A ScholForge - hierarchical multi-agent LLM system that tackles end-to-end software engineering research end-to-end, with a natural-language research goal, and produces working code, experimental results, and a compiled manuscript without human intervention.
Abstract
Software engineering research spans many activities: surveying literature, building software, running experiments, writing papers-that typically require different tools and expertise at each stage. We present a ScholForge - hierarchical multi-agent LLM system that tackles this end-to-end. Manager agent delegates to five specialists (Librarian, Proposer, MLE Solver, Writer, Reviewer) through a Code-Act execution loop, with external capabilities exposed via three Model Context Protocol (MCP) servers. Given a natural-language research goal, the system produces working code, experimental results, and a compiled manuscript without human intervention. We present one representative case study: Prompt2Edge, an autonomously developed agentic ML pipeline for edge devices that achieved 86.71% test accuracy on Speech Commands with a 17.8k-parameter model and 0.9638 Macro-F1 on UCI HAR, consuming approximately 5 M tokens and completing the full pipeline in under 5 hours.
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