Machine Learning Street Talk (MLST)

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:

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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

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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] Bullshit Jobs: A Theory

https://www.simonandschuster.com/books/Bullshit-Jobs/David-Graeber/9781501143335

organization:

[01:05:09] Redwood Research

https://www.redwoodresearch.org/

---

RESCRIPT:

https://app.rescript.info/public/share/33d1a58fa8f307ae7dfd504d4fdaa9d5