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A lot of people in AI treat evolution as a dumb fallback, basically random search for when you can't take a gradient. Akarsh Kumar thinks that is wrong. Selection hangs on to partial solutions, so mutations only need to be useful about 1% of the time for the search to keep making progress.Akarsh is a PhD student at MIT working with Phillip Isola, works with Sakana AI, and is first author of the Fractured Entangled Representation paper with Kenneth Stanley, Jeff Clune and Joel Lehman. He tells Tim Scarfe why the path a learner takes may shape the structure of what it learns, and why that is a different way of looking at intelligence from the statistical one.Most of the conversation is about ASAL, the method he led for searching whole spaces of artificial worlds. Rather than predict what a rule will do, ASAL runs the simulation and asks a foundation model what happened. Mapped across all 262,144 Life-like rules, the most interesting worlds sit on one small island. Along the way: Game of Life, Lenia (with a clip from its creator, Bert Chan), neural cellular automata, Boids, Particle Life and the emergence of persistence.---TIMESTAMPS:00:00:00 Intro: artificial life, ASAL and Core War in four minutes00:04:19 Life as it could be, not just as it is00:05:46 Why the order you learn things in matters00:08:33 No shortcuts: Wolfram, novelty search and regularisation00:10:44 Kenneth Stanley: the path matters, not just the destination00:11:31 Statistical intelligence vs regularity-based intelligence00:13:49 Artificial chemistry and other possible universes00:15:59 Convergent patterns: shadows of the substrate?00:18:02 How Conway's Game of Life works00:20:49 Change one cell, change everything?00:22:31 Lenia (with Bert Chan) and neural cellular automata00:25:26 Boids, Particle Life and cell-like creatures00:28:47 Persistence, entropy and what life is00:30:15 ASAL: a foundation model as the critic00:34:04 Impostors inside the simulation00:36:50 From primordial soup to alien animals00:38:26 262,144 rules and the island of open-endedness00:40:17 From artificial life to AGI00:41:35 Core War: Game of Thrones, Turing edition00:43:22 LLMs as the mutation step: evolving warriors00:46:03 Evolution is anything but random---REFERENCES:paper:[00:04:19] ASAL (Kumar et al.)https://arxiv.org/abs/2412.17799[00:08:08] Assembly theory https://www.nature.com/articles/s41586-023-06600-9[00:09:54] FEP paperhttps://arxiv.org/abs/2505.11581[00:22:38] Lenia: Biology of Artificial Life (Bert Chan)https://arxiv.org/abs/1812.05433[00:23:20] Growing Neural Cellular Automata (Mordvintsev et al.)https://distill.pub/2020/growing-ca/[00:41:35] Digital Red Queen: Core War with LLMs (Kumar et al.)https://arxiv.org/abs/2601.03335[00:43:42] MAP-Elites https://arxiv.org/abs/1504.04909[00:44:55] AlphaEvolve https://arxiv.org/abs/2506.13131[00:47:03] AutoML-Zero (Real et al.)https://arxiv.org/abs/2003.03384book:[00:07:04] Why Greatness Cannot Be Planned (Stanley and Lehman)https://link.springer.com/book/10.1007/978-3-319-15524-1tool:[00:18:12] Conway's Game of Lifehttps://en.wikipedia.org/wiki/Conway%27s_Game_of_Life[00:25:27] Boids (Craig Reynolds)https://www.red3d.com/cwr/boids/[00:27:12] Particle Life (Tom Mohr)https://github.com/tom-mohr/particle-life[00:41:50] Core Warhttps://corewar.co.uk/other:[00:08:49] Computational irreducibility (Stephen Wolfram)https://www.wolframscience.com/nks/p737--computational-irreducibility/[00:10:46] MLST: Why Every AI Model Is an Impostor (Kenneth Stanley, FER documentary)https://www.youtube.com/watch?v=o1q6Hhz0MAg[00:28:30] MLST: Blaise Agüera y Arcas on life emerging from codehttps://www.youtube.com/watch?v=rMSEqJ_4EBk---LINKS:Akarsh Kumar: https://akarshkumar.com/ASAL project page and demos: https://pub.sakana.ai/asal/Digital Red Queen project page: https://sakana.ai/drq/RESCRIPT:https://app.rescript.info/public/share/Qfv3T0EVzqOXL_CYYeRr9Blv8HnNm4JsXBh7ByFjJbc

Tsung-Hsien (Shawn) Wen, CTO of PolyAI, tells Tim Scarfe why voice agents are harder than text agents. Voice adds time, and a good conversation depends on adapting to the person on the line, not just on reasoning to the best answer. Shawn describes an audio-native model (Dialog-RSN-1) that first predicts a turn-taking signal, then replies in text with citations, and writes the transcript last so enterprises can audit it.Along the way: training on real, noisy calls with synthetic noise added, and why over-cleaned audio made the new model worse. Latency, and what a voice agent should do while it thinks. Why a voice with a hint of regional accent beats a generic one. Why public benchmarks fall short for voice, why enterprises want to own their agent harness, and whether behaviour belongs in the harness or in the weights.The last stretch is about working with agents: cognitive debt, the shift from producing content to checking it, Wispr Flow, building tools that agents can use, and whether slop is in the eye of the reader.This episode was produced in partnership with PolyAI.https://poly.ai CHAPTERS0:00 Why voice agents are harder than text4:27 What enterprises want, and why PolyAI built its own model9:04 How an audio-native voice model works15:21 Training data, spectrograms and synthetic noise20:36 The cocktail party problem and the future of turn-taking25:21 Latency, adaptive reasoning and keeping callers' trust31:33 Voices, personality and the uncanny valley36:32 How do you benchmark a voice agent?42:00 Harness engineering and owning the intelligence45:09 Well-specified problems and auditable agents49:46 Weight adaptation and cognitive debt56:31 Agents at work: Wispr Flow, voice and tool building1:02:50 The next decade of voice, and what counts as slopREFERENCESThe Bitter Lesson: http://www.incompleteideas.net/IncIdeas/BitterLesson.html [9:05]Retrieval-augmented generation: https://arxiv.org/abs/2005.11401 [13:23]Mel scale: https://en.wikipedia.org/wiki/Mel_scale [17:16]Victor Zue: https://en.wikipedia.org/wiki/Victor_Zue [17:45]Cocktail party effect: https://en.wikipedia.org/wiki/Cocktail_party_effect [20:39]Speaker diarisation: https://en.wikipedia.org/wiki/Speaker_diarisation [21:16]

Leonardo de Moura created Lean and co-created Z3. ---This episode is sponsored by Parallel.Parallel, where agents find answers: web search, extraction and deep research APIs built for AI agents.Start free with the Parallel MCP server and $5 of credits every month: https://parallel.ai/mlst?utm_source=creator&utm_medium=podcast&utm_content=MLST---Tim Scarfe talks with Leo about how Lean escaped its original audience, why dependent types and Mathlib made it useful to working mathematicians, and what happens when formal verification leaves the lab. De Moura explains the small trusted kernel and independent checkers, and gives his account of the recent Collatz incident, in which a purported proof was accepted by both Lean's official kernel and Nanoda, apparently by exploiting a different bug in each.---TIMESTAMPS:00:00:00 Cold open: the green checkmark can lie00:00:59 Cathedral or bazaar: who controls Lean's core?00:04:55 Why Lean's core stays small and protected00:08:07 The Slack purge, Brandolini's law and the Lean FRO00:11:12 The Collatz exploit: two kernels, two bugs00:16:44 More kernels, reward hacking and safety by transparency00:21:04 Sponsor: Parallel00:21:59 Kim Morrison, Claude and the zlib proof00:25:25 Can we specify complex systems?00:28:04 Specs change: proofs are cheaper to redo with AI00:31:27 From Lean 1 to Lean 400:34:57 Dependent types in plain terms00:37:25 Lean 4's extensibility and Mathlib's growth00:41:44 Mathlib as infrastructure: Formal Frontiers00:44:15 Creativity, abstraction and nut-sniping00:48:46 Breadcrumbs, not learning: what AI agents lack00:53:03 Competence without comprehension, and verified guardrails00:56:21 Is the human still the author?01:01:44 AlphaProof, LLMs and why certificates still matter01:06:38 What's next for Lean, and its legacy01:11:42 How to start learning Lean---REFERENCES:tool:[00:00:48] Leanhttps://lean-lang.org/[00:01:24] Mathlibhttps://github.com/leanprover-community/mathlib4[00:12:09] nanoda_libhttps://github.com/ammkrn/nanoda_lib[00:12:19] CollatzLeanhttps://github.com/xrchz/CollatzLean/blob/a79357462a33d2a6babd4cf6c8d8bcd25425d653/README.md[00:13:06] Lean issue 14576https://github.com/leanprover/lean4/issues/14576[00:13:21] Lean pull request 14577https://github.com/leanprover/lean4/pull/14577[00:13:45] nanoda_lib pull request 22https://github.com/ammkrn/nanoda_lib/pull/22[00:15:33] Lean comparatorhttps://github.com/leanprover/comparator[00:18:02] Lean4Leanhttps://github.com/digama0/lean4lean[00:19:50] ARC-AGI-3https://arcprize.org/arc-agi/3[00:21:59] lean-ziphttps://github.com/kim-em/lean-zip[00:29:45] CompCerthttps://compcert.org/[00:29:45] seL4https://www.sel4.org/[00:30:23] Z3https://github.com/Z3Prover/z3[00:38:10] Veilhttps://github.com/verse-lab/veil[00:38:10] Velvethttps://github.com/verse-lab/velvetorganization:[00:00:52] Lean FROhttps://lean-lang.org/fro/[00:42:39] Mathlib Initiativehttps://mathlib-initiative.org/about/person:[00:05:11] Ilya Sergeyhttps://ilyasergey.net/[00:10:31] Joachim Breitnerhttps://www.joachim-breitner.de/[00:32:58] Adam Chlipalahttps://adam.chlipala.net/[00:38:42] Kevin Buzzardhttps://www.ma.imperial.ac.uk/~buzzard/[01:10:16] Terence Taohttps://terrytao.wordpress.com/book:[00:08:19] The Proof in the Codehttps://us.macmillan.com/books/9780374620059/theproofinthecode/other:[00:10:05] Brandolini's lawhttps://en.wikipedia.org/wiki/Brandolini%27s_law[00:45:33] A new result on unit distanceshttps://openai.com/index/model-disproves-discrete-geometry-conjecture/[01:01:08] Fermat's Last Theorem formalisationhttps://imperialcollegelondon.github.io/FLT/paper:[00:34:36] The Lean 4 theorem prover and programming languagehttps://doi.org/10.1007/978-3-030-79876-5_37---RESCRIPT:https://app.rescript.info/share/7d3d4a0059443236a01f6c9acbf4db58https://app.rescript.info/api/public/sessions/b007264c0ce89047/pdf

Weco let an AI coding agent rewrite the harness around another agent for eight days: its code, prompts and tools, while the underlying language model stayed fixed. Tim Scarfe asks Weco co-founder Zhengyao Jiang what the reported gains over two years of human engineering actually demonstrate.The discussion examines AIDE 85's generated code, held-out evaluation and the difficulty of separating useful discoveries from reward hacking. Jiang explains Weco's four levels of recursive self-improvement and compares the experiment with AlphaEvolve and the Darwin Gödel Machine.The limits matter as much as the gains. Jiang explains why the experiment did not establish that the system had become a better improver. The conversation closes with open-ended search, human-designed primitives and Parameter Golf: where does the next useful idea come from when the agent is searching inside a space that people designed?---TIMESTAMPS:00:00:00 Eight days of self-improvement: what counts?00:03:25 AIDE and the puzzle of useful spaghetti code00:08:38 Four levels of recursive self-improvement00:12:02 What AIDE 85 changed and how it was tested00:20:04 AlphaEvolve, Darwin Gödel Machine and the RSI claim00:26:21 Reward hacking and the limits of detection00:33:09 Open-ended search, harness tuning and creativity00:39:43 Parameter Golf and the limits of self-improvement---REFERENCES:organization:[00:00:30] Weco AIhttps://www.weco.ai/other:[00:00:33] AIDE²: The First Evidence of Recursive Self-Improvementhttps://www.weco.ai/blog/first-evidence-of-recursive-self-improvement[00:14:11] Faulty reward functions in the wildhttps://openai.com/index/faulty-reward-functions/[00:29:59] The Hugging Face incident and the road aheadhttps://openai.com/index/hugging-face-incident-and-the-road-ahead/tool:[00:03:29] AIDEhttps://github.com/WecoAI/aideml[00:04:29] MLE-benchhttps://github.com/openai/mle-bench[00:04:33] ALE-Benchhttps://github.com/SakanaAI/ALE-Bench[00:04:52] WeatherBench 2https://github.com/google-research/weatherbench2[00:08:18] ReActhttps://react-lm.github.io/[00:39:43] Parameter Golfhttps://github.com/openai/parameter-golfpaper:[00:20:08] AlphaEvolve: A coding agent for scientific and algorithmic discoveryhttps://arxiv.org/abs/2506.13131v1[00:21:35] Darwin Gödel Machine: Open-Ended Evolution of Self-Improving Agentshttps://arxiv.org/abs/2505.22954v3[00:23:45] Hyperagentshttps://arxiv.org/abs/2603.19461v1[00:27:01] SpecBench: Measuring Reward Hacking in Long-Horizon Coding Agentshttps://arxiv.org/abs/2605.21384book:[00:33:14] Why Greatness Cannot Be Planned: The Myth of the Objectivehttps://link.springer.com/book/10.1007/978-3-319-15524-1---LINKS:https://app.rescript.info/share/3a9dc6189cb539c6a05fcc4f75c101b3PDF:https://app.rescript.info/api/public/sessions/9eda60ede2b31c92/pdf

Frank Hutter, co-founder of Prior Labs, talks about TabPFN, a tabular foundation model that makes predictions in a single forward pass, and the research behind it. TabPFN is pre-trained on synthetic datasets drawn from a prior over structural causal models, rather than on real data. At prediction time it takes the whole training table as context and outputs an approximation of the Bayesian posterior predictive distribution, without per-dataset training or hyperparameter search. Frank explains how this grew out of his earlier work on AutoML and neural architecture search, how the priors are built and revised, and why tabular data was hard for deep learning for so long. The conversation also covers the TabArena benchmark, how the architecture changed from TabPFN v1 to v3, scaling to larger tables, using the model with coding agents, test-time compute, Google's TabFM, causal inference and interventions, and relational data. At the end, a short update Frank recorded after the interview covers the TabPFN-3.5 release. Prior Labs: TabPFN-3.5: https://priorlabs.ai/tabpfn-3-5 https://priorlabs.ai/careers TOC: 00:00 Introduction 00:44 Welcome and Frank's background 02:05 Why tabular data was hard for deep learning 10:17 Pre-training on synthetic data 12:52 The TabArena benchmark 19:28 From AutoML to neural architecture search 26:34 TabPFN as a learned algorithm 30:50 Bayesian prediction in one forward pass 39:37 Scaling to larger tables 47:48 Using TabPFN with coding agents 57:47 Output heads and architecture from v1 to v3 1:05:29 Test-time compute and adaptation 1:13:32 Google's TabFM 1:16:53 How the priors are designed 1:18:40 Correlation, causation and interventions 1:35:22 Relational and multimodal data 1:38:31 Use in organisations 1:46:38 The open research arm 1:50:21 Update: TabPFN-3.5 REFS: TabPFN v2, Nature (Hollmann et al., 2025) https://www.nature.com/articles/s41586-024-08328-6 Transformers Can Do Bayesian Inference (Müller et al.) https://arxiv.org/abs/2112.10510 TabArena (Erickson et al.) https://arxiv.org/abs/2506.16791 AutoGluon-Tabular (Erickson et al.) https://arxiv.org/abs/2003.06505 Beyond IID: How General Are Tabular Foundation Models, Really? https://arxiv.org/abs/2606.30410 Neural Architecture Search: A Survey (Elsken, Metzen & Hutter) https://arxiv.org/abs/1808.05377 Auto-WEKA (Thornton et al.) https://www.cs.ubc.ca/~hutter/papers/AutoWEKA-KDD2013.pdf TabPFN v1 (Hollmann et al., 2022) https://arxiv.org/abs/2207.01848 TabPFN-3 technical report https://arxiv.org/abs/2605.13986 TabPFN-2.5 report https://arxiv.org/abs/2511.08667 CAAFE (Hollmann et al.) https://arxiv.org/abs/2305.03403 TabICL (Qu et al.) https://arxiv.org/abs/2502.05564 TabICLv2 (Qu et al.) https://arxiv.org/abs/2602.11139 Google TabFM https://research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/ TALENT benchmark (Ye et al.) https://arxiv.org/abs/2407.00956 Do-PFN (Robertson et al.) https://arxiv.org/abs/2506.06039 CausalPFN (Balazadeh et al.) https://arxiv.org/abs/2506.07918 Causal Foundation Models with Partial Graphs (Reuter et al.) https://arxiv.org/abs/2602.14972 RelBench (Robinson et al.) https://arxiv.org/abs/2407.20060 RelArena-α, TabPFN-Rel and RPI https://arxiv.org/abs/2608.16319 TabPFN on GitHub https://github.com/PriorLabs/TabPFN TabPFN-3.5 technical report https://arxiv.org/abs/2609.17895 Otto Group Product Classification Challenge (Kaggle, 2015) https://www.kaggle.com/competitions/otto-group-product-classification-challenge ---RESCRIPT:https://app.rescript.info/share/e99676c25ee6189fbf54c9be07eb623e

Alexander Mattick is a researcher at Fraunhofer IIS and a PhD researcher at the University of Technology Nuremberg (UTN), and a regular on Yannic Kilcher's Discord. He first came on MLST in 2022, after helping research the Yann LeCun and Randall Balestriero episode on interpolation. SPONSOR: --- Cyber Fund built the Monastery to help founders ship products that were impossible a year ago. Applications for Batch 1 are now open. Apply now: https://cyber.fund --- Alexander treats inference as the thread running through modern machine learning: once you have a model, what does it cost to get an answer out of it? He works through Monte Carlo, GFlowNets, energy-based models, diffusion, normalising flows and flow matching, with four short explainers he recorded himself. He is blunt about energy-based models: you can sample from them in principle, but it is rarely worth the compute. JEPA and "world model", he says, are closer to branding than to technical categories. Next: theories of deep learning, none of which he thinks predicts enough yet to guide practice, then reinforcement learning. --- 0:00 Cold open: information is expensive 0:51 Welcome back, Alexander Mattic 2:08 Alexander's research background 2:50 Inference: densities, sampling and Monte Carlo 6:42 GFlowNets, energy functions and MCMC 9:45 Explainer: energy-based models 11:03 Why model a density at all? 17:30 From learned energies to flow matching 25:08 Explainers: diffusion and normalising flows 28:33 Are energy-based models generative? 33:22 JEPA, contrastive learning and collapse 41:13 Why non-language modalities need flows 44:51 Inference as search: branch and bound 49:43 Q-learning and delayed consequences 55:14 Flow matching, optimal transport, Fokker-Planck 1:00:03 Explainer: flow matching 1:01:49 AlphaFold, latents and scale versus architecture 1:07:52 Two families of deep learning theory 1:15:04 What a good theory would predict 1:23:53 The manifold hypothesis and compression 1:28:25 Is reward enough? 1:32:01 Control theory versus reinforcement learning 1:37:22 The Bitter Lesson and expensive information 1:42:08 Constrained RL: the constrained MDP toolbox 1:50:12 Creativity as constrained search 1:55:44 Reality is protean: when abstractions hold 2:00:32 What is a world model? 2:04:38 Prediction is not control 2:08:13 Robot demos, MPC and reliability --- REFERENCES: [6:55] GFlowNets (Bengio et al., 2021) https://arxiv.org/abs/2106.04399 [38:46] Contrastive Self-Supervised Learning (Anand, 2020) https://ankeshanand.com/blog/2020/01/26/contrative-self-supervised-learning.html [38:56] LeJEPA (Balestriero and LeCun, 2025) https://arxiv.org/abs/2511.08544v3 [47:10] RL for Node Selection in Branch-and-Bound (Mattick) https://openreview.net/forum?id=0ez68a5UqI [56:20] Flow Matching for Generative Modeling https://arxiv.org/abs/2210.02747v2 [1:12:41] Disentangling feature and lazy training in deep neural networks https://arxiv.org/abs/1906.08034v4 [1:31:05] Reward is enough (Silver) https://doi.org/10.1016/j.artint.2021.103535 [1:35:12] Learning ReLU networks to high uniform accuracy is intractable (Berner et al.) https://arxiv.org/abs/2205.13531v2 [1:40:20] Dota 2 with Large Scale Deep RL https://arxiv.org/abs/1912.06680v1 [1:45:41] Constrained Update Projection for Safe Policy Optimization (Yang et al., 2022) https://arxiv.org/abs/2209.07089 [1:46:11] SafeMPO (ICLR 2026) https://openreview.net/forum?id=1m0EU6QXj6 [1:50:17] Why Creativity Cannot Be Interpolated https://archive.mlst.ai/paper/why-creativity-cannot-be-interpolated/ [1:51:39] Invalid Action Masking (Huang and Ontañón) https://arxiv.org/abs/2006.14171 [2:00:04] Probability Theory: The Logic of Science (Jaynes, 2003) https://www.cambridge.org/core/books/probability-theory/9CA08E224FF30123304E6D8935CF1A99 [2:01:53] Training Agents Inside of Scalable World Models (Hafner et al., 2025) https://arxiv.org/abs/2509.24527v1 [2:03:43] World Models (Ha and Schmidhuber, 2018) https://arxiv.org/abs/1803.10122v4

The car making a left turn at the start of this episode was never filmed. Cosmos 3 generated it. Ming-Yu Liu, who leads the Cosmos research at NVIDIA, explains how one model can describe a video, generate one, and produce robot actions. He walks Tim through the architecture. A vision language model reasons one token at a time; its weights then initialise a bidirectional diffusion generator for video, audio and action, and a shared temporal position scheme lines up signals that run at different rates. Ming-Yu treats "world model" as a set of tools, not one definition: forward dynamics, inverse dynamics and policy, trained together under a capacity limit so that each helps the others. He also explains why plentiful first-person human video carries over to robots, which have far less data of their own, and why a Cosmos model post-trained on the DROID dataset is a good starting point for pick-and-place policies. The most practical thread is testing. A neural simulator does not need accurate success rates. It only needs to rank policy A above policy B the way the real world would, so a team can narrow down which checkpoints deserve a real trial. Cosmos Dreams applies that closed-loop idea to driving and robotics, and Ming-Yu argues that humanoids around children and pets make safety matter even more than it does for cars. The conversation ends on the Super, Nano and Edge sizes (Edge targets Jetson Thor, Orin and DGX Spark) and where to find the open weights, code and data. This episode is a paid partnership with NVIDIA. Learn more about Cosmos: https://nvda.ws/4cJoY1S Explore Cosmos Lab: https://research.nvidia.com/labs/cosmos-lab/cosmos3/ --- TIMESTAMPS: 00:00:00 A road that was never filmed 00:02:28 Inside Cosmos 3: reasoning and generator towers 00:05:02 World models: dynamics, policy and one clock 00:08:59 Learning robot skills from human video 00:11:06 Ambiguous tasks and system 2 planning 00:12:53 Neural simulators for policy verification 00:16:41 Cosmos as a starting point for robot policies 00:19:00 Cosmos Dreams and robot safety 00:22:04 Super, Nano and Edge model sizes 00:24:24 Open models, the Cosmos repo and feedback --- REFERENCES: tool: [00:00:13] Cosmos 3 (NVIDIA Cosmos Lab project page) https://research.nvidia.com/labs/cosmos-lab/cosmos3/ [00:18:27] NVIDIA Cosmos GitHub repository https://github.com/NVIDIA/cosmos [00:22:05] Cosmos3-Edge model card https://huggingface.co/nvidia/Cosmos3-Edge [00:22:15] Cosmos3-Super model card https://huggingface.co/nvidia/Cosmos3-Super [00:22:16] Cosmos3-Nano model card https://huggingface.co/nvidia/Cosmos3-Nano [00:22:50] NVIDIA Jetson Thor https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-thor/ [00:22:52] NVIDIA Jetson Orin https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/ [00:22:53] NVIDIA DGX Spark https://www.nvidia.com/en-us/products/workstations/dgx-spark/ [00:24:42] Cosmos 3 collection on Hugging Face https://huggingface.co/collections/nvidia/cosmos3 other: [00:01:07] Cosmos-Dreams closed-loop simulators (NVIDIA SIGGRAPH 2026 blog) https://blogs.nvidia.com/blog/siggraph-news-2026/ paper: [00:08:54] Cosmos 3: Omnimodal World Models for Physical AI https://arxiv.org/abs/2606.02800 [00:17:43] DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset https://arxiv.org/abs/2403.12945 --- RESCRIPT: https://app.rescript.info/share/e2385948cf465f0d6a2c0930150fc3ab

Pavan Kumar Reddy leads audio research at Mistral AI. He joins Tim Scarfe for a deep technical tour of Voxtral — and explains why the frontier of deployed voice is still a cascade of specialised models rather than one end-to-end system. IN PARTNERSHIP WITH MISTRAL AI: --- This episode was produced in partnership with Mistral AI. Mistral AI: https://mistral.ai/ --- The conversation opens on architecture. Voxtral Chat feeds a 3B Ministral text trunk with continuous embeddings from an audio encoder, passed to the decoder as direct token input rather than through cross-attention as in Whisper, so the model can answer questions about emotion, timing and who spoke when without an intermediate transcript to lose them. The real-time model becomes a dual-stream decoder that consumes audio and emits text at once, at a target delay down to 160ms, with slower streams in parallel for anything that can wait for more context. On generation, Pavan explains why Voxtral TTS predicts continuous latents rather than discrete codec tokens, traces the lineage from SoundStream through EnCodec to Mimi's split of semantic and acoustic codebooks, and places FSQ and flow matching in it. Tim presses on the priors underneath: why a mel spectrogram instead of raw waveform, what noise augmentation buys, and when acoustic overfitting becomes somebody's fine-tuning problem. Then the failure modes. Diarisation is emitted autoregressively inside the transcript rather than by a separate head, which makes streaming diarisation fragile — less context, late speaker changes, invented extra speakers. And because the architecture commits to what it has already predicted, one out-of-distribution mistake compounds into looping or skipped segments, which is what DPO corrects: the negative supervision pre-training and SFT cannot give. The last third is the argument Tim keeps returning to. Customers running voice agents over millions of sessions describe scaffolding, not a solved problem, with a sharp drop outside the top few languages. Cascades survive because each component stays separately adaptable, observable and constrainable. And voice alone is cognitive debt: absorbing information and deciding in one serial stream is harder than glancing at a menu. Voice becomes ubiquitous beside a screen, not instead of one. --- TIMESTAMPS: 00:00:00 Cold open 00:00:46 Why Mistral moved into audio 00:09:27 Inside Voxtral: trunk, encoder, dual streams 00:20:22 Speech that works in real time 00:30:52 How a voice becomes tokens 00:39:59 Flow matching, FSQ and the new codec 00:52:51 When speech models lose the speaker 01:03:23 Correcting hallucinations with preferences 01:12:12 Controlling synthetic speech 01:20:06 Why cascades still win 01:29:25 Speech in the wild 01:33:46 Audio models as interfaces 01:37:54 Why voice still needs a screen --- REFERENCES: paper: [00:01:42] Mistral 7B https://arxiv.org/abs/2310.06825 [00:09:38] Voxtral https://arxiv.org/abs/2507.13264 [00:14:41] Whisper: Robust Speech Recognition https://arxiv.org/abs/2212.04356 [00:19:11] Voxtral Realtime https://arxiv.org/abs/2602.11298 [00:21:52] Delayed Streams Modeling (Kyutai) https://arxiv.org/abs/2509.08753 [00:30:52] Voxtral TTS https://arxiv.org/abs/2603.25551 [00:32:38] SoundStream neural audio codec https://arxiv.org/abs/2107.03312 [00:34:59] Flow Matching for Generative Modeling https://arxiv.org/abs/2210.02747 [00:37:03] EnCodec: High Fidelity Neural Audio Compression https://arxiv.org/abs/2210.13438 [00:37:42] Moshi and the Mimi codec https://arxiv.org/abs/2410.00037 [00:39:05] Finite Scalar Quantization (FSQ) https://arxiv.org/abs/2309.15505 [01:03:33] Direct Preference Optimization (DPO) https://arxiv.org/abs/2305.18290 dataset: [00:46:14] Mozilla Common Voice https://commonvoice.mozilla.org/en/datasets organization: [00:50:47] Hugging Face https://huggingface.co/

Can a machine learn the judgement that separates a plausible-looking result from a faithful experiment? Edward Hughes, Chief Scientist and co-founder of Inherent, joins Tim Scarfe to argue that creativity is not optimisation, and that the missing capability in AI is choosing which questions are worth asking. SPONSOR: --- Cyber Fund built the Monastery to help founders ship products that were impossible a year ago. Apply now: https://cyber.fund --- Edward makes the case that Move 37 was innovative rather than creative, and that the field, not the individual, decides what counts as a discovery. That reframing runs through Csikszentmihalyi, Deutsch and exaptation into open-endedness, where deceptive goals and imperfect world models turn out to be the point rather than the problem. The second half turns to the paper: Replica, a task space built by redacting figures from real papers, and Faraday, a 27-billion-parameter model trained to steer a frontier coding agent that then beats the frontier on held-out replications. --- TIMESTAMPS: 00:00:00 Cold open: Move 37, Faraday and collective intelligence 00:01:08 Sponsor: CyberFund 00:01:46 Inherent's $50M raise and the road from string theory 00:09:14 Three timescales of learning: weights, context, culture 00:13:47 Move 37 was innovative, not creative: the field decides 00:20:39 Creativity as satisficing: the urinal and evolution 00:25:06 Exaptation and the Tristan chord: creativity in context 00:30:56 Coherence for whom? Deutsch's hard-to-vary explanations 00:35:53 Why copying is creative: Deutsch and the constraint engineer 00:42:27 Societies of agents and the strong Moravec paradox 00:45:51 Evaluate in hindsight: from Lean proofs to climate change 00:51:56 Picbreeder, local goals and why discovery needs deception 00:57:21 Spaghetti proofs, translation layers and superhuman Go 01:00:37 Does nature compress? Naturalness and real patterns 01:07:36 Why replicate? Replica's redacted figures and Faraday 01:12:31 Faraday beats Codex, Claude and GLM 5.2 on held-out tasks 01:15:31 Replication to innovation: how the Transformer happened 01:18:26 Deep replication: what Faraday learns from Voyager and GNoME 01:23:37 Can the AI scientist cheat? Goodharting the judge 01:29:09 Inside Replica: scale-down, 8xB300 runs, per-task rubrics 01:34:11 The RL crisis: getting GRPO to work with per-turn credit 01:39:43 Weights vs harnesses: AlphaEvolve, DGM and EvoTune 01:45:45 The recursive company: agents cross a phase transition 01:50:35 Collective intelligence and the electric dynamo 01:55:46 What replaces OKRs? Incumbents and the burden of knowledge --- REFERENCES: MLST Creativity Article: https://archive.mlst.ai/read/why-creativity-cannot-be-interpolated organization: [00:01:47] Inherent https://inherentlabs.ai/ other: [00:20:51] Marcel Duchamp, Fountain https://www.tate.org.uk/art/artworks/duchamp-fountain-t07573 [00:05:19] Human-Timescale Adaptation in an Open-Ended Task Space (Adaptive Agent) https://arxiv.org/abs/2301.07608 [00:06:05] The AI Scientist https://arxiv.org/abs/2408.06292 [00:12:13] Training AI Scientists to Replicate Research (Replica and Faraday) https://arxiv.org/abs/2608.13331 [01:44:46] Evolutionary Principles in Self-Referential Learning https://people.idsia.ch/~juergen/diploma.html [01:59:33] Are Ideas Getting Harder to Find? https://www.nber.org/papers/w23782 book: [00:16:04] Creativity: Flow https://search.worldcat.org/title/254487436 [00:26:22] Why Greatness Cannot Be Planned https://link.springer.com/book/10.1007/978-3-319-15524-1 [00:33:03] The Beginning of Infinity https://www.penguinrandomhouse.com/books/293575/the-beginning-of-infinity-by-david-deutsch/ [01:55:47] Laws of Knowledge https://www.penguin.co.nz/books/the-infinite-alphabet-9780241655672 (Full list refs on YT/rescript) --- RESCRIPT: https://app.rescript.info/session/670296ba913761d0?share=6281911cac9bdbff637f10819d4d1e5c

Could slowing AI development make superintelligence safer? Daniel Kokotajlo and Thomas Larsen of the AI Futures Project join Tim Scarfe to examine AI 2040: Plan A, a proposal to buy time before AI exceeds human control. SPONSOR: --- Cyber Fund built the Monastery to help founders ship products that were impossible a year ago. Apply now: https://cyber.fund --- After revisiting AI 2027 and the limits of forecasting, they ask what happens when AI can automate research and sustain an economy without human workers. Tim challenges the case for general models and asks whether intelligence alone explains power. Plan A proposes an initial pause to build safety infrastructure, then cautious development up to the strongest AI that can still be reliably controlled. The discussion tests the distinction between control and alignment, the case for public AI research, and whether the US and China could enforce a slowdown. It ends with the evidence that would change their forecasts. --- TIMESTAMPS: 00:00:00 AI 2040: a slower route to superintelligence 00:01:34 Sponsor: Cyber Fund 00:02:12 From OpenAI to AI 2027 00:06:58 Forecasts, war games and self-fulfilling prophecies 00:17:44 Why AI sceptics are changing their minds 00:23:04 When AI can replace its own researchers 00:28:45 Could an AI economy grow without human workers? 00:37:32 One general model or a society of specialists? 00:47:43 Brains, machines and collective intelligence 00:56:12 Plan A: buy time at the controllable frontier 01:00:02 Why control buys time but cannot replace alignment 01:06:36 Why AI research should be public 01:10:32 Can the US and China enforce an AI slowdown? 01:19:04 Why AI policy debates miss the technology 01:21:56 Is AI normal technology? The remaining disagreement Many thanks to James Wilken-Smith for helping with show research. --- REFERENCES: other: [00:00:01] AI 2040: Plan A https://ai-2040.com/ [00:03:27] AI 2027 https://ai-2027.com/ [00:13:47] Scenario Scrutiny for AI Policy https://blog.aifutures.org/p/scenario-scrutiny-for-ai-policy [00:33:11] The 2028 Global Intelligence Crisis https://www.citriniresearch.com/p/2028gic [01:00:40] Brief independent investigation of agents' behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident https://www.redwoodresearch.org/research/hugging-face-incident [01:09:21] The Hugging Face incident and the road ahead https://openai.com/index/hugging-face-incident-and-the-road-ahead/ [01:22:01] AI as Normal Technology https://www.normaltech.ai/p/ai-as-normal-technology [01:22:51] Common Ground between AI 2027 & AI as Normal Technology https://asteriskmag.substack.com/p/common-ground-between-ai-2027-and person: [00:19:43] Geoffrey Hinton https://www.cs.toronto.edu/~hinton/ [00:20:07] Ryan Greenblatt https://www.lesswrong.com/users/ryan_greenblatt [00:26:06] Elon Musk https://www.tesla.com/elon-musk tool: [00:21:46] ARC-AGI-3 https://arcprize.org/arc-agi/3 [00:21:53] AlphaGo and Move 37 https://deepmind.google/research/alphago/ [00:39:41] Claude https://claude.com/product/overview [00:39:58] NVIDIA H100 GPU https://www.nvidia.com/en-us/data-center/h100/ paper: [00:24:42] Training AI Scientists to Replicate Research https://arxiv.org/abs/2608.13331v1 [01:27:19] Validity of the single processor approach to achieving large scale computing capabilities https://www.cs.cmu.edu/~18742/papers/Amdahl1967.pdf book: [00:28:52] B******t Jobs: A Theory https://www.simonandschuster.com/books/B******t-Jobs/David-Graeber/9781501143335 organization: [01:05:09] Redwood Research https://www.redwoodresearch.org/ --- RESCRIPT: https://app.rescript.info/public/share/33d1a58fa8f307ae7dfd504d4fdaa9d5

Tom McGrath is co-founder and Chief Scientist at Goodfire, and a former Google DeepMind researcher. He joins Tim Scarfe to ask what neural networks actually learn, whether their internal representations converge on structures in the world, and whether interpretability can extract new scientific knowledge rather than merely explain model outputs. Beginning with AlphaZero and learned modularity, the conversation moves into neural geometry: concept manifolds, reusable computation inside Llama, and why activation steering can fail when it pushes a model off-manifold. McGrath then makes the case for intentional design, using interpretability as part of the training loop. They examine controlled generalisation, features as rewards, predictive data debugging, and the uncomfortable fact that a model may recognise a hallucination or reward hack and still produce it. The discussion closes on grader awareness, oversight and collusion between adaptive agents, then returns to sparse autoencoders. SAEs are useful, McGrath argues, but they may fracture the higher-dimensional structures networks actually use. This episode was made with support from Goodfire. --- TIMESTAMPS: 00:00:00 Introduction: Can interpretability speed-run science? 00:02:03 The invisible grader 00:06:51 What AlphaZero learned from the world 00:12:24 Interpretability as a control loop 00:21:54 The forbidden method and safer interventions 00:37:36 Why models catch hallucinations too late 00:46:19 Debug the dataset before training 00:50:44 Why neural networks become modular 00:55:57 Finding the geometry inside a network 01:02:55 Why steering falls off the manifold 01:12:10 A reusable calculator inside Llama 01:17:19 From abstractions to goals 01:25:28 Reward hacking, oversight and collusion 01:37:23 Are sparse autoencoders dead? --- REFERENCES: paper: [00:05:45] Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs https://arxiv.org/abs/2502.17424v7 [00:11:05] Acquisition of Chess Knowledge in AlphaZero https://arxiv.org/abs/2111.09259 [00:25:30] Steering Out-of-Distribution Generalization with Concept Ablation Fine-Tuning https://arxiv.org/abs/2507.16795 [00:29:30] Persona Vectors: Monitoring and Controlling Character Traits in Language Models https://arxiv.org/abs/2507.21509 [00:41:14] Features as Rewards: Scalable Supervision for Open-Ended Tasks via Interpretability https://arxiv.org/abs/2602.10067 [00:47:03] Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signal https://arxiv.org/abs/2606.12360 [01:00:26] Do Sparse Autoencoders Capture Concept Manifolds? https://arxiv.org/abs/2604.28119 [01:03:04] Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior https://arxiv.org/abs/2605.05115 [01:14:20] Arithmetic in the Wild: Llama uses Base-10 Addition to Reason About Cyclic Concepts https://arxiv.org/abs/2605.01148 [01:29:35] Measuring Reward-Seeking via Contrastive Belief Updates https://arxiv.org/abs/2607.18966v1 other: [00:15:44] Intentional Design https://www.goodfire.com/blog/intentional-design [00:56:12] The World Inside Neural Networks https://www.goodfire.com/research/the-world-inside-neural-networks [01:37:28] A Pragmatic Vision for Interpretability https://www.alignmentforum.org/posts/StENzDcD3kpfGJssR/a-pragmatic-vision-for-interpretability --- RESCRIPT: https://app.rescript.info/share/846cfee4131b664fd09209cc3b98018e

Tim Scarfe speaks with Ilia Shumailov and Alexander Panfilov about their paper, Stealing Reasoning Traces from Proprietary LLM APIs.The core bug sounds deceptively simple: providers return encrypted reasoning state so conversations can be resumed or forked. But those blobs can be replayed across users and sibling models. A smaller model can ask the provider to decrypt the trace, then repeat the hidden reasoning in plain text. The discussion covers leaked private data, a broadly reusable jailbreak, poisoned agent traces, chain-of-thought monitoring, responsible disclosure, and possible defenses.Ilia Shumailov is an AI and security researcher, formerly at Google DeepMind, who completed his Cambridge PhD under Ross Anderson. Alexander Panfilov is a PhD researcher at the ELLIS Institute Tübingen and the Max Planck Institute for Intelligent Systems, working on AI safety, adversarial machine learning, and LLM red-teaming. They close by separating the demonstrated jailbreaking threat from ordinary benign distillation, and by arguing for controlled experiments over sweeping claims.---TIMESTAMPS:00:00:00 Intro montage00:01:33 Portable encrypted thought and decoded reasoning00:24:55 How the attack works and what it means00:39:04 Doom, defense, and scientific restraint---REFERENCES:paper:[00:00:00] Stealing Reasoning Traces from Proprietary LLM APIshttps://arxiv.org/abs/2608.09867[00:09:22] Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safetyhttps://arxiv.org/abs/2507.11473[00:11:30] Reasoning Models Don’t Always Say What They Thinkhttps://www.anthropic.com/research/reasoning-models-dont-say-think[00:37:22] PostTrainBench: Can LLM Agents Automate LLM Post-Training?https://arxiv.org/abs/2603.08640[00:41:02] Large-scale online deanonymization with LLMshttps://arxiv.org/abs/2602.16800other:[00:09:28] OpenAI and Hugging Face partner to address security incident during model evaluationhttps://openai.com/index/hugging-face-model-evaluation-security-incident/[00:10:22] Claude, GPT, and Gemini All Struggle to Evade Monitorshttps://metr.org/notes/2025-08-22-claude-gpt-gemini-struggle-evade-monitors/tool:[00:42:08] Isabelle proof assistanthttps://isabelle.in.tum.de/---RESCRIPT: https://app.rescript.info/share/07fc38276e0823dc9b8986c32e202c7f

Astrophysicist Adam Becker, author of "What Is Real?", joins Tim Scarfe to take apart the futures Silicon Valley keeps selling: the 2045 singularity, mind uploading, Mars colonies, and the AI apocalypse. His new book *More Everything Forever* argues these ideas are hugely influential, mostly evidence-free, and bankrolled by tech billionaires who need a story in which growth never ends.Becker does the physics the boosters skip. Kurzweil's "law of accelerating returns" rests on cherry-picked data, and every exponential ends. Grant Bezos his perpetual energy growth and humanity boils the oceans within a few centuries, then exhausts the observable universe in under 4,000 years. The stars are too far away, Mars dirt is poison, and the day the dinosaur-killing asteroid hit Earth was still nicer than any day on Mars. On AI, Becker calls LLMs pocket calculators for language: hallucination is the model doing exactly what it always does, and the intelligence explosion assumes intelligence is a single number you can buy with compute.The sting is that Becker thinks the doomers are sincere. Yudkowsky, Bostrom and the effective altruists are not grifters, he says, just wrong, and their warnings that AI could end the world feed the same growth story the money depends on. He closes with his own prescription: take social problems seriously, regulate the whole tech industry, and tax billionaires out of existence.---TIMESTAMPS:00:00:00 Cold open and the thesis of More Everything Forever00:04:24 Kurzweil's singularity and the physical limits of exponential growth00:14:02 High agency and the fantasy of imprinting humanity on the cosmos00:16:55 Mind uploading, functionalism, and embodied cognition00:24:24 AI psychosis and anthropomorphizing LLMs00:26:24 Calculators, hallucination, and the limits of scale00:32:20 Yudkowsky and the intelligence-explosion argument00:40:37 True believers, venture capital, and the sci-fi growth narrative00:47:21 From Extropians to EA: utilitarianism and longtermism00:53:50 Brain worms and Becker's prescription: take social science seriously00:56:49 Why the AI-ethics discourse is broken01:01:42 The eugenics and IQ argument against 'intelligence'01:06:07 Why space settlement fails: Mars, the moon, and orbital data centers01:10:42 Billionaire myths and the search for purpose01:13:38 Tax billionaires, regulate tech: closing prescriptions---REFERENCES:book:[00:00:07] More Everything Forever (Adam Becker, 2025)https://www.hachettebookgroup.com/titles/adam-becker/more-everything-forever/9781541619593/[00:00:15] What Is Real? (Adam Becker, 2018)https://en.wikipedia.org/wiki/What_Is_Real%3F[00:15:46] What We Owe the Future (Will MacAskill, 2022)https://www.hachettebookgroup.com/titles/william-macaskill/what-we-owe-the-future/9781541618626/other:[00:00:27] Dreaming Against the Machine (podcast)https://www.dreamingagainstthemachine.com[00:01:04] The Useful Idiots of AI Doomsaying (Adam Becker, The Atlantic, 2025)https://www.theatlantic.com/books/archive/2025/09/what-ais-doomers-and-utopians-have-in-common/684270/ RESCRIPT: https://app.rescript.info/share/d6e37f9866673d8f74a39076efa5926b

This episode is sponsored by Notion. Learn more about Notion's Developer Platform today at https://notion.com/mlstWhy can deep networks discover abstractions that shallow models miss? Statistical physicist Matthieu Wyart joins Tim Scarfe to argue that the answer lies in the hidden hierarchy of data. Language and images are built from parts within parts; depth lets a network recover those coarse-grained variables and escape the curse of dimensionality.The conversation moves from jamming transitions and rough loss surfaces to Chomsky, context-free grammars and machine creativity. Wyart explains why next-token prediction can still recover compositional structure, where current systems fall short of genuine scientific invention, and why predicting latent representations rather than raw tokens could make learning far more sample-efficient.They also examine diffusion models, neural scaling laws and the limits of physics-inspired theory. The final question is on a personal note: if mistakes are the price of leaving the beaten path, how much scientific risk is worth taking?---TIMESTAMPS:00:00:00 Can machines learn abstractions from data?00:02:00 Notion agentic workspace00:02:49 From statistical physics to machine learning00:06:40 What physics can explain about learning00:16:37 From Carnot to Chomsky bulldozer00:21:21 How deep networks recover hidden hierarchies00:32:43 Where machine creativity still falls short00:40:48 How deep nets escape the curse of dimensionality00:52:19 Why predict latents instead of tokens01:02:49 The sample-efficiency case for latent prediction01:08:31 Diffusion, scaling laws and text entropy01:16:40 The scientists we learn from and the mistakes we make---REFERENCES:person:[00:00:43] Noam Chomskyhttps://linguistics.mit.edu/user/chomsky/tool:[00:02:08] Notion Developer Platformhttps://www.notion.com/en-gb/blog/introducing-developer-platformpaper:[00:04:43] Mastering the game of Go with deep neural networks and tree searchhttps://www.nature.com/articles/nature16961[00:05:52] Reconciling modern machine-learning practice and the bias-variance trade-offhttps://arxiv.org/abs/1812.11118[00:25:54] How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Modelhttps://arxiv.org/abs/2307.02129[00:42:12] Efficient Estimation of Word Representations in Vector Spacehttps://arxiv.org/abs/1301.3781[00:52:46] Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecturehttps://arxiv.org/abs/2301.08243[00:52:54] Learn from your own latents and not from tokens: A sample-complexity theoryhttps://arxiv.org/abs/2605.27734[01:08:31] A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Datahttps://arxiv.org/abs/2402.16991[01:11:39] Scaling Laws for Neural Language Modelshttps://arxiv.org/abs/2001.08361[01:12:17] Deriving Neural Scaling Laws from the statistics of natural languagehttps://arxiv.org/abs/2602.07488[01:13:34] Prediction and Entropy of Printed Englishhttps://ieeexplore.ieee.org/document/6773263---LINKS:Download PDF transcript: https://app.rescript.info/share/f7644cdaa86c5cc1e41e484e290f2bd4

Can an AI do the right thing for the wrong reason? Tim Scarfe speaks with Apollo Research’s Alexander Meinke, Axel Højmark and Jérémy Scheurer about Measuring Reward-Seeking via Contrastive Belief Updates, their new research with OpenAI. The panel asks how models infer what graders reward, why good behaviour can come from the wrong reason, and whether that difference can be measured. The conversation moves through promise-breaking, grader awareness, reward hacking, scheming, opaque reasoning and corrigibility, then turns to a detailed walkthrough of the contrastive-belief method and what its results do and do not show. The o3 results discussed here concern an intermediate checkpoint without safety training. This episode was made in partnership with Apollo Research. MLST retained full editorial control. Reference Apollo Research: https://www.apolloresearch.ai/ --- TIMESTAMPS: 00:00:00 Cold Open 00:02:12 Right Things, Wrong Reasons 00:12:47 Grader Awareness 00:26:22 Legibility 00:32:35 What To Call It 00:35:58 Intelligence, Agency, Anthropomorphism 00:45:16 Apollo’s Mission 00:48:54 The End of the Exponential 00:55:45 The Paper 01:16:34 Closing Reflection --- REFERENCES: tool: [00:00:08] Claude Fable https://www.anthropic.com/claude/fable [00:12:50] AlphaGo Zero https://deepmind.google/blog/alphago-zero-starting-from-scratch/ [00:44:30] AlphaFold 3 https://deepmind.google/science/alphafold/ paper: [00:01:02] Measuring Reward-Seeking via Contrastive Belief Updates https://arxiv.org/abs/2607.18966 [00:16:19] Natural Language Autoencoders Produce Unsupervised Explanations of LLM Activations https://transformer-circuits.pub/2026/nla/ [00:26:48] Stress Testing Deliberative Alignment for Anti-Scheming Training https://arxiv.org/abs/2509.15541 [00:35:33] Shortcut learning in deep neural networks https://arxiv.org/abs/2004.07780 [00:53:49] Measuring AI Ability to Complete Long Software Tasks https://arxiv.org/abs/2503.14499 [00:59:52] Modifying LLM Beliefs with Synthetic Document Finetuning https://alignment.anthropic.com/2025/modifying-beliefs-via-sdf/ [01:10:44] Alignment Faking in Large Language Models https://arxiv.org/abs/2412.14093 [01:13:55] Natural Emergent Misalignment from Reward Hacking https://www.anthropic.com/research/emergent-misalignment-reward-hacking other: [00:10:14] We Need a Science of Scheming https://www.apolloresearch.ai/science/science-of-scheming/ [00:32:56] CoastRunners reward hacking example https://deepmind.google/blog/specification-gaming-the-flip-side-of-ai-ingenuity/ organization: [01:06:07] Redwood Research https://www.redwoodresearch.org/ --- ReScript: https://app.rescript.info/share/718ab68e18cfa3b9b800da6b3290fd42