ARTS improves automated scientific discovery by using reasoning LMs with test-time training to separate hypothesis merit from execution quality in tree search, achieving 15.3% relative gains on 22 MLGym and MLEBench tasks.
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DPTS shows cold-start bottlenecks at low budgets while SSDP exhibits frontier depletion, indicating fixed ToT strategies are inelastic across compute levels.
SLATE benchmark and Entropy-Guided Branching algorithm improve LLM agent success and efficiency on long-horizon tasks in large tool libraries.
citing papers explorer
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Learning the ARTS of Search for Automated Discovery
ARTS improves automated scientific discovery by using reasoning LMs with test-time training to separate hypothesis merit from execution quality in tree search, achieving 15.3% relative gains on 22 MLGym and MLEBench tasks.
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Beyond Fixed Budgets: Characterizing the Inelasticity and Limitations of Tree-of-Thought Reasoning Strategies
DPTS shows cold-start bottlenecks at low budgets while SSDP exhibits frontier depletion, indicating fixed ToT strategies are inelastic across compute levels.
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Long-Horizon Plan Execution in Large Tool Spaces through Entropy-Guided Branching
SLATE benchmark and Entropy-Guided Branching algorithm improve LLM agent success and efficiency on long-horizon tasks in large tool libraries.