Higher-Order Knowledge Representations for Agentic Scientific Reasoning

• URL: http://arxiv.org/abs/2601.04878v1
• Date: Thu, 08 Jan 2026 12:25:37 GMT
• Title: Higher-Order Knowledge Representations for Agentic Scientific Reasoning
• Authors: Isabella A. Stewart, Markus J. Buehler,
• Abstract summary: We introduce a methodology for constructing hypergraph-based knowledge representations that faithfully encode multi-entity relationships.<n> Applied to a corpus of 1,100 manuscripts on biocomposite scaffolds, our framework constructs a global hypergraph of 161,172 nodes and 320,201 hyperedges.<n>We further demonstrate that equipping agentic systems with hypergraph tools, specifically using node-intersection constraints, enables them to bridge semantically distant concepts.
• Score: 1.1458853556386797
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Scientific inquiry requires systems-level reasoning that integrates heterogeneous experimental data, cross-domain knowledge, and mechanistic evidence into coherent explanations. While Large Language Models (LLMs) offer inferential capabilities, they often depend on retrieval-augmented contexts that lack structural depth. Traditional Knowledge Graphs (KGs) attempt to bridge this gap, yet their pairwise constraints fail to capture the irreducible higher-order interactions that govern emergent physical behavior. To address this, we introduce a methodology for constructing hypergraph-based knowledge representations that faithfully encode multi-entity relationships. Applied to a corpus of ~1,100 manuscripts on biocomposite scaffolds, our framework constructs a global hypergraph of 161,172 nodes and 320,201 hyperedges, revealing a scale-free topology (power law exponent ~1.23) organized around highly connected conceptual hubs. This representation prevents the combinatorial explosion typical of pairwise expansions and explicitly preserves the co-occurrence context of scientific formulations. We further demonstrate that equipping agentic systems with hypergraph traversal tools, specifically using node-intersection constraints, enables them to bridge semantically distant concepts. By exploiting these higher-order pathways, the system successfully generates grounded mechanistic hypotheses for novel composite materials, such as linking cerium oxide to PCL scaffolds via chitosan intermediates. This work establishes a "teacherless" agentic reasoning system where hypergraph topology acts as a verifiable guardrail, accelerating scientific discovery by uncovering relationships obscured by traditional graph methods.

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