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] 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

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

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RESCRIPT:

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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/

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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