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.
A multi-agent testing framework in which requirement analysis, test-case generation, sandboxed execution, and defect detection are delegated to four distinct agents, and in which detection rests on a composite rule combining the execution signal with the semantic judgment of a dedicated diagnostic agent.
Yuxuan Li· Transactions on Computing Sc...· 0 citations
Developing a GenAI agent that autonomously formulates, prioritizes, and completes a limited set of code improvement tasks enterprise-wide is reported, and a Difference-in-Differences analysis found that automated improvements had an effect on the reduction of future maintenance effort that was as strong as or stronger than manual improvements confirming the feasibility of autonomous maintenance.
A comprehensive overview of the existing tools and frameworks for implementing MAS in software engineering and a set of lessons learned and challenges that can help researchers and practitioners to select a suitable MAS framework according to their needs are provided.
Mariama Celi Serafim De Oliveira, M. Ibiyo, Marco Gianrusso et al.· 0 citations
Agent Gym is introduced, a modular, domain-agnostic framework that wraps any existing LLM-based agent in a continuous evaluation-and-evolution loop and introduces the Spec-to-Note Gap, an autoencoder-inspired view of agentic system transparency.
Pouya Ghiasnezhad Omran, Michael Zimmermann, Duncan Cambridge et al.· 0 citations
Multi-agent systems built on large language models (LLMs) are increasingly deployed for complex tasks requiring autonomous planning, tool use, and inter-agent coordination. However, the non-deterministic nature of LLM outputs and the emergent behavior arising from agent interactions render traditional test oracles ineffective, creating a critical gap in quality assurance for agentic AI. This work introduces MORPHAGENT, a framework designed to address the oracle problem in multi-agent LLM systems through trace-based behavioral analysis. Our contributions are threefold: (1) goal-preservation relations that verify consistent goal achievement under input perturbations, (2) coordination-consistency relations that validate inter-agent delegation and communication patterns under agent substitution and reordering, and (3) tool-use integrity relations that ensure semantic equivalence of tool invocation sequences under prompt paraphrasing. MorphAgent instruments agent execution to capture structured traces comprising planning steps, tool calls, message exchanges, and final outputs, then systematically applies metamorphic transformations and checks behavioral invariants without requiring ground-truth oracles. We evaluate the framework on four multi-agent benchmarks spanning code generation, research synthesis, customer service, and data analysis tasks, encompassing 2,840 source-followup execution pairs across three LLM backends. Results show that MORPHAGENT detects 82.0% of seeded behavioral faults, including 90.3% of coordination failures and 81.7% of goal-deviation faults, while maintaining a false positive rate of 6.1%. The framework uncovers 14 previously unreported behavioral anomalies in established multi-agent frameworks, demonstrating its practical utility for assuring agentic AI reliability. These results suggest that trace-based metamorphic testing can serve as a practical foundation for reliable validation of emerging agentic AI systems.
Gopalakrishnan Marimuthu· International Conference on...· 0 citations
AutoMOOSE is an agentic software framework that converts a natural-language request into an executed, screened, and interpreted MOOSE phase-field simulation. Here, we deploy AutoMOOSE as a agentic software, complementing our prior work which focused on development of the agentic tool. We describe our software framework and architecture through Use Case and logical views of the 1+5 architectural-views model, covering its user roles, component structure, six-agent pipeline, physics plugin layer, Model Context Protocol interface, and screening/falsification/recovery loop. Our architecture separates physical falsification from automatic repair, so corrected simulations remain inspectable and must be re-admitted before acceptance. We focus on software design, extensibility, interoperability, and reuse of the AutoMoose framework for broad utilization in multiphysics materials design problems.
Sukriti Manna, Henry Chan, Subramanian K. R. S. Sankaranarayanan· 0 citations