Mango raises web agent success rates to 63.6% on WebVoyager and 52.5% on WebWalkerQA by bandit-based starting-point selection and memory, beating baselines by 7.3% and 26.8%.
The Thirteenth International Conference on Learning Representations , year=
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Meta-prompt optimization enables LLM agents to discover stable, generalizable tacit collusion strategies in market simulations that outperform hand-crafted prompt baselines.
citing papers explorer
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Mango: Multi-Agent Web Navigation via Global-View Optimization
Mango raises web agent success rates to 63.6% on WebVoyager and 52.5% on WebWalkerQA by bandit-based starting-point selection and memory, beating baselines by 7.3% and 26.8%.
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Prompt Optimization Enables Stable Algorithmic Collusion in LLM Agents
Meta-prompt optimization enables LLM agents to discover stable, generalizable tacit collusion strategies in market simulations that outperform hand-crafted prompt baselines.