Workshop on the Integration of Large Language Models and Reasoning, April 19th, 13:00-17:00 CEST

Hosted by Work Packages 4 and 5, this online workshop explores the fusion of large language models and reasoning. We are thrilled to welcome two esteemed speakers: Guy Van den Broeck (UCLA) and Scott Sanner (University of Toronto).

The workshop will be held online on Microsoft Teams. An email with instructions on how to join will be sent out before the event.

Call for Presentations

We invite participants to showcase their work through talks or poster sessions. Kindly indicate your preference during registration.

Registration Deadline

Please register by April 1st to secure your spot.


Free, but registration is mandatory. Reserve your spot here:

Hourly ScheduleProgram
13.00-13.30Opening remarks
13.10-13.25Contributed talk: Paul Debjit
Title: Refining and Improving the Reasoning Capabilities of LLMs
13.25-13.40Contributed talk: Angelos Poulis, Eleni Tsalapati, Manolis Koubarakis
Title: Reasoning over Description Logic-based Contexts with Transformers
13.40-13.55Contributed talk: Leonardo Lucio Custode, Chiara Camilla Rambaldi Migliore
Title: Comparing Evolutionary Methods and LLMs for Program Synthesis
14.00-14.50Invited talk: Scott Sanner (University of Toronto)
Title: Symbolic AI 3.0 (S3): Rise of the LLMs
Abstract: Large Language Models (LLMs) such as ChatGPT, GPT-4, and Gemini have emerged as a revolutionary technology for natural language and visual reasoning and numerous related AI applications. I’ll discuss some of my group’s own work on abstract reasoning and interactive conversational systems leveraging LLMs and the game-changing realizations that I have taken away from these investigations.  This talk will then discuss some general implications of the LLM era and my conjectures as to how it will shift (and has already shifted) research foci in the near future and enable levels of user-facing AI deployment that were unthinkable just two years ago.
15.05-15.20Contributed talk: Alberto Bugarín-Diz
Title: Enriching interactive explanations with fuzzy temporal constraint networks
15.20-15.35Rishi Harzra
Title: SayCanPay: Heuristic Planning with LLMs using Learnable Domain Knowledge
15.35-15.50Tias Guns / Dimos Tsouros / Kostis Michailidis
Title: Chatbots and LLMs for Constraint Programming: Opportunities and Challenges
16.00-16.50Invited talk: Guy Van den Broeck (UCLA)
Title: Symbolic Reasoning for Large Language Models
Abstract: Many expect that AI will solve society’s problems by simply being more intelligent than we are. Implicit in this bullish perspective is the assumption that AI will naturally learn to reason from data: that it can form trains of thought that “make sense”, similar to how a human expert might reason about a case, or more formally, how a mathematician might prove a theorem. This talk will investigate the question whether this behavior can be learned from data, and how we can design the next generation of AI techniques that can achieve such capabilities. It will focus on neurosymbolic reasoning for large language models, both at training and generation time, using probabilistic circuits as the architecture that bridges learning and reasoning
16.50-17.00Closing remarks

  • Antonio Di Stasio (University of Oxford)
  • Franesco Giannini (University of Siena)
  • Giuseppe De Giacomo (University of Oxford)
  • Luc De Raedt (KU Leuven)
  • Mehdi Ali (Fraunhofer)
  • Michela Milano (University of Bologna)
  • Robin Manhaeve (KU Leuven)
  • Vincent Derkinderen (KU Leuven)

Contact: Robin Manhaeve (


19 Apr 2024


13:00 - 17:00




Robin Manhaeve