EnvSimBench reveals that state-of-the-art LLMs exhibit a universal state change cliff in environment simulation, with a new constraint-driven pipeline raising synthesis yield by 6.8% and cutting costs over 90%.
Webshop: Towards scalable real-world web interaction with grounded language agents
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SiRA uses LLM world models for simulative reasoning to achieve up to 124% higher task completion and 32.2% navigation success versus reactive baselines in web environments.
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EnvSimBench: A Benchmark for Evaluating and Improving LLM-Based Environment Simulation
EnvSimBench reveals that state-of-the-art LLMs exhibit a universal state change cliff in environment simulation, with a new constraint-driven pipeline raising synthesis yield by 6.8% and cutting costs over 90%.
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General Agentic Planning Through Simulative Reasoning with World Models
SiRA uses LLM world models for simulative reasoning to achieve up to 124% higher task completion and 32.2% navigation success versus reactive baselines in web environments.