Results and Retrospective Analysis of the CODS 2025 AssetOpsBench Challenge · DeepSignal
Results and Retrospective Analysis of the CODS 2025 AssetOpsBench Challenge arXiv cs.AI · Dhaval Patel, Chathurangi Shyalika, Suryanarayana Reddy Yarrabothula, Ling Yue, Shuxin Lin, Nianjun Zhou, James Rayfield 4d ago · · · · The CODS 2025 AssetOpsBench Challenge revealed key insights on evaluation metrics and team performance in multi-agent orchestration.
Key Points Public planning leaderboard peaked at 72.73%. Hidden evaluations showed weak correlation in execution. Successful methods focused on improving response selection and guardrails. Reader Mode is being prepared.
Invisible Orchestrators Suppress Protective Behavior and Dissociate Power-Holders: Safety Risks in Multi-Agent LLM Systems AI Summary
Invisible orchestrators in multi-agent LLM systems pose significant safety risks and affect behavior dynamics.
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📰 Read Original arXiv cs.AI · Saharsh Koganti, Priyadarsi Mishra, Pierfrancesco Beneventano, Tomer Galanti 2d ago Distribution-Aware Algorithm Design with LLM Agents AI Summary
The study presents a distribution-aware algorithm leveraging LLM agents for optimized solver code generation.
Enhanced and Efficient Reasoning in Large Learning Models AI Summary
The paper proposes an efficient reasoning method for large language models, enhancing trust in generated content.
arXiv cs.CL · Luis Lara, Aristides Milios, Zhi Hao Luo, Aditya Sharma, Ge Ya Luo, Christopher Beckham, Florian Golemo, Christopher Pal 2d ago Generative Floor Plan Design with LLMs via Reinforcement Learning with Verifiable Rewards AI Summary
A new LLM-based approach generates floor plans while adhering to numerical and topological constraints using reinforcement learning.
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Why Featured
The CODS 2025 AssetOpsBench Challenge highlights crucial evaluation metrics for multi-agent orchestration, guiding developers, PMs, and investors in optimizing AI collaboration strategies and performance benchmarks.