Abstract Synthesis

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 sci...

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 it...

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 an...

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 chall...

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....

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 p...

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 emer...

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...

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 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...

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 offe...

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 ba...

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 inter...

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...

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 correctne...

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