AICLMAJul 7

Danus: Orchestrating Mathematical Reasoning Agents with Fact-Graph Memory

arXiv:2607.0644717.4Has Code
Predicted impact top 25% in AI · last 90 daysOriginality Incremental advance
AI Analysis

For researchers in automated mathematical reasoning, Danus provides a scalable orchestration method that addresses the challenge of coordinating parallel proof search with organized intermediate claims.

Danus introduces a fact-graph memory system to orchestrate multiple LLM agents for parallel proof search in research-level mathematics, enabling construction of long, detailed proofs across six case studies in algebraic geometry, singularity theory, and combinatorics.

Recent LLM-based mathematical reasoning agents have begun to tackle research-level problems and, in several cases, have contributed to the resolution of open problems. However, scaling and orchestrating such agents effectively remains challenging, due to the difficulty of coordinating parallel proof search while keeping intermediate claims organized and reliable. In this paper, we propose Danus, an orchestration system for research-level mathematical reasoning centered on a shared fact graph as a global memory-management mechanism. Danus consists of a main agent that performs planning and coordination, multiple worker agents that carry out proof search in parallel, and a stateless verifier that checks proposed mathematical claims before they are admitted into the fact graph. Each verified fact is stored together with its proof and logical dependencies, allowing the system to build long arguments incrementally while keeping the shared proof state organized. The main agent periodically summarizes the evolving proof state, redirects workers across promising directions, and supports interaction with human mathematicians through progress reports. We evaluate Danus through six research-level case studies in algebraic geometry, singularity theory, and combinatorics, illustrating how the fact-graph memory mechanism enables Danus to construct long, detailed mathematical proofs. Our results suggest that fact-graph-based orchestration provides an effective route toward scaling mathematical reasoning agents for long-horizon research problems. Danus is open source at https://github.com/frenzymath/Danus.

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