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submitted 2 days ago by [email protected] to c/[email protected]

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
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this post was submitted on 04 Jul 2025
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