This episode explores Generative Recursive Reasoning, a paper that asks whether models can reason more effectively by repeatedly refining an internal latent state instead of externalizing long chains of thought as tokens. It explains how recursive reasoning trades parameter growth for inference-time computation, and how this approach may be especially useful for tasks like Sudoku, ARC-style problems, graph coloring, and N-Queens that benefit from iterative constraint solving. A central focus is the paper’s argument that reasoning should be stochastic rather than locked into a single deterministic path, using variational methods to model multiple possible latent trajectories and improve coverage when problems have more than one valid answer. The discussion is especially interesting because it contrasts this elegant search-like mechanism with mainstream transformer practice, highlighting both the promise of branching internal hypotheses and the practical reasons industry has not adopted such architectures at scale.

Sources:

1. Generative Recursive Reasoning in Latent Space

https://arxiv.org/pdf/2605.19376v1

2. Universal Transformers — Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Lukasz Kaiser, 2018

https://scholar.google.com/scholar?q=Universal+Transformers

3. Looped Transformers are Better at Learning Learning Algorithms — Liu Yang, Kangwook Lee, Robert Nowak, Dimitris Papailiopoulos, 2024

https://scholar.google.com/scholar?q=Looped+Transformers+are+Better+at+Learning+Learning+Algorithms

4. Hierarchical Reasoning Model — Guan Wang, Jin Li, Yuhao Sun, Xing Chen, Changling Liu, Yue Wu, Meng Lu, Sen Song, Yasin Abbasi Yadkori, 2025

https://scholar.google.com/scholar?q=Hierarchical+Reasoning+Model

5. Less is More: Recursive Reasoning with Tiny Networks — Alexia Jolicoeur-Martineau, 2025

https://scholar.google.com/scholar?q=Less+is+More%3A+Recursive+Reasoning+with+Tiny+Networks

6. Probabilistic Tiny Recursive Model — Amin Sghaier, Ali Parviz, Alexia Jolicoeur-Martineau, 2026

https://scholar.google.com/scholar?q=Probabilistic+Tiny+Recursive+Model

7. Auto-Encoding Variational Bayes — Diederik P. Kingma, Max Welling, 2013/2014

https://scholar.google.com/scholar?q=Auto-Encoding+Variational+Bayes

8. Stochastic Backpropagation and Approximate Inference in Deep Generative Models — Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra, 2014

https://scholar.google.com/scholar?q=Stochastic+Backpropagation+and+Approximate+Inference+in+Deep+Generative+Models

9. Inference Suboptimality in Variational Autoencoders — Chris Cremer, Xuechen Li, David Duvenaud, 2018

https://scholar.google.com/scholar?q=Inference+Suboptimality+in+Variational+Autoencoders

10. Iterative Amortized Inference — Joseph Marino, Yisong Yue, Stephan Mandt, 2018

https://scholar.google.com/scholar?q=Iterative+Amortized+Inference

11. Amortized Variational Inference: A Systematic Review — Ankush Ganguly, Sanjana Jain, Ukrit Watchareeruetai, 2023

https://scholar.google.com/scholar?q=Amortized+Variational+Inference%3A+A+Systematic+Review

12. Reasoning with Latent Thoughts: On the Power of Looped Transformers — Nikunj Saunshi, Nishanth Dikkala, Zhiyuan Li, Sanjiv Kumar, Sashank J. Reddi, 2025

https://scholar.google.com/scholar?q=Reasoning+with+Latent+Thoughts%3A+On+the+Power+of+Looped+Transformers

13. Structured Denoising Diffusion Models in Discrete State-Spaces — Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, Rianne van den Berg, 2021

https://scholar.google.com/scholar?q=Structured+Denoising+Diffusion+Models+in+Discrete+State-Spaces

14. One Step Forward and K Steps Back: Better Reasoning with Denoising Recursion Models — Chris Cameron, Wangzheng Wang, Nikita Ivanov, Ashmita Bhattacharyya, Didier Chetelat, Yingxue Zhang, 2026

https://scholar.google.com/scholar?q=One+Step+Forward+and+K+Steps+Back%3A+Better+Reasoning+with+Denoising+Recursion+Models

15. LASAR: Latent Adaptive Semantic Aligned Reasoning for Generative Recommendation — approx. recent recommendation/LLM reasoning authors, 2025

https://scholar.google.com/scholar?q=LASAR%3A+Latent+Adaptive+Semantic+Aligned+Reasoning+for+Generative+Recommendation

16. Towards Inference-time Scaling for Continuous Space Reasoning — approx. recent latent reasoning authors, 2025

https://scholar.google.com/scholar?q=Towards+Inference-time+Scaling+for+Continuous+Space+Reasoning

17. GTS: Inference-Time Scaling of Latent Reasoning with a Learnable Gaussian Thought Sampler — approx. recent latent reasoning authors, 2025

https://scholar.google.com/scholar?q=GTS%3A+Inference-Time+Scaling+of+Latent+Reasoning+with+a+Learnable+Gaussian+Thought+Sampler

18. Latent Chain-of-Thought for Visual Reasoning — approx. recent visual reasoning authors, 2025

https://scholar.google.com/scholar?q=Latent+Chain-of-Thought+for+Visual+Reasoning

19. AI Post Transformers: TMAS: Scaling Test-Time Compute with Multi-Agent Synergy — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-14-tmas-scaling-test-time-compute-with-mult-3abe7a.mp3

20. AI Post Transformers: Agentic Discovery for Test-Time Scaling — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-12-agentic-discovery-for-test-time-scaling-f9a81f.mp3

21. AI Post Transformers: Latent Space as a New Computational Paradigm — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-05-latent-space-as-a-new-computational-para-810f39.mp3

22. AI Post Transformers: Causal-JEPA for Object-Level World Models — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-15-causal-jepa-for-object-level-world-model-311a8b.mp3

23. AI Post Transformers: LeWorldModel: Stable Joint-Embedding World Models from Pixels — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-03-25-leworldmodel-stable-joint-embedding-worl-650f9f.mp3

Interactive Visualization: Generative Recursive Reasoning in Latent Space