Quantization+for+Nearest+Neighbor+Search 4. Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs — Yu. A. Malkov, D. A. Yashunin, 2018 https://scholar.google.com/scholar?q=Efficient+and+Robust+Approximate+Nearest+Neighbor+Search+Using+Hierarchical+Navigable+Small+World+Graphs 5. DiskANN: Fast Accurate Billion-Point Nearest Neighbor Search on a Single Node — Suhas Jayaram Subramanya, Devvrit, Rohan Kadekodi, Ravishankar Krishnaswamy, Harsha Vardhan Simhadri, 2019 https://scholar.google.com/scholar?q=DiskANN:+Fast+Accurate+Billion-Point+Nearest+Neighbor+Search+on+a+Single+Node 6. The FAISS Library — Jeff Johnson, Matthijs Douze, Hervé Jégou, 2021 https://scholar.google.com/scholar?q=The+FAISS+Library 7. Vespa: Serving Large-Scale Machine-Learned Relevance — Jon Bratseth and colleagues, 2023 https://scholar.google.com/scholar?q=Vespa:+Serving+Large-Scale+Machine-Learned+Relevance 8. pgvector: Open-Source Vector Similarity Search for Postgres — Andrew Kane, 2023 https://scholar.google.com/scholar?q=pgvector:+Open-Source+Vector+Similarity+Search+for+Postgres 9. Milvus: A Purpose-Built Vector Data Management System — Milvus/Zilliz engineering team and collaborators, 2021 https://scholar.google.com/scholar?q=Milvus:+A+Purpose-Built+Vector+Data+Management+System 10. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting — Yoav Freund, Robert E. Schapire, 1997 https://scholar.google.com/scholar?q=A+Decision-Theoretic+Generalization+of+On-Line+Learning+and+an+Application+to+Boosting 11. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization — John Duchi, Elad Hazan, Yoram Singer, 2011 https://scholar.google.com/scholar?q=Adaptive+Subgradient+Methods+for+Online+Learning+and+Stochastic+Optimization 12. Ad Click Prediction: a View from the Trenches — H. Brendan McMahan, Gary Holt, David Sculley, Michael Young, Dietmar Ebner, Julian Grady, et al., 2013 https://scholar.google.com/scholar?q=Ad+Click+Prediction:+a+View+from+the+Trenches 13. Bandit Algorithms for Website Optimization — John White, 2012 https://scholar.google.com/scholar?q=Bandit+Algorithms+for+Website+Optimization 14. The Case for Learned Index Structures — Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, and Neoklis Polyzotis, 2018 https://scholar.google.com/scholar?q=The+Case+for+Learned+Index+Structures 15. Semantic Caching and Query Processing — Qiong Luo, Jeffrey F. Naughton, Rajasekar Krishnamurthy, Pei Cao, and Yunrui Li, 2003 https://scholar.google.com/scholar?q=Semantic+Caching+and+Query+Processing 16. GPTCache: An Open-Source Semantic Cache for LLM Applications Enabling Faster Answers and Cost Savings — Zilliz / GPTCache contributors, 2023 https://scholar.google.com/scholar?q=GPTCache:+An+Open-Source+Semantic+Cache+for+LLM+Applications+Enabling+Faster+Answers+and+Cost+Savings 17. Adaptive Similarity Search Caching (or related similarity-caching theory cited as [12] and [38]) — As cited in the paper, Unknown from excerpt https://scholar.google.com/scholar?q=Adaptive+Similarity+Search+Caching+(or+related+similarity-caching+theory+cited+as+[12]+and+[38]) 18. SPANN: Highly-efficient Billion-scale Approximate Nearest Neighbor Search — Qi Chen, Bingbing Wang, et al., 2021 https://scholar.google.com/scholar?q=SPANN:+Highly-efficient+Billion-scale+Approximate+Nearest+Neighbor+Search 19. Optimizing SSD-Resident Graph Indexing for High-Throughput Vector Search — approx. VeloANN authors, exact author list not recoverable from snippet, recent (likely 2024-2025) https://scholar.google.com/scholar?q=Optimizing+SSD-Resident+Graph+Indexing+for+High-Throughput+Vector+Search 20. Quake: Adaptive indexing for vector search — approx. Quake authors, exact author list not recoverable from snippet, recent (likely 2024-2025) https://scholar.google.com/scholar?q=Quake:+Adaptive+indexing+for+vector+search 21. Vector Search for the Future: From Memory-Resident, Static Heterogeneous Storage, to Cloud-Native Architectures — approx. survey/tutorial authors, exact author list not recoverable from snippet, recent https://scholar.google.com/scholar?q=Vector+Search+for+the+Future:+From+Memory-Resident,+Static+Heterogeneous+Storage,+to+Cloud-Native+Architectures 22. GPT Semantic Cache: Reducing LLM Costs and Latency via Semantic Embedding Caching — approx. GPT semantic cache authors, exact author list not recoverable from snippet, recent https://scholar.google.com/scholar?q=GPT+Semantic+Cache:+Reducing+LLM+Costs+and+Latency+via+Semantic+Embedding+Caching 23. AI Post Transformers: TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/2026-03-25-turboquant-online-vector-quantiz-1967b7.mp3 24. AI Post Transformers: Sentence-BERT: Siamese Networks for Sentence Embeddings — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/sentence-bert-siamese-networks-for-sentence-embeddings/ 25. AI Post Transformers: MEMRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/memrl-self-evolving-agents-via-runtime-reinforcement-learning-on-episodic/ 26. AI Post Transformers: Doc-to-LoRA: Internalizing Context as LoRA — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/2026-03-29-doc-to-lora-internalizing-context-as-lor-8dd5ec.mp3 Interactive Visualization: QVCache for Semantic Caching in ANN Search

  7. HACE 3 DÍAS

    Recursive Language Models for Arbitrarily Long Prompts

    This episode explores a 2026 MIT CSAIL paper on “Recursive Language Models,” which argues that handling very long prompts may be better framed as a systems problem than a bigger-context-window problem. It explains the distinction between hard context overflow and “context rot,” where models technically fit long inputs but increasingly fail to use them reliably, challenging the assumption that larger windows automatically mean better memory. The discussion connects this idea to inference-time compute scaling, chain-of-thought, tree search, and agentic AI, showing how models can iteratively inspect external information, use tools, and update state instead of forcing everything through a single forward pass. Listeners would find it interesting because it offers a concrete alternative to the current long-context arms race and suggests a different path for building more capable, reliable language systems. Sources: 1. Recursive Language Models — Alex L. Zhang, Tim Kraska, Omar Khattab, 2025 http://arxiv.org/abs/2512.24601 2. Theoretical expressive power of reasoning models — Merrill & Sabharwal, 2024 https://scholar.google.com/scholar?q=Theoretical+expressive+power+of+reasoning+models 3. Context Rot — Hong et al., 2025 https://scholar.google.com/scholar?q=Context+Rot 4. Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval — Khattab et al., 2021 https://scholar.google.com/scholar?q=Baleen:+Robust+Multi-Hop+Reasoning+at+Scale+via+Condensed+Retrieval 5. OpenAI context compaction work — OpenAI, 2025 https://scholar.google.com/scholar?q=OpenAI+context+compaction+work 6. Smith long-context compaction work — Smith, 2025 https://scholar.google.com/scholar?q=Smith+long-context+compaction+work 7. Wu et al. task-specific long-context methods — Wu et al., 2021 https://scholar.google.com/scholar?q=Wu+et+al.+task-specific+long-context+methods 8. Wu et al. context compaction / long-context scaffolding — Wu et al., 2025 https://scholar.google.com/scholar?q=Wu+et+al.+context+compaction+/+long-context+scaffolding 9. Anthropic self-delegation / sub-agent work — Anthropic, 2025 https://scholar.google.com/scholar?q=Anthropic+self-delegation+/+sub-agent+work 10. Schroeder et al. self-delegation work — Schroeder et al., 2025 https://scholar.google.com/scholar?q=Schroeder+et+al.+self-delegation+work 11. Sun et al. self-delegation work — Sun et al., 2025 https://scholar.google.com/scholar?q=Sun+et+al.+self-delegation+work 12. Deep research benchmark/work — Chen et al., 2025 https://scholar.google.com/scholar?q=Deep+research+benchmark/work 13. Information aggregation benchmark/work — Bertsch et al., 2025 https://scholar.google.com/scholar?q=Information+aggregation+benchmark/work 14. Code repository understanding benchmark/work — Bai et al., 2025 https://scholar.google.com/scholar?q=Code+repository+understanding+benchmark/work 15. Qwen3 technical report — Yang et al., 2025 https://scholar.google.com/scholar?q=Qwen3+technical+report 16. Core Context Aware Transformers for Long Context Language Modeling — approx. 2024 long-context transformer authors, 2024 https://scholar.google.com/scholar?q=Core+Context+Aware+Transformers+for+Long+Context+Language+Modeling 17. Challenges in Deploying Long-Context Transformers: A Theoretical Peak Performance Analysis — approx. 2024 systems/theory authors, 2024 https://scholar.google.com/scholar?q=Challenges+in+Deploying+Long-Context+Transformers:+A+Theoretical+Peak+Performance+Analysis 18. Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models — approx. 2024 dialogue-memory authors, 2024 https://scholar.google.com/scholar?q=Recursively+Summarizing+Enables+Long-Term+Dialogue+Memory+in+Large+Language+Models 19. Augmenting Language Models with Long-Term Memory — approx. LONGMEM authors, 2024 https://scholar.google.com/scholar?q=Augmenting+Language+Models+with+Long-Term+Memory 20. Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach — approx. 2024/2025 comparative-study authors, 2024/2025 https://scholar.google.com/scholar?q=Retrieval+Augmented+Generation+or+Long-Context+LLMs?+A+Comprehensive+Study+and+Hybrid+Approach 21. LongRAG: Enhancing Retrieval-Augmented Generation with Long-Context LLMs — approx. LongRAG authors, 2024/2025 https://scholar.google.com/scholar?q=LongRAG:+Enhancing+Retrieval-Augmented+Generation+with+Long-Context+LLMs 22. Let's (Not) Just Put Things in Context: Test-Time Training for Long-Context LLMs — approx. 2025 test-time-training authors, 2025 https://scholar.google.com/scholar?q=Let's+(Not)+Just+Put+Things+in+Context:+Test-Time+Training+for+Long-Context+LLMs 23. Z1: Efficient Test-Time Scaling with Code — approx. Z1 authors, 2025 https://scholar.google.com/scholar?q=Z1:+Efficient+Test-Time+Scaling+with+Code 24. A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well? — approx. 2025 survey authors, 2025 https://scholar.google.com/scholar?q=A+Survey+on+Test-Time+Scaling+in+Large+Language+Models:+What,+How,+Where,+and+How+Well? 25. AI Post Transformers: NVIDIA: TTT-E2E: Unlocking Long-Context Learning via End-to-End Test-Time Training — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/nvidia-ttt-e2e-unlocking-long-context-learning-via-end-to-end-test-time-training/ 26. AI Post Transformers: Generalist Reward Modeling with Inference-Time Scaling — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/generalist-reward-modeling-with-inference-time-scaling/ 27. AI Post Transformers: AI Agent Traps and Prompt Injection — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/2026-04-02-ai-agent-traps-and-prompt-injection-7ce4ba.mp3 28. 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 29. AI Post Transformers: Experiential Reinforcement Learning: Internalizing Reflection for Better Policy Training — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/experiential-reinforcement-learning-internalizing-reflection-for-better-policy-t/ Interactive Visualization: Recursive Language Models for Arbitrarily Long Prompts

  8. HACE 4 DÍAS

    Batch-Aware Expert Routing for Faster MoE Decoding

    This episode explores a practical systems paper on speeding up Mixture-of-Experts language models at inference time by changing how tokens are routed during decoding, without any retraining. It explains why MoE models, despite using sparse per-token computation, can still be slow in real-world serving because small decode batches activate a large union of different experts, making inference memory-bound due to irregular weight loading. The discussion highlights the paper’s central argument that routing should be batch-aware rather than token-local, so expert choices account for which experts are already being loaded for other tokens in the batch. Listeners would find it interesting for its clear explanation of the gap between MoE’s theoretical efficiency and deployment reality, and for its focus on a low-cost serving optimization with direct economic impact on LLM inference. Sources: 1. Opportunistic Expert Activation: Batch-Aware Expert Routing for Faster Decode Without Retraining — Costin-Andrei Oncescu, Qingyang Wu, Wai Tong Chung, Robert Wu, Bryan Gopal, Junxiong Wang, Tri Dao, Ben Athiwaratkun, 2025 http://arxiv.org/abs/2511.02237 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:+The+Sparsely-Gated+Mixture-of-Experts+Layer 3. Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity — William Fedus, Barret Zoph, Noam Shazeer, 2021 https://scholar.google.com/scholar?q=Switch+Transformers:+Scaling+to+Trillion+Parameter+Models+with+Simple+and+Efficient+Sparsity 4. MegaBlocks: Efficient Sparse Training with Mixture-of-Experts — Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, Yuxiong He and collaborators, 2023 https://scholar.google.com/scholar?q=MegaBlocks:+Efficient+Sparse+Training+with+Mixture-of-Experts 5. Opportunistic Expert Activation: Batch-Aware Expert Routing for Faster Decode Without Retraining — Costin-Andrei Oncescu, Qingyang Wu, Wai Tong Chung, Robert Wu, Bryan Gopal, Junxiong Wang, Tri Dao, Ben Athiwaratkun, 2025 https://scholar.google.com/scholar?q=Opportunistic+Expert+Activation:+Batch-Aware+Expert+Routing+for+Faster+Decode+Without+Retraining 6. Efficient Memory Management for Large Language Model Serving with PagedAttention — Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Hao Zhang, Eric Gonzalez, Ion Stoica, Joseph E. Gonzalez, 2023 https://scholar.google.com/scholar?q=Efficient+Memory+Management+for+Large+Language+Model+Serving+with+PagedAttention 7. SGLang: Efficient Execution of Structured Language Model Programs — Lianmin Zheng and collaborators, 2024 https://scholar.google.com/scholar?q=SGLang:+Efficient+Execution+of+Structured+Language+Model+Programs 8. DeepSeek-V3 Technical Report — DeepSeek-AI / Liu et al., 2024 https://scholar.google.com/scholar?q=DeepSeek-V3+Technical+Report 9. Kimi K2 Technical Report — Kimi Team, 2025 https://scholar.google.com/scholar?q=Kimi+K2+Technical+Report 10. Qwen3 Technical Report — Yang et al., 2025 https://scholar.google.com/scholar?q=Qwen3+Technical+Report 11. The Roofline Model: A Pedagogical Tool for Program Analysis and Optimization — Samuel Williams, Andrew Waterman, David Patterson, 2009 https://scholar.google.com/scholar?q=The+Roofline+Model:+A+Pedagogical+Tool+for+Program+Analysis+and+Optimization 12. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness — Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, Christopher Ré, 2022 https://scholar.google.com/scholar?q=FlashAttention:+Fast+and+Memory-Efficient+Exact+Attention+with+IO-Awareness 13. Moe-Infinity: Efficient MoE Inference on Personal Machines with Sparsity-Aware Expert Cache — approx. recent systems paper, exact authors unclear from snippet, 2024/2025 https://scholar.google.com/scholar?q=Moe-Infinity:+Efficient+MoE+Inference+on+Personal+Machines+with+Sparsity-Aware+Expert+Cache 14. Diff-MoE: Efficient Batched MoE Inference with Priority-Driven Differential Expert Caching — approx. recent systems paper, exact authors unclear from snippet, 2024/2025 https://scholar.google.com/scholar?q=Diff-MoE:+Efficient+Batched+MoE+Inference+with+Priority-Driven+Differential+Expert+Caching 15. SliceMoE: Bit-Sliced Expert Caching under Miss-Rate Constraints for Efficient MoE Inference — approx. recent systems paper, exact authors unclear from snippet, 2024/2025 https://scholar.google.com/scholar?q=SliceMoE:+Bit-Sliced+Expert+Caching+under+Miss-Rate+Constraints+for+Efficient+MoE+Inference 16. A Survey on Inference Optimization Techniques for Mixture of Experts Models — approx. recent survey, exact authors unclear from snippet, 2024/2025 https://scholar.google.com/scholar?q=A+Survey+on+Inference+Optimization+Techniques+for+Mixture+of+Experts+Models 17. Rewiring Experts on the Fly: Continuous Rerouting for Better Online Adaptation in Mixture-of-Expert Models — approx. recent paper, exact authors unclear from snippet, 2024/2025 https://scholar.google.com/scholar?q=Rewiring+Experts+on+the+Fly:+Continuous+Rerouting+for+Better+Online+Adaptation+in+Mixture-of-Expert+Models 18. Stabilizing MoE Reinforcement Learning by Aligning Training and Inference Routers — approx. recent paper, exact authors unclear from snippet, 2024/2025 https://scholar.google.com/scholar?q=Stabilizing+MoE+Reinforcement+Learning+by+Aligning+Training+and+Inference+Routers 19. AI Post Transformers: Switch Transformers: Trillion Parameter Models with Sparsity — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/switch-transformers-trillion-parameter-models-with-sparsity/ 20. AI Post Transformers: LFM2-8B-A1B: Efficient On-Device Mixture-of-Experts — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/lfm2-8b-a1b-efficient-on-device-mixture-of-experts/ 21. AI Post Transformers: FlexGen: High-Throughput LLM Inference on a Single GPU — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/flexgen-high-throughput-llm-inference-on-a-single-gpu/ 22. AI Post Transformers: FlashAttention-2: Faster Attention with Better Parallelism — Hal Turing & Dr. Ada Shannon, 2025 https://podcast.do-not-panic.com/episodes/flashattention-2-faster-attention-with-better-parallelism/ 23. 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 24. AI Post Transformers: FAST26: Bidaw: Enhancing Key-Value Caching for Interactive LLM Serving via Bidirectional — Hal Turing & Dr. Ada Shannon, 2026 https://podcast.do-not-panic.com/episodes/fast26-bidaw-enhancing-key-value-caching-for-interactive-llm-serving-via-bidirec/ Interactive Visualization: Batch-Aware Expert Routing for Faster MoE Decoding

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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.

Información

  • Creador
    mcgrof
  • Años de actividad
    2025 - 2026
  • Episodios
    510
  • Clasificación
    Apto
  • Mostrar sitio web
    AI Post Transformers

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