AIBullisharXiv – CS AI · Jun 27/10
🧠Researchers propose MAAD (Multi-Agent Architecture Design), a framework using orchestrated AI agents with external knowledge and hierarchical memory to automate software architecture design from requirements. The system outperforms existing approaches and demonstrates that advanced LLMs significantly improve architectural quality and validation efficiency.
🧠 GPT-5
AINeutralarXiv – CS AI · Jun 96/10
🧠A research paper proposes replacing click-based web automation with typed actions backed by semantic APIs, arguing this shift would make AI agents more reliable, auditable, and cost-effective. The authors introduce 'web verbs' as a standardized interface for web operations that could improve agent behavior and enable trustworthy automation at scale.
AINeutralarXiv – CS AI · Jun 46/10
🧠Researchers propose Software 4.0, a new programming paradigm that integrates human intelligence, neural AI, and symbolic systems as a self-regulating network rather than static code. The approach aims to eliminate the architectural friction between traditional programming models and large language models by enabling software to verify and evolve its own integrity, potentially reducing computational overhead and inference costs.
AINeutralarXiv – CS AI · Jun 26/10
🧠Researchers conducted a controlled experiment evaluating 12 multi-agent LLM collaboration topologies for software design, running 520 tests across 8 tasks. Structural adversarial prompting ranked first, cross-model review second, while parallel merge approaches performed poorly due to token limitations and design fragmentation issues.
$GPT🧠 Claude🧠 Sonnet🧠 Opus
AINeutralarXiv – CS AI · May 276/10
🧠Researchers evaluated 13 large language models' ability to generate code following the Singleton design pattern across four prompting strategies, finding that iterative binary feedback and instruction-based guidance most effectively guide LLMs to incorporate architectural best practices while maintaining code functionality.
🧠 Llama
AINeutralarXiv – CS AI · May 76/10
🧠Researchers propose a retrieval-augmented scaffolding approach that enhances AI-assisted code generation by embedding architectural constraints and infrastructure requirements during service development. The method combines platform templates with agentic clarification loops to improve production deployability and architectural consistency compared to standard AI code generation tools.
AINeutralarXiv – CS AI · Mar 36/107
🧠Researchers introduce Theory of Code Space (ToCS), a new benchmark that evaluates AI agents' ability to understand software architecture across multi-file codebases. The study reveals significant performance gaps between frontier LLM agents and rule-based baselines, with F1 scores ranging from 0.129 to 0.646.
AINeutralarXiv – CS AI · Apr 74/10
🧠A study presents the first systematic audit of carbon footprint from GenAI usage in software architecture research and IEEE ICSA conference activities. The research provides two carbon inventories examining both AI inference usage in research papers and traditional conference operations including travel and venue energy consumption.
AINeutralarXiv – CS AI · Mar 175/10
🧠Researchers propose a new C4-based documentation framework specifically designed for agentic AI systems, which operate through specialized agents collaborating via artifact exchange and tool invocation. The approach provides structured modeling vocabulary and hierarchical description techniques to capture the unique architectural patterns of these systems for industrial applications.
AINeutralarXiv – CS AI · Mar 94/10
🧠Researchers propose a reference architecture for reinforcement learning frameworks after analyzing 18 state-of-the-practice implementations. The study identifies recurring architectural components and relationships to establish a common basis for comparison, evaluation, and integration across RL frameworks.
