Arastun Mammadli

🚀 New paper: AgentSearchBench — a benchmark for AI agent search in the wild. As agent ecosystems grow, a fundamental question emerges: how do we find the right agent for a task? We build a benchmark with ~10k real-world agents and show: • Semantic similarity ≠ agent performance • Description-based ranking often underestimates capable agents • Execution-aware probing improves ranking 📄 Paper: arxiv.org/abs/2604.22436 Joint work with Arastun Mammadli @arastunmammadli (co-first author) and Xiaoyu Zhang, Emine Yilmaz @emine_yilm at the UCL AI Center @ai_ucl and UCL Computer Science @uclcs #AI #Agents #LLM #InformationRetrieval