# Clement Bonnet - Can Latent Program Networks Solve Abstract Reasoning?

Page: https://stenobird.com/podcast/machine-learning-street-talk/clement-bonnet-can-latent-program-networks-solve-abstract-reasoning
Text version: https://stenobird.com/podcast/machine-learning-street-talk/clement-bonnet-can-latent-program-networks-solve-abstract-reasoning.md
Podcast: [Machine Learning Street Talk (MLST)](https://stenobird.com/podcast/machine-learning-street-talk)
Published: 2025-02-19T22:05:30+00:00
Episode link: https://podcasters.spotify.com/pod/show/machinelearningstreettalk/episodes/Clement-Bonnet---Can-Latent-Program-Networks-Solve-Abstract-Reasoning-e2v41og
Audio file: https://anchor.fm/s/1e4a0eac/podcast/play/98747600/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2025-1-19%2F65f76eb2-6635-fdd1-17ed-23a2f70eb94a.mp3
Processing state: processed
JSON: https://stenobird.com/v1/public/podcasts/machine-learning-street-talk/episodes/clement-bonnet-can-latent-program-networks-solve-abstract-reasoning
Duration seconds: 3086

## Resource

Clement Bonnet discusses his novel approach to the ARC (Abstraction and Reasoning Corpus) challenge. Unlike approaches that rely on fine-tuning LLMs or generating samples at inference time, Clement's method encodes input-output pairs into a latent space, optimizes this representation with a search algorithm, and decodes outputs for new inputs. This end-to-end architecture uses a VAE loss, including reconstruction and prior losses. SPONSOR MESSAGES: *** CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. Check out their super fast DeepSeek R1 hosting! https://centml.ai/pricing/ Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** TRANSCRIPT + RESEARCH OVERVIEW: https://www.dropbox.com/scl/fi/j7m0gaz1126y594gswtma/CLEMMLST.pdf?rlkey=y5qvwq2er5nchbcibm07rcfpq&dl=0 Clem and Matthew- https://www.linkedin.com/in/clement-bonnet16/ https://github.com/clement-bonnet https://mvmacfarlane.github.io/ TOC 1. LPN Fundamentals [00:00:00] 1.1 Introduction to ARC Benchmark and LPN Overview [00:05:05] 1.2 Neural Networks' Challenges with ARC and Program Synthesis [00:06:55] 1.3 Induction vs Transduction in Machine Learning 2. LPN Architecture and Latent Space [00:11:50] 2.1 LPN Architecture and Latent Space Implementation [00:16:25] 2.2 LPN Latent Space Encoding and VAE Architecture [00:20:25] 2.3 Gradient-Based Search Training Strategy [00:23:39] 2.4 LPN Model Architecture and Implementation Details 3. Implementation and Scaling [00:27:34] 3.1 Training Data Generation and re-ARC Framework [00:31:28] 3.2 Limitati…

## Actions

- request_transcript: `POST https://stenobird.com/v1/public/podcasts/machine-learning-street-talk/episodes/clement-bonnet-can-latent-program-networks-solve-abstract-reasoning/transcription-requests` — Idempotently request low-priority transcript generation for this episode.
- read_markdown: `GET https://stenobird.com/podcast/machine-learning-street-talk/clement-bonnet-can-latent-program-networks-solve-abstract-reasoning.md` — Read the agent-friendly Markdown representation of this episode resource.

A page view does not enqueue transcription. Agents should invoke `request_transcript` explicitly when they need this episode processed.

## Transcript

Full transcripts are not published on public pages unless there is a clear rights basis.