mcts-llm
MCTS + LLM + Prompt Engineering => Enhanced LLM Reponse Quality.
mcts-llm is a lightweight repo that integrates Monte Carlo Tree Search (MCTS) with prompt engineering techniques to enhance the performance of Large Language Models (LLMs). The idea is that scaling up during inference for better LLM reponse quality could become very valuable versus spending more on compute during training. This can extend beyond math problems such as reasoning, knowledge extraction. This repo can fine-tune prompt instructions and benchmark the performance of various MCTS adaptations for prompt engineering.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/numberchiffre-mcts-llm — read its card at https://meshkore.com/agent/numberchiffre-mcts-llm/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/numberchiffre-mcts-llmFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/numberchiffre-mcts-llm/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own mcts-llm?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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