2 citations · 5 across the 10 of their papers we have counts for
11 papers
SwarmWorld: Stigmergic technological evolution in societies of language-model agents
Subhadeep Pal, Fiona Y. Wang, Markus J. Buehler
Collective intelligence can emerge when individuals coordinate through a shared environment, allowing local actions to accumulate into durable social organization. Language-model a…
Visualizing Graph-to-Answer Mechanism Recovery in Materials-Science Hypothesis Generation
Shashwat Sourav, Subhadeep Pal, Markus J. Buehler +4
AI co-scientists can generate fluent materials-science hypotheses, but fluency does not show that an answer preserves a scientifically meaningful mechanism. We present a graph-to-a…
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, using three open-weigh…
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…
ProjectionBench: Evaluating Scientific Hypothesis Generation in LLMs Under Progressive Information Disclosure
A. J. Lew, Y. Cao, M. J. Buehler
Scientific discovery is an inherently creative and uncertain process, requiring reasoning beyond the recall of known knowledge. While many benchmarks have been proposed to evaluate…
Selective Imperfection as a Generative Framework for Analysis, Creativity and Discovery
Markus J. Buehler
We introduce materiomusic as a generative framework linking the hierarchical structures of matter with the compositional logic of music. Across proteins, spider webs and flame dyna…
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…
Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design
Markus J. Buehler
We present Cephalo, a series of multimodal vision large language models (V-LLMs) designed for materials science applications, integrating visual and linguistic data for enhanced un…