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 synthesis

Guests & 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-latticeColin “Coco” Conwell / Research Scientist, MIThttps://colinconwell.com/https://openreview.net/forum?id=nuZBUyzBs0Chris Hamblin / Research Scientist, After Thoughthttps://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, Ndea• Camera & Audio: Rowan Rintala• Editor: Alejandro Ramirez• https://x.com/ndea• https://x.com/bryanlanders• https://ndea.com

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 synthesis

References -

• 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=s6GKwRIQRf0

About the Paper -

“Program Synthesis And Non-monotonic Reasoning (Extended Abstract)”

Kedar S. NamjoshiPresented at SYNT 2025 Workshop

The 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=sharing

About 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.io

Credits -

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

• Editor: Alejandro Ramirez

• https://x.com/ndea

• https://x.com/bryanlanders

• https://ndea.com

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

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