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20242026
most citedExploring Flow-Lenia Universes with a Curiosity-driven AI Scientist: Discovering Diverse Ecosystem Dynamics

1 citations · 1 across the 2 of their papers we have counts for

collaborators

20 papers

cs.CY2026

Teaching Students to Question the Machine: An AI Literacy Intervention Improves Students' Regulation of LLM Use in a Science Task

O. Clerc, R. Abdelghani, C. Desvaux +3

The rapid adoption of generative artificial intelligence (GenAI) in schools raises concerns about students' uncritical reliance on its outputs. Effective use of large language mode…

cs.AI20261 cited

Exploring Flow-Lenia Universes with a Curiosity-driven AI Scientist: Discovering Diverse Ecosystem Dynamics

Thomas Michel, Marko Cvjetko, Gautier Hamon +2

We present a curiosity-driven AI scientist method for discovering system-level dynamics in Flow-Lenia, a continuous cellular automaton (CA) with mass conservation and parameter loc…

cs.CY2026

Curiosity and Metacognition: Towards a Unified Framework for Learning and Education in the Age of AI

Chloé Desvaux, Rania Abdelghani, Pierre-Yves Oudeyer +1

This chapter examines the relationship between curiosity and metacognition as critical drivers of autonomous and self-regulated learning. We synthesize recent research to propose a…

cs.LG2026

Self-Improving Language Models for Evolutionary Program Synthesis: A Case Study on ARC-AGI

Julien Pourcel, Cédric Colas, Pierre-Yves Oudeyer

Many program synthesis tasks prove too challenging for even state-of-the-art language models to solve in single attempts. Search-based evolutionary methods offer a promising altern…

cs.CY2026

The Illusion of Understanding: How Middle-Schoolers Fail to Regulate Inquiry with ChatGPT in a Science Task

Rania Abdelghani, Kou Murayama, Celeste Kidd +2

Generative AI (GenAI) tools allow for effortless task completion, potentially fostering cognitive and metacognitive laziness in students. While surveys indicate widespread GenAI us…

cs.LG2026

Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning

Thomas Carta, Clément Romac, Thomas Wolf +3

Recent works successfully leveraged Large Language Models' (LLM) abilities to capture abstract knowledge about world's physics to solve decision-making problems. Yet, the alignment…