AI Research
Agentic World Modeling for Agent Test-time Scaling without Environment Replica
A study of agent test-time scaling without an environment replica
Test-time scaling (TTS) improves large language model agents by sampling and selecting among multiple candidate trajectories, but common realizations assume that these trajectories can be executed in parallel.
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ArXiv CS.AI
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ArXiv CS.AI
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Generated insight by OnAirToday AI
Sampling and selecting candidate trajectories can improve language-model agents, but parallel execution is a common assumption. An approach that avoids requiring an environment replica could matter where that assumption does not hold.
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