6 papers · 1 filter
Reading and Steering Representations of Materials-Science Mechanisms in an Open-Weight Language Model
Markus J. Buehler
Large language models can answer scientific questions, yet a correct output does not reveal whether the model represents or uses the governing physics. Here we show that materials…
Graph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination
Subhadeep Pal, Shashwat Sourav, Tirthankar Ghosal +1
Accelerating materials discovery requires AI systems that can generate scientifically valid hypotheses through multi-step, domain-grounded reasoning. Standard large language models…
MusicSwarm: Biologically Inspired Intelligence for Music Composition
Markus J. Buehler
We show that coherent, long-form musical composition can emerge from a decentralized swarm of identical, frozen foundation models that coordinate via stigmergic, peer-to-peer signa…
Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks
Markus J. Buehler
We present an agentic, autonomous graph expansion framework that iteratively structures and refines knowledge in situ. Unlike conventional knowledge graph construction methods rely…
In-situ graph reasoning and knowledge expansion using Graph-PReFLexOR
Markus J. Buehler
The pursuit of automated scientific discovery has fueled progress from symbolic logic to modern AI, forging new frontiers in reasoning and pattern recognition. Transformers functio…
PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking
Markus J. Buehler
PRefLexOR (Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning) combines preference optimization with concepts from Reinforcement Learning to ena…