Garfield: Graph Augmented Reasoning Framework for Integrated Experimental Lab Discovery

Experimental protocols form the knowledge backbone of laboratory experiments and automation. They contain information on experimental steps, actions, materials, instruments, parameters, and execution flow, while also sharing recurring concepts across protocols. This structure and cross-protocol knowledge remain largely implicit in natural language. To address this, we introduce GARFIELD (Graph Augmented Reasoning Framework for Integrated Experimental Lab Discovery), a framework that provides experimental protocols as structured context through our proposed multi-view experimental protocol graph representation. This graph representation captures four views: hierarchical, execution, action context, and cross-protocol alignment. The hierarchical view organizes protocols into processes, steps, and actions; the execution view captures procedural flow; the action context view connects actions with their experimental objects and parameters; and the cross-protocol alignment view links equivalent actions, materials, instruments, products (outputs), and biological samples across protocols. During inference, our graph-aware GARFIELD agent dynamically interacts with the multi-view experimental protocol graph representation with a dedicated set of tools to adaptively retrieve the necessary context based on the query. Experiments show that GARFIELD outperforms language-model and retrieval baselines in retrieving experimental details (+11.5% accuracy), reasoning over questions on structural relationships (+0.326 Token-F1), detecting protocol errors (+11.6% accuracy), and resolving procedural order (+14.4% sequence EM). We also evaluate GARFIELD on protocol design, where access to its structured context improved overlap with gold protocols for experimental operations and execution flow by 27.0% and 13.6%, respectively. These results show that GARFIELD can complement large language models by providing structured experimental context for both protocol knowledge retrieval and design.