Duplicate from lamm-mit/HyperGraphReasoning

Co-authored-by: Markus Buehler <mjbuehler@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+---
+
+# Higher-Order Knowledge Representations for Agentic Scientific Reasoning
+
+
+#### Isabella Stewart, Markus J. Buehler, MIT, 2025 
+Corresponding author: mbuehler@MIT.EDU
+
+#### Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
+
+## Summary
+
+Scientific inquiry increasingly requires systems-level reasoning that integrates heterogeneous experimental data, cross-domain knowledge, and mechanistic evidence into coherent, interpretable scientific explanations. While Large Language Models (LLMs) offer certain inferential capabilities for such tasks, they often depend on structured substrates such as knowledge graphs (KGs) for factual grounding. However, traditional pairwise KGs are ill-suited for scientific reasoning as they cannot adequately capture higher-order interactions among multiple entities that often govern emergent physical system behavior. To address this here we introduce a methodology for constructing hypergraph-based knowledge representations from large data corpora that move beyond traditional pairwise graphs. By explicitly encoding higher-order relationships, hypergraphs provide a more expressive and faithful framework for modeling the mechanistically coupled, multi-entity structures and physical behavior that underlie complex scientific phenomena. 
+
+Applied to a scientific corpus on biocomposite scaffolds, our framework reveals that domain knowledge can be organized into a scale-free hypergraph with ego hubs around highly connected concepts. This representation enables an explicit detection of co-occurring ideas and inter-concept influences, clarifying how these relationships shape the emergent topology of the knowledge network. We further demonstrate that equipping agentic systems with hypergraph traversal tools enables them to exploit structural features, particularly hyperedge intersections, to ground beliefs and more effectively support mechanistic inference for hypothesis generation for the design of novel composite scaffold materials. This work establishes an agentic reasoning system using hypergraphs to capture higher-order relationships, enabling more effective use of large scientific datasets to accelerate scientific discovery.
+
+
+![hypergraph_idea](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/XtyZjQkAloUoH4TTit25M.png)
+
+## Code 
+
+The full code is available at [https://github.com/lamm-mit/HyperGraphReasoning](https://github.com/lamm-mit/HyperGraphReasoning).
+
+## Citation
+
+Please cite this work as:
+
+```bibtex
+@article{stewartbuehler2025hypergraphreasoning,
+  title     = {Higher-Order Knowledge Representations for Agentic Scientific Reasoning},
+  author    = {I.A. Stewart and M.J. Buehler},
+  journal   = {arXiv},
+  year      = {2026},
+  doi       = {https://arxiv.org/abs/2601.04878}
+}

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