GenForge: An LMM Agent Framework for Intelligent Knowledge Extraction from Nuclear Fuel Reprocessing Literature

Nuclear Fuel Reprocessing literature contains critical experimental parameters, safety information, theoretical relations, and process data that are highly heterogeneous and subject to strict logical constraints. Manually interpreting complex charts and handling tedious database schema mappings imposes a high cognitive load on experts. Although existing Large Multimodal Models (LMMs) have demonstrated strong potential in information extraction, they often face engineering bottlenecks—such as poor structural compliance and a tendency to confuse entity logic—when dealing with domain databases containing complex foreign key constraints. To address this, we propose GenForge, a schema-aware extraction framework. By taking the target database schema as an explicit constraint, GenForge achieves automatic task decomposition and formatting self-correction via a “Generation–Execution–Reflection–Reforging” iterative loop. Additionally, a Local ID mechanism is introduced to ensure data lineage consistency. We evaluated GenForge on four internal evaluation corpora from nuclear fuel reprocessing literature, each aligned with a distinct database schema: Safety Event and Causal Context Extraction Schema, Property-Condition Data Extraction Schema, Model-Parameter Association Schema, and Process Topology and Stream Mapping Schema. On the independent test set, GenForge achieved 88.0% precision, 83.0% recall, and a 98.6% Schema Compliance Rate (SCR). These results indicate that GenForge, as an expert-assisted framework, reduces the need for manual JSON debugging and supports practical schema-constrained knowledge extraction under four schema-specific evaluation settings within the Nuclear Fuel Reprocessing domain.