Instead of just generating the next response, it simulates entire conversation trees to find paths that achieve long-term goals.
How it works:
- Generates multiple response candidates at each conversation state
- Simulates how conversations might unfold down each branch (using the LLM to predict user responses)
- Scores each trajectory on metrics like empathy, goal achievement, coherence
- Uses MCTS with UCB1 to efficiently explore the most promising paths
- Selects the response that leads to the best expected outcome
Limitations:
- Scoring is done by the same LLM that generates responses
- Branch pruning is naive - just threshold-based instead of something smarter like progressive widening
- Memory usage grows with tree size, there currently no node recycling