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M. Ali

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Conference Jul 2026

Proactive Multi-Agent Systems for Autonomous Code Generation and Software Maintenance

The evolution from reactive to proactive AI systems represents a paradigm shift in software engineering, enabling autonomous agents to anticipate requirements, plan complex workflows, and execute multi-step development tasks without human intervention. This paper presents a novel multi-agent architecture for autonomous code generation and software maintenance in enterprise environments. Our framework integrates specialized AI agents for requirements analysis, code synthesis, testing, and deployment, coordinated through a hierarchical planning system with adaptive decision-making capabilities. The system employs reinforcement learning to optimize task allocation, learn from code review feedback, and improve over time. Experimental evaluation on real-world enterprise codebases demonstrates that our proactive agent system achieves 87.3% automated issue resolution, reduces bug fix latency by 62%, and maintains code quality metrics comparable to human developers. The framework successfully handles complex refactoring tasks, security vulnerability remediation, and feature implementation with minimal human oversight, representing a significant advancement toward fully autonomous software engineering workflows.

Saher Elsayed, Samer Abubaker, M. Ali et al. · 0 citations
Preprint Aug 2026

GRPO Beyond English: A Large-Scale Study of GRPO in Non-English and Multilingual Settings

Reinforcement Learning with Verifiable Rewards (RLVR), often optimized with Group Relative Policy Optimization (GRPO), has become a central recipe for improving the reasoning capabilities of pretrained language models but current studies remain heavily English-centric. We conduct a large-scale empirical study of multilingual and non-English GRPO across a wide range of base models, training languages, and different reasoning language rewards. We find that training to reason in the native language often leaves only a small gap to training for English reasoning. We further observe strong crosslingual transfer: training in one language often improves performance in many others. However, specific trends are highly model- and language-dependent. In some cases, training in a particular language induces severe regressions on out-of-domain capabilities in other languages. Our analysis shows that RLVR beyond English can provide broad crosslingual gains, but also requires broad evaluation to detect language-specific regressions.

Konstantin Dobler, Federico Scozzafava, Jonathan Janke et al. · 0 citations