February 2026 Podcast Recap

February 2026 Podcast Recap

Program synthesis is the problem of automatically generating code that satisfies a specification. The real challenge isn’t searching faster, it’s making the right parts of the search space searchable at all.


This week's episode is a short recap of the podcast so far. Across the past 8 conversations - spanning grammar filtering, temporal synthesis, inductive logic programming, vision-language programs, and symbolic world models - we explore 3 emergent themes.


1. Shrinking the search space, without breaking correctness

2. Why "correct" programs still behave badly

3. The real meaning of "neurosymbolic"


At a high level, all of the solutions we've explored are grappling with the problem of search - from problem representation to the optimal divide between neural and symbolic.


Credits -


• Host, Editor, Music: Bryan Landers, Technical Staff, Ndea

• https://x.com/ndea

• https://x.com/bryanlanders

• https://ndea.com

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Jaksot(14)

Inventing Inductive Logic Programming - Stephen Muggleton

Inventing Inductive Logic Programming - Stephen Muggleton

Stephen Muggleton, Emeritus Professor at Imperial College London, discusses his paper “Inductive Logic Programming”, which introduced and named the field. The paper presents a framework that combines ...

18 Kesä 57min

Recursive Program Synthesis - Aws Albarghouthi

Recursive Program Synthesis - Aws Albarghouthi

Aws Albarghouthi, Associate Professor of Computer Science at the University of Wisconsin-Madison, discusses his paper “Recursive Program Synthesis”, which introduced Escher, an inductive synthesis alg...

27 Touko 55min

DreamCoder's Wake-Sleep Library Learning - Kevin Ellis

DreamCoder's Wake-Sleep Library Learning - Kevin Ellis

Kevin Ellis, Assistant Professor at Cornell University, discusses his influential paper “DreamCoder,” which presents a system that jointly learns reusable program abstractions and a neural search stra...

7 Huhti 47min

Semantic Programming by Example with Pre-trained Models - Gust Verbruggen

Semantic Programming by Example with Pre-trained Models - Gust Verbruggen

Gust Verbruggen, Senior AI researcher and member of the PROSE team at Microsoft, discusses his paper "Semantic Programming by Example with Pre-trained Models," which introduces a framework for integra...

3 Maalis 1h 15min

Relational Decomposition for Program Synthesis - Céline Hocquette

Relational Decomposition for Program Synthesis - Céline Hocquette

The way a problem is represented can determine whether it is solvable at all.Céline Hocquette, AI researcher at Ndea and former postdoctoral researcher at the University of Oxford, discusses her paper...

2 Helmi 47min

Symbolic World Models - Top Piriyakulkij

Symbolic World Models - Top Piriyakulkij

Wasu "Top" Piriyakulkij, PhD student at Cornell University advised by Kevin Ellis, discusses his paper "PoE-World: Compositional World Modeling with Products of Programmatic Experts." The episode expl...

26 Tammi 57min

Vision-Language Programs - Antonia Wüst

Vision-Language Programs - Antonia Wüst

Antonia Wüst, PhD student at TU Darmstadt, discusses her paper "Synthesizing Visual Concepts as Vision-Language Programs," which introduces a neurosymbolic approach to visual concept induction by comb...

19 Tammi 54min