A new framework called SWORD has been introduced for user behavior simulation, which jointly optimizes multi-agent workflow topology and natural-language prompts. This approach is guided solely by a scalar task metric, eliminating the need for domain initialization or task-specific engineering. SWORD has demonstrated statistically significant improvements over baseline methods, achieving higher accuracy with less training data and a smaller backbone model, while also autonomously discovering domain-relevant signals. AI
IMPACT This framework could improve the efficiency and accuracy of user behavior modeling in AI systems.
RANK_REASON The cluster contains an academic paper detailing a new framework and its experimental results.
Read on arXiv cs.MA (Multiagent) →
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