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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Shepherd provides a reversible execution trace substrate for LLM agents that enables meta-agents to inspect and transform runs, yielding reported gains on coding and terminal benchmarks via supervision, counterfactual repair, and RL credit assignment.
The optimal reasoning strategy for LLMs depends on the model's diversity profile rather than the exploration method itself.
TSMCTS applies Sequential Monte Carlo in two stages for tree search, claiming better performance, favorable scaling with depth, lower variance, and reduced path degeneracy than SMC and modern MCTS baselines across discrete and continuous environments.
citing papers explorer
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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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Shepherd: Enabling Programmable Meta-Agents via Reversible Agentic Execution Traces
Shepherd provides a reversible execution trace substrate for LLM agents that enables meta-agents to inspect and transform runs, yielding reported gains on coding and terminal benchmarks via supervision, counterfactual repair, and RL credit assignment.
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Your Model Diversity, Not Method, Determines Reasoning Strategy
The optimal reasoning strategy for LLMs depends on the model's diversity profile rather than the exploration method itself.
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Twice Sequential Monte Carlo for Tree Search
TSMCTS applies Sequential Monte Carlo in two stages for tree search, claiming better performance, favorable scaling with depth, lower variance, and reduced path degeneracy than SMC and modern MCTS baselines across discrete and continuous environments.