✨Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective

📝 Summary:
This study investigated software developers' perspectives on Large Language Models, identifying benefits like improved workflow and entrepreneurship, alongside risks to personal well-being and reputation. It highlights key trade-offs and best practices for adopting LLMs in software development.

🔹 Publication Date: Published on Nov 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06428
• PDF: https://arxiv.org/pdf/2511.06428

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#LLMs #SoftwareDevelopment #AIinDevelopment #DeveloperExperience #TechResearch

✨AgentDevel: Reframing Self-Evolving LLM Agents as Release Engineering

📝 Summary:
AgentDevel reframes LLM agent improvement as release engineering, treating agents as shippable software. It emphasizes stable, auditable improvements through an externalized pipeline that prioritizes non-regression, leading to more reliable and traceable agent development.

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04620
• PDF: https://arxiv.org/pdf/2601.04620

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#LLMAgents #ReleaseEngineering #SoftwareDevelopment #AIResearch #MLOps

✨Multi-Agent Software Development through Cross-Team Collaboration

📝 Summary:
Existing multi-agent LLM software development yields a single solution, missing better alternatives. We introduce Cross-Team Collaboration CTC, a framework where multiple agent teams propose and communicate diverse decisions. This significantly improves software quality and generalizes well.

🔹 Publication Date: Published on Jun 13, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2406.08979
• PDF: https://arxiv.org/pdf/2406.08979
• Github: https://github.com/OpenBMB/ChatDev

✨ Spaces citing this paper:
• https://huggingface.co/spaces/shanghengdu/LLM-Agent-Optimization-PaperList

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#MultiAgentSystems #LLMAgents #SoftwareDevelopment #AICollaboration #AIResearch

✨Building AI Coding Agents for the Terminal: Scaffolding, Harness, Context Engineering, and Lessons Learned

📝 Summary:
OPENDEV is an open-source command-line AI coding agent for autonomous software engineering assistance. It uses specialized model routing, a dual-agent architecture, and efficient context management to provide robust, terminal-first assistance. This design prevents reasoning degradation and accumu...

🔹 Publication Date: Published on Mar 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05344
• PDF: https://arxiv.org/pdf/2603.05344

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#AICodingAgents #SoftwareDevelopment #CommandLine #OpenSource #ArtificialIntelligence

✨InCoder-32B-Thinking: Industrial Code World Model for Thinking

📝 Summary:
Industrial software development lacks expert reasoning traces for hardware constraints, so a model was trained on error-driven reasoning chains and domain-specific execution traces to generate high-qu...

🔹 Publication Date: Published on Apr 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03144
• PDF: https://arxiv.org/pdf/2604.03144

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#AI #CodeGeneration #IndustrialAI #WorldModels #SoftwareDevelopment