The optimal reasoning strategy for LLMs depends on the model's diversity profile rather than the exploration method itself.
less improved
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
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
-
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.
-
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.
- Shepherd: A Runtime Substrate Empowering Meta-Agents with a Formalized Execution Trace