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Ji Zeng

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#artificial intelligence Preprint Sep 2026

Rules to Tools: Executable Checks for LLM Agents in Scientific Computing

Scientific coding agents receive equations, boundary conditions, and output requirements in writing, then must assess the programs they revise. Rules to Tools (R2T) supplies prepared executable checks of public scientific requirements. Matched SciCode repair groups share written checks, starting programs, model, and bu...

Jing-Jie Ning, Guo-Jiang Zhao, Cheng-Xin Xu et al. · 0 citations
Book Open access Aug 2026

HyMAGE: Semantic-Aware Dynamic Hypergraph Generation

HyMAGE models each node as an autonomous agent and leverages LLMs for local-level semantic selection, so that hyperedge formation and dissolution emerge from local semantic affinity and structural context in a self-organizing manner.

B. Gu, Ji Zeng, Nuoran Zhou et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Scores Alone Do Not Prove Discovery: The Discovery Certification Protocol for Auditing AI Research Agents

The Discovery Certification Protocol (DCP) turns an outcome claim into an executable audit under a registered model, information boundary, and budget, and a deterministic verifier reproduces these local decisions from frozen records.

Jing-Jie Ning, Shan Zhong, Xiao-Chuan Li et al. · 1 citation
Book Open access Aug 2026

HyMAGE: Semantic-Aware Dynamic Hypergraph Generation

Understanding hypergraph evolution is essential for revealing high-order interaction patterns and generating credible synthetic data when real interaction records are scarce. Existing models suffer from two key limitations: (1) they rely on global topological heuristics that treat nodes as passive entities, yielding po...

B. Gu, Ji Zeng, Nuoran Zhou et al. · 0 citations

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