Test-Time Search for Automated GFM Fine-Tuning

Graph Foundation Models (GFMs) have emerged as a powerful paradigm for learning transferable graph representations, yet adapting them to downstream tasks requires navigating an exponentially large decision space, traditionally demanding heavy expert effort. We propose GFMTuner, a framework that automates GFM fine-tuning by combining Large Language Model (LLM) agents with Monte Carlo Tree Search. GFMTuner accepts natural language task descriptions and generates effective fine-tuning strategies through test-time search. We introduce the Graph-Instructed Actor, which equips the LLM with graph analysis tools to ground action generation in structural insights, and Gradient Consistency, a self-supervised reward that measures gradient alignment across perturbed executions for efficient strategy evaluation. Experiments across diverse graph domains demonstrate that GFMTuner matches or exceeds human expert designs while reducing effort from weeks to a single natural language query. The code is available in https://github.com/GFMTuner/GFMTuner