Abstract Synthesis

About Abstract Synthesis

Go beyond the paper abstract to synthesize new ideas. AGI research lab Ndea presents the stories behind remarkable academic papers in the field of program synthesis.

Publisher

Ndea

Category

technology

Language

en

Explicit

No

First episode

15 Dec 2025

Latest episode

22 Jul 2026

Latest episodes

16 episodes in the feed.

1. 

   22 Jul 2026

   Why Creativity Cannot Be Interpolated - MLST

   Jeremy Budd, Assistant Professor at the University of Birmingham, and Tim Scarfe, CEO of Machine Learning Street Talk, discuss the paper “Why Creativity Cannot Be Interpolated”, which argues that genuine creativity requires respect for constraints that today’s AI lacks. Building on ideas from François Chollet, Kenneth Stanley, and others, explore why AI slop is the result of novelty unconstrained by understanding and how systems capable of extending their own phylogeny could become creative, regardless of substrate. In This Episode - • Creativity vs. interpolation • Understanding as structured constraint-following • Picbreeder’s evolutionary image representations • AlphaZero and creative game play • Why LLMs remain highly derivative • Human-AI co-creativity • Open-ended search vs. optimization • Evolvable representations and abstraction • Constraints enable creativity • Future directions beyond gradient descent References - • CBMM10 Panel: Research on Intelligence in the Age of AI - https://www.youtube.com/watch?v=Gg-w_n9NJIE&t=2885 • “Sparks of Artificial General Intelligence: Early experiments with GPT-4” - https://arxiv.org/abs/2303.12712 • Chollet: “On the Measure of Intelligence” - https://arxiv.org/abs/1911.01547 • Stanley: PicBreeder - https://picbreeder.net/6793 • Sakana’s PicBreeder Experiment - https://pub.sakana.ai/picbreeder-vlm/ • ARC-AGI-3 - https://arcprize.org/arc-agi/3 About the Paper - “Why Creativity Cannot Be Interpolated: And Why Understanding Is the Path to Get There” Jeremy Budd and Tim Scarfe The paper argues that novelty alone is insufficient for creativity. Instead, creative systems must develop structured, path-dependent representations that preserve the constraints underlying previous discoveries, allowing them to extend rather than merely recombine existing ideas. Through examples including Picbreeder, AlphaGo, AlphaZero, and modern large language models, the authors propose that human-AI collaboration currently offers the strongest path toward genuinely creative machine intelligence. https://arxiv.org/abs/1911.01547 About the Guests - Dr. Jeremy Budd is Assistant Professor of Mathematics at the University of Birmingham. His research focuses on the intersection of applied analysis and data science, specializing in graph-based learning methods for image processing. https://jeremybudd.com/ Dr. Tim Scarfe is the founder and host of the popular AI podcast Machine Learning Street Talk (MLST). He’s a multi-time startup founder and was previously a Principal Engineer at Microsoft and Chief Data Scientist at bp. He has a Ph.D in machine learning and a first-class degree in computer science. https://www.mlst.ai/about https://www.youtube.com/@MachineLearningStreetTalk Credits - • Host & Music: Bryan Landers, Technical Staff, Ndea • Editor: Alejandro Ramirez • https://x.com/ndea • https://x.com/bryanlanders • https://ndea.com

2. 

   1 Jul 2026

   Constrained Adaptive Rejection Sampling - Loris D’Antoni

   Loris D’Antoni, Professor of Computer Science and Engineering at UC San Diego, discusses his paper “Constrained Adaptive Rejection Sampling,” which introduces a constrained decoding algorithm that preserves the original language model distribution while satisfying formal constraints, enabling higher-quality structured generation for applications including compiler testing, code generation, and scientific discovery. Explore how the rise of large language models has reshaped research in programming languages, and how formal methods remain essential for producing software that is both useful and trustworthy in the era of AI-generated code. In This Episode - • Program synthesis in the age of LLMs • Why constrained decoding distorts language model distributions • Adaptive rejection sampling with remembered failures • Formal methods for trustworthy AI-generated code • Compiler fuzzing with language models • Using symbolic methods to improve LLM output • Automata theory • Verified code translation and equivalence checking References - • UCSD Programming Systems: https://cseweb.ucsd.edu/groups/progsys/ • Nadia Polikarpova: https://cseweb.ucsd.edu/~npolikarpova/ • Rajeev Alur: https://www.cis.upenn.edu/~alur/ • Code Metal: https://www.codemetal.ai/ About the Paper - “Constrained Adaptive Rejection Sampling” Loris D’Antoni, Pavel Parys, Sriram Vadia, Taylor Berg-Kirkpatrick Large language models often rely on constrained decoding to generate outputs that satisfy grammars or structured schemas, but existing methods can substantially distort the model’s probability distribution. This paper introduces Constrained Adaptive Rejection Sampling (CARS), an algorithm that incrementally learns from rejected samples while provably sampling from the correct constrained distribution, producing significantly higher-quality outputs and large improvements in practical tasks such as compiler fuzzing. https://arxiv.org/pdf/2510.01902 About the Guest - Loris D’Antoni is Jacobs Faculty Scholar and Professor of Computer Science and Engineering at the University of California, San Diego, where he leads the Programming Systems Group. His research spans program synthesis, programming languages, formal verification, compiler testing, and trustworthy AI systems, with recent work focusing on combining formal methods with LLMs. He also serves as a Scholar at Code Metal, where he works on verified AI-assisted software engineering. https://cseweb.ucsd.edu/~ldantoni/ Credits - • Host & Music: Bryan Landers, Technical Staff, Ndea • Editor: Alejandro Ramirez • https://x.com/ndea • https://x.com/bryanlanders • https://ndea.com

3. 

   18 Jun 2026

   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 logic programming with machine learning, enabling systems to learn interpretable logical rules from examples and background knowledge. Muggleton reflects on the intellectual origins of ILP, tracing its development through his PhD work under Donald Michie and his interactions with pioneering figures including John McCarthy, Ross Quinlan, and others from the early AI community. He describes how dissatisfaction with purely propositional learning systems motivated a search for richer representations capable of expressing structured knowledge and supporting scientific discovery. In This Episode - • Origins of ILP • Michie, Turing, and AI research bans • Logic programming meets machine learning • Learning from positive examples • Learning from a single example • Predicate invention & abstraction • Robot Scientist research program • Efficient greedy search algorithms • ILP & modern large language models References - • https://www.doc.ic.ac.uk/~shm/Papers/Reduce.pdf • https://en.wikipedia.org/wiki/Donald_Michie • https://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist) • https://en.wikipedia.org/wiki/Ross_Quinlan • https://en.wikipedia.org/wiki/Karl_Popper About the Paper - “Inductive Logic Programming” Author: Stephen Muggleton Venue: New Generation Computing (1991) The paper formally introduced inductive logic programming as a research field at the intersection of machine learning and logic programming. It argues that learning systems should be able to construct logical theories using both observed examples and existing background knowledge, enabling more expressive and interpretable forms of machine learning. https://www.doc.ic.ac.uk/~shm/Papers/ilp.pdf About the Guest - Stephen Muggleton is Emeritus Professor of Machine Learning at Imperial College London. He is the founder of inductive logic programming and has made foundational contributions to machine learning, scientific discovery systems, program synthesis, and neurosymbolic AI. His research focuses on machine learning, logic-based reasoning, scientific discovery, probabilistic inference, and automated knowledge acquisition. https://www.doc.ic.ac.uk/~shm/ Credits - • Host & Music: Bryan Landers, Technical Staff, Ndea • Editor: Alejandro Ramirez • https://x.com/ndea • https://x.com/bryanlanders • https://ndea.com

4. 

   27 May 2026

   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 algorithm for learning recursive programs from input-output examples.The project emerged from Albarghouthi’s early work in program verification and inductive proofs for recursive procedures. After he and fellow graduate student Zachary Kincaid developed initial ideas for synthesizing recursive programs, they cold-emailed Sumit Gulwani at Microsoft Research, whose feedback and collaboration helped shape the direction of the paper.In This Episode -- Recursive synthesis from examples- Escher’s forward and backward search- Goal graphs for partial programs- Components as reusable building blocks- Synthesis benchmarks and comparisons with Sketch- Quantum compiler synthesis- Qubit mapping and routing synthesis- Agent correctness and prompt injectionReferences -- Microsoft PROSE: https://www.microsoft.com/en-us/research/project/prose/- SKETCH: https://people.csail.mit.edu/asolar/papers/Solar-Lezama09.pdf- Generating Compilers for Qubit Mapping and Routing: https://arxiv.org/abs/2508.10781- Synthesizing Quantum-Circuit Optimizers: https://arxiv.org/abs/2211.09691- ‘Introduction to Neural Network Verification’ book: https://verifieddeeplearning.com/About the Paper -“Recursive Program Synthesis”Aws Albarghouthi, Sumit Gulwani, and Zachary KincaidComputer Aided Verification, CAV 2013The paper presents Escher, a synthesis algorithm that learns recursive procedures from input-output examples. Escher combines component-based enumeration, interactive example refinement, and a goal graph that helps assemble partial programs into complete recursive solutions.https://www.microsoft.com/en-us/research/publication/recursive-program-synthesis/About the Guest -Aws Albarghouthi is an associate professor of computer science at the University of Wisconsin-Madison. His research focuses on program synthesis, formal verification, quantum computing systems, and the correctness of AI agents.https://pages.cs.wisc.edu/~aws/Credits -Host & Music: Bryan Landers, Technical Staff, NdeaEditor: Alejandro Ramirezhttps://x.com/ndeahttps://x.com/bryanlandershttps://ndea.com

5. 

   7 Apr 2026

   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 strategy through an iterative wake-sleep process. The work emerged from early efforts in library learning and a broader question about how humans accumulate concepts over time. Ellis reflects on the challenge of searching vast program spaces and how inspiration from cognitive processes, particularly dreaming and replay, led to a system that incrementally builds knowledge by reusing prior solutions. In This Episode - • Program synthesis beyond formal specifications • Natural language as executable programs • Library learning for compositional reuse • Wake-sleep cycles for program learning • Neural-guided search over program space • E-graph refactoring for abstraction discovery • Emergence of map and fold primitives • Probabilistic programs for uncertainty • World models beyond frame prediction • Program synthesis benchmarks References - • ARC-AGI-3: https://arcprize.org/arc-agi/3 • ExoPredicator: https://arxiv.org/abs/2509.26255 • AutumnBench: https://www.basis.ai/blog/autumn-platform-2025/ About the Paper - “DreamCoder: bootstrapping inductive program synthesis with wake-sleep library learning” Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sablé-Meyer, Lucas Morales, Luke Hewitt, Luc Cary, Armando Solar-Lezama, Joshua B. Tenenbaum PLDI 2021 (ACM SIGPLAN Conference on Programming Language Design and Implementation) DreamCoder is a program synthesis system that learns both a library of reusable program components and a neural search policy by iteratively solving tasks and compressing solutions into abstractions. It alternates between solving problems (wake phase) and improving its internal representations via abstraction and dreaming phases, enabling more efficient search and generalization across domains. https://dl.acm.org/doi/10.1145/3453483.3454080 About the Guest - Kevin Ellis is an Assistant Professor at Cornell University working on program synthesis, neurosymbolic AI, and computational models of cognition. His research focuses on learning structured representations such as programs that capture compositional knowledge about the world. https://www.cs.cornell.edu/~ellisk/ Credits - • Host & Music: Bryan Landers, Technical Staff, Ndea • Editor: Alejandro Ramirez • https://x.com/ndea • https://x.com/bryanlanders • https://ndea.com

6. 

   3 Mar 2026

   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 integrating inductive program synthesis with large language models. The project emerged from an attempt to extend Flash Fill-style program synthesis beyond purely syntactic string transformations. Motivated by limitations in symbolic systems - especially their inability to access semantic knowledge without manually encoding it - Verbruggen and collaborators explored how GPT-3 could serve as a semantic oracle within the PROSE framework. The result is a neurosymbolic architecture that preserves the efficiency and guarantees of symbolic synthesis while selectively delegating semantic subproblems to a language model. In This Episode • Limitations of both program synthesis and LLMs • Programming by example • Syntactic versus semantic • Integrating GPT-3 as semantic operators • Semantic map, position, and condition operators • Deductive backpropagation in PROSE • Deferred query execution for efficiency • Greedy clustering to control search explosion • Ranking programs to minimize semantic calls References • https://www.microsoft.com/en-us/research/group/prose/ • https://www.microsoft.com/en-us/research/project/prose-framework/ • https://www.dagstuhl.de/en/seminars/seminar-calendar • Sumit Gulwani's Flash Fill talk: https://youtu.be/421gU482xFE About the Paper "Semantic Programming by Example with Pre-trained Models" Gust Verbruggen, Vu Le, Sumit Gulwani Proceedings of the ACM on Programming Languages (OOPSLA), 2021 This paper presents a framework for augmenting inductive program synthesis with semantic operators powered by large language models. By decomposing tasks into syntactic and semantic subproblems, the system delegates only the irreducibly semantic components to a pre-trained model, while maintaining symbolic guarantees elsewhere. A deferred query execution strategy allows efficient learning without excessive model calls. https://dl.acm.org/doi/10.1145/3485477 About the Guest Gust Verbruggen is a researcher at KU Leuven and a member of Microsoft’s PROSE team. His work focuses on program synthesis, data wrangling, and neurosymbolic integration, particularly in real-world automation settings such as spreadsheets and code refactoring tools. • https://www.microsoft.com/en-us/research/people/gverbruggen/ • https://scholar.google.com/citations?user=TmU3sKMAAAAJ&hl=en Credits • Host & Music: Bryan Landers, Technical Staff, Ndea • Editor: Alejandro Ramirez • https://x.com/ndea • https://x.com/bryanlanders • https://ndea.com

7. 

   9 Feb 2026

   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

8. 

   2 Feb 2026

   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 “Relational Decomposition for Program Synthesis”, which introduces a representation-driven approach to inductive program synthesis based on decomposing examples into relational facts. The paper emerged from Hocquette’s long-standing engagement with inductive logic programming (ILP), beginning with her doctoral work at Imperial College London under Stephen Muggleton and continuing through her time in Andrew Cropper’s group in Oxford. Motivated by the scalability limits of learning long chains of reasoning, the work reflects a broader intellectual trajectory focused on making symbolic learning systems more efficient by rethinking representation and decomposition rather than adding domain-specific heuristics. In This Episode - • Inductive logic programming (ILP) • Deductive vs. inductive program synthesis • Relational vs. functional programs • Decomposing examples into logical facts • Datasets: ARC-AGI, 1D-ARC, strings, list functions • Systems & approaches: POPPER, ARGA, METABIAS, BEN, Hacker-Like References - • https://github.com/logic-and-learning-lab/Popper • https://andrewcropper.com/ • ARC-AGI - https://arcprize.org/arc-agi • 1D-ARC - https://arxiv.org/abs/2305.18354 • ARGA - https://arxiv.org/abs/2210.09880 • METABIAS - https://www.doc.ic.ac.uk/~shm/Papers/ECAI-546.pdf • BEN - https://arxiv.org/abs/2301.03094 • Hacker-Like - https://www.nature.com/articles/s41467-024-50966-x About the Paper - “Relational Decomposition for Program Synthesis” Céline Hocquette, Andrew Cropper arXiv, 2024 The paper proposes transforming inductive program synthesis problems into sets of relational input–output facts, allowing systems to learn smaller, reusable logical rules instead of long functional compositions. This decomposition significantly improves scalability and generalization when learning programs from few examples across strings, lists, and ARC-style reasoning tasks. https://arxiv.org/abs/2408.12212 About the Guest - Céline Hocquette, Technical Staff at Ndea, works on program synthesis, inductive logic programming, and symbolic reasoning. She completed her PhD at Imperial College London and previously held a research position at the University of Oxford in Andrew Cropper’s lab. Her work focuses on scalable learning of interpretable programs from small data. https://celinehocquette.github.io/ Credits - Host & Music: Bryan Landers, Technical Staff, Ndea Editor: Alejandro Ramirez https://x.com/ndea https://x.com/bryanlanders https://ndea.com

9. 

   26 Jan 2026

   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 explores how symbolic, programmatic world models can achieve strong generalization and sample efficiency by composing many small causal programs instead of learning a single monolithic model. The conversation traces how PoE-World emerged from earlier work on active concept learning and hypothesis testing, and how object-centric Atari environments became a natural testbed for scaling symbolic world models beyond grid worlds. Piriyakulkij reflects on design failures, surprising successes, and the moment the learned world model became interactive enough to serve as a real-time simulator. In This Episode - • Symbolic vs. neural world models • Products of programmatic experts • Modular causal rules as world models • Object-centric Atari environments • Montezuma’s Revenge as exploration benchmark • Sample-efficient learning from demonstrations • Weights as expert confidence signals • World models as executable simulators • Exploration as program testing References - • WorldCoder - https://arxiv.org/abs/2402.12275 • Object-Centric Atari - https://arxiv.org/abs/2306.08649v2 • ARC-AGI-3 - https://arcprize.org • VisualPredicator - https://arxiv.org/abs/2410.23156 • People: Marvin Minsky, François Chollet, Armando Solar-Lezama About the Paper - "PoE-World: Compositional World Modeling with Products of Programmatic Experts" Authors: Wasu Top Piriyakulkij, Yishou Wang, Hao Tang, Martha Lewis, Kevin Ellis The paper introduces a symbolic world modeling framework in which many small, interpretable programs - each encoding a simple causal rule - are combined multiplicatively into a probabilistic world model. By learning weights over these programmatic experts from limited demonstrations, the system produces accurate, stochastic simulators that generalize to new environments with minimal data. https://arxiv.org/abs/2505.10819 About the Guest - Wasu Top Piriyakulkij is a PhD student at Cornell University advised by Kevin Ellis. His research focuses on symbolic world models, program synthesis, and human-like learning and exploration in artificial agents. He is particularly interested in how compositional structure enables generalization in complex environments. • https://www.cs.cornell.edu/~wp237/ • https://scholar.google.com/citations?user=nlO1TkkAAAAJ&hl=en Credits - Host & Music: Bryan Landers, Technical Staff, Ndea Editor: Alejandro Ramirez https://x.com/ndea https://x.com/bryanlanders https://ndea.com

10. 

    19 Jan 2026

    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 neuro-symbolic approach to visual concept induction by combining vision-language models with program synthesis. The work grew out of Wüst’s early PhD research on visual concept learning with symbolic programs, initially in synthetic domains, and her dissatisfaction with reliance on pre-trained object detectors. As vision-language models matured, the project evolved into a broader attempt to treat these models as perceptual tools embedded within a symbolic reasoning system. In This Episode - • Strengths & weaknesses of vision-language models (VLMs) • Visual concept induction • Symbol grounding across image sets • Designing a domain-specific language (DSL) for visual reasoning • A probabilistic context-free grammar for program search • Interpretability benefits of synthesized visual programs • Bongard problems and human-like abstraction References - • https://arxiv.org/abs/2511.18964 • https://cs.stanford.edu/people/jcjohns/clevr/ • https://en.wikipedia.org/wiki/Bongard_problem • https://wolfstam.github.io/ • https://www.hikarushindo.com/ • https://www.ml.informatik.tu-darmstadt.de/people/lhelff/index.html • https://ojs.aaai.org/index.php/AAAI/article/view/20616 • https://arcprize.org/arc-agi About the Paper - “Synthesizing Visual Concepts as Vision-Language Programs” Antonia Wüst, Wolfgang Stammer, Hikaru Shindo, Lucas Nunes, Christian Kersting NeurIPS 2025 The paper presents a neuro-symbolic framework that combines vision-language models with program synthesis to learn visual concepts from examples. Vision-language models provide grounded symbolic representations, while program synthesis performs explicit reasoning to derive interpretable and reliable visual rules. https://arxiv.org/abs/2511.18964 About the Guest - Antonia Wüst is a PhD student at Technische Universität Darmstadt in the AI and Machine Learning Lab, supervised by Christian Kersting. Her research focuses on abstract visual reasoning, visual concept induction, and neuro-symbolic AI, with an emphasis on combining perception and symbolic reasoning. • https://www.ml.informatik.tu-darmstadt.de/people/awuest/index.html • https://x.com/toniwuest Credits - Host & Music: Bryan Landers, Technical Staff, Ndea Editor: Alejandro Ramirez https://x.com/ndea https://x.com/bryanlanders https://ndea.com

11. 

    12 Jan 2026

    Inductive Logic Programming - Andrew Cropper

    Andrew Cropper, logic luminary and creator of the popular Popper, discusses the paper "Inductive Logic Programming at 30: A New Introduction."This episode examines how inductive logic programming (ILP) learns symbolic rules from examples and background knowledge, and what it takes to build ILP systems that scale. As machine learning has shifted toward opaque, data-hungry models, ILP offers a path to interpretable, constrained programs learned from data. The paper distills 30 years of ideas (learning settings, bias, search, recursion, predicate invention, and system design) into a modern entry point for symbolic generalization.Cropper reflects on how the paper emerged alongside his work on Popper, a high-performance ILP system designed around falsification and solver-backed search. He traces this line of thinking back to his training under Stephen Muggleton, the most influencial researcher in ILP.In This Episode -• Inductive bias to constrain search.• Utilizing SAT/ASP-style engines as solver tools.• Why recursion is a decisive capability for true generalization on algorithmic tasks.• Predicate invention enabling more compact programs and better abstraction.• Popper’s core idea: learning by ruling out hypotheses via failures.• A practical research workflow advantage: adding constraints to prune search can yield orders-of-magnitude speedups without rewriting the learner.• ILP in the wild: scientific discovery loops (the "Robot Scientist" pattern), program-by-example tools (Flash Fill), and rule learning to guide RL agents.References -• https://arxiv.org/abs/2008.07912• https://github.com/logic-and-learning-lab/Popper/• https://www.cs.cmu.edu/~tom/mlbook.html• https://europepmc.org/abstract/MED/14724639• https://www.microsoft.com/en-us/research/publication/automating-string-processing-spreadsheets-using-input-output-examples/About the Paper -"Inductive logic programming at 30: a new introduction"Andrew Cropper, Sebastijan DumančićJournal of Artificial Intelligence Research (JAIR), 2022The paper explains how ILP learns symbolic rules from labeled examples plus background knowledge, and it breaks down ILP system design into learning settings, bias/representation choices, and search strategies. It also surveys major systems and practical limitations, framing modern ILP around solver-backed search, recursion, and predicate invention.https://arxiv.org/abs/2008.07912About the Guest -Andrew Cropper is an Associate Professor at the University of Helsinki and a principal investigator at ELLIS Institute Finland, where he works on combining logical reasoning with machine learning. His research centers on inductive logic programming and on building high-performance ILP systems (including Popper) that leverage modern SAT/ASP/MaxSAT solving to learn interpretable rules from data. https://andrewcropper.com/Credits -Host & Music: Bryan Landers, Technical Staff, NdeaEditor: Alejandro Ramirezhttps://x.com/ndeahttps://x.com/bryanlandershttps://ndea.com

12. 

    5 Jan 2026

    Symbolic Linear Temporal Logic over Finite Traces Synthesis - Moshe Vardi

    Moshe Vardi, Professor at Rice University and one of the most influential figures in logic, verification, and theoretical computer science, discusses his paper “Symbolic LTLf Synthesis”. This conversation explores how Linear Temporal Logic over finite traces (LTLf) provides a more practical and scalable foundation for program and controller synthesis, especially compared to classical approaches based on infinite executions. The discussion traces the deep theoretical roots of synthesis in logic, automata, and games, while connecting them to modern challenges in AI, planning, and autonomy. Moshe shares the origin story of the paper, which grew out of collaborations with AI researchers and a visiting student, explains why “simpler” finite-trace reasoning turned out to be a strength rather than a limitation, and reflects on how LTLf has helped shift the direction of research and tooling in temporal synthesis. In This Episode - • The history of program synthesis • Why infinite traces dominate classical LTL • Linear Temporal Logic over finite traces (LTLf) • Automata theory in both verification and synthesis • Implications for reactive systems, autonomy, and agent design • The future of symbolic synthesis + neurosymbolic AI research About the Paper - “Symbolic LTLf Synthesis” Shufang Zhu, Lucas M. Tabajara, Jianwen Li, Geguang Pu, Moshe Y. Vardi IJCAI, 2017 This paper introduces an automata-based approach to synthesizing controllers from LTLf specifications, leveraging the finite-trace setting to achieve simpler constructions and improved scalability. It demonstrates how symbolic techniques can make temporal synthesis more practical for AI and planning-oriented applications. https://arxiv.org/abs/1705.08426 About the Guest - Moshe Vardi is University Professor at Rice University, where his research spans logic, automata theory, formal verification, and the foundations of computer science. He has played a central role in bringing temporal logic and model checking from theory into industrial practice, while also contributing to broader discussions about AI and society. https://www.cs.rice.edu/~vardi/ Credits - Host & Music: Bryan Landers, Technical Staff, Ndea Co-Host: Mark Santolucito, Assistant Professor, Barnard College/Columbia U Editor: Alejandro Ramirez https://x.com/ndea https://x.com/bryanlanders https://ndea.com

13. 

    29 Dec 2025

    Live @ NeurIPS 2025

    This is a special episode of the Abstract Synthesis podcast featuring a series of live interviews from NeurIPS 2025 in sunny San Diego, California.Rather than centering on a single paper, this episode captures a snapshot of the current research landscape at the Neural Information Processing Systems annual conference, highlighting how program synthesis and symbolic reasoning are increasingly intersecting with vision, reinforcement learning, world models, and scientific discovery. The conversations collectively illustrate why these ideas are resurfacing as key tools for efficiency, interpretability, and generalization.In This Episode -• Visual concept induction using program synthesis combined with vision-language models• Program synthesis as a tool for scientific discovery and interpretability in quantum computing• Symbolic world modeling and efficiency-driven learning in reinforcement learning environments• Composed program induction on structured domains such as the Rubik’s cube• Program synthesis as a lens for understanding brain, behavior, and neural representations• Interpretability perspectives framing neural network training as an implicit form of program synthesisGuests & Papers Mentioned -Clément Bonnet / Technical Staff, Ndeahttps://x.com/ClementBonnet16https://arxiv.org/abs/2411.08706Antonia Wüst / PhD Student, TU Darmstadthttps://ml-research.github.io/people/awuest/index.htmlhttps://arxiv.org/abs/2511.18964Leopoldo Sarra / Senior AI Research Scientist, Axiomatic_AIhttps://leopoldo.sarra.eu/https://arxiv.org/abs/2411.00230Wasu Top Piriyakulkij / PhD Student, Cornell Universityhttps://www.cs.cornell.edu/~wp237/https://topwasu.github.io/poe-worldJumyung Park / Student, Gwangju Institute of Science and Technology (GIST)https://jumyung-park.vercel.app/https://intrig.vercel.app/research/composed-program-induction-with-latent-program-lattice Colin "Coco" Conwell / Research Scientist, MIT https://colinconwell.com/ https://openreview.net/forum?id=nuZBUyzBs0 Chris Hamblin / Research Scientist, After Thought https://chrishamblin.xyz/Other Mentions -DreamCoder, Kevin Ellis et al.https://arxiv.org/abs/2006.08381ARC-AGI Benchmarkhttps://arcprize.org/arc-agiCredits -Host & Music: Bryan Landers, Technical Staff, NdeaCamera & Audio: Rowan RintalaEditor: Alejandro Ramirezhttps://x.com/ndeahttps://x.com/bryanlandershttps://ndea.com

14. 

    22 Dec 2025

    Program Synthesis and Non-Monotonic Reasoning - Kedar Namjoshi

    Leading formal methods researcher Kedar Namjoshi (Distinguished Member of Technical Staff, Nokia Bell Labs) discusses his extended abstract “Program Synthesis And Non-monotonic Reasoning”.This conversation explores why standard logical specifications, while well-suited for verification, can lead synthesis procedures to produce programs with unnecessary or undesirable actions, and how introducing minimality and preferences fundamentally changes the reasoning model required for synthesis.Kedar shares the research motivations behind the work, tracing its roots to non-monotonic logic and his experience synthesizing real-world control programs, and reflects on how practical scenarios like IoT device coordination expose deep gaps between formal specifications and human expectations of reasonable system behavior.In This Episode -• Why logical specifications are ideal for verification but problematic for synthesis• The notion of superfluous actions in synthesized programs• An IoT case study - door locks and refrigerators• How preference and minimality introduce non-monotonicity into synthesis• Compact strategies and minimizing behaviors in reactive systems• Checking minimality versus performing synthesis• Scaling synthesis to multi-agent systems (i.e., robotics)• Automaton-based approach limitations and LLM-based synthesisReferences -• https://synt2025.github.io/• https://link.springer.com/chapter/10.1007/978-3-030-99524-9_3• https://en.wikipedia.org/wiki/Alonzo_Church• https://dl.acm.org/doi/10.1145/3689624• https://www.youtube.com/watch?v=H53JDxt_cbI• https://www.youtube.com/watch?v=s6GKwRIQRf0About the Paper -“Program Synthesis And Non-monotonic Reasoning (Extended Abstract)”Kedar S. NamjoshiPresented at SYNT 2025 WorkshopThe paper shows that enforcing minimality in program synthesis breaks monotonic reasoning assumptions, reframing the avoidance of unnecessary actions as a problem of preference-based, non-monotonic reasoning.https://drive.google.com/file/d/1btOZ52OLD_RuvYc8KIkEk0rLDBn0lBvR/view?usp=sharingAbout the Guest -Kedar Namjoshi is a Distinguished Member of Technical Staff at Nokia Bell Labs. His research spans formal methods, program synthesis, verification, distributed systems, and the design of correct-by-construction systems, with an emphasis on bridging theory and real-world applications.https://kedar-namjoshi.github.ioCredits -Host & Music: Bryan Landers, Technical Staff, NdeaEditor: Alejandro Ramirezhttps://x.com/ndeahttps://x.com/bryanlandershttps://ndea.com

15. 

    16 Dec 2025

    Grammar Filtering For Syntax-Guided Synthesis - Mark Santolucito

    Leading program synthesis researcher Mark Santolucito (Assistant Professor, Barnard College, Columbia University) discusses his paper "Grammar Filtering for Syntax-Guided Synthesis". This conversation explores how machine learning can be used to shrink the search space of syntax-guided synthesis (SyGuS) problems, dramatically speeding up synthesis without sacrificing the strong correctness guarantees of formal methods. Mark shares the unexpected origin story of the paper, which began at a hackathon in Bermuda, explains the core ideas behind grammar filtering, and reflects on the broader role of neurosymbolic approaches in modern program synthesis. In This episode: • What program synthesis is and why it matters • Intro to syntax-guided synthesis (SyGuS) • Why grammar size dominates synthesis runtime • How machine learning can safely prune grammars before synthesis • Predicting both criticality and runtime impact of grammar terminals • Combining neural guidance with SMT-based synthesis solvers • Results from SyGuS benchmarks (including ~50% runtime improvements) • Reflections on the future of neurosymbolic program synthesis About the Paper: "Grammar Filtering for Syntax-Guided Synthesis" Kairo Morton, William Hallahan, Elven Shum, Ruzica Piskac, Mark Santolucito Published at AAAI 2020 The paper introduces a machine-learning–based technique for identifying and removing low-utility grammar terminals in SyGuS problems, significantly accelerating synthesis while maintaining correctness guarantees. https://arxiv.org/abs/2002.02884 About the Guest: Mark Santolucito is an Assistant Professor of Computer Science at Barnard College, Columbia University, where he develops novel program synthesis and analysis techniques to help programmers interact with code more effectively with a focus on Temporal Logic. https://www.marksantolucito.com Credits: Host & Music: Bryan Landers, Technical Staff, Ndea Editor: Alejandro Ramirez https://x.com/ndea https://ndea.com https://x.com/bryanlanders

16. 

    15 Dec 2025

    Introducing Abstract Synthesis

    Welcome to Abstract Synthesis - a podcast where we share the stories behind interesting academic papers in the world of program synthesis. Brought to you by AGI research lab Ndea. Subscribe wherever you get your podcasts to stay tuned for in-depth, technical interviews with leaders in the space of symbolic AI. https://ndea.com

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