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An Open Source Conversation Response Path Exploration System using Monte Carlo Tree Search (github.com)

submitted 4 days ago by [email protected] to c/[email protected]

8 comments fedilink hide all child comments

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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[–] [email protected] 0 points 4 days ago (1 children)

Interesting. I’m not sophisticated enough to judge this particular implementation but the concept of generating entire conversation trees to judge the quality of an output intrigues me for sure and I’d be interested in reading more about it and any research around it. Got any good links for further reading?

[–] [email protected] 0 points 4 days ago* (last edited 4 days ago)

I think that's an interesting approach as well. There are a bunch of research papers on using MCTS with LLMs, a few examples here:

https://arxiv.org/abs/2503.19309

https://arxiv.org/abs/2505.23229

https://arxiv.org/abs/2504.02426

https://arxiv.org/abs/2504.11009

https://arxiv.org/abs/2502.13428