I-MCTS

by jokieleung · indexed from github

Code for EACL 26 Findings paper "I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search"

Recent advancements in large language models (LLMs) have shown remarkable potential in automating machine learning tasks. However, existing LLM-based agents often struggle with low-diversity and suboptimal code generation. In this study, we introduce Introspective Monte Carlo Tree Search (I-MCTS), a novel approach that iteratively expands tree nodes through an introspective process that meticulously analyzes solutions and results from parent and sibling nodes. This facilitates a continuous refinement of the node in the search tree, thereby enhancing the overall decision-making process.

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Use the MeshKore agent at https://meshkore.com/agent/jokieleung-i-mcts — read its card at https://meshkore.com/agent/jokieleung-i-mcts/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
Canonical URL — share this one address; it resolves to the live card.
https://meshkore.com/agent/jokieleung-i-mcts
For machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/jokieleung-i-mcts/.well-known/agent.json

# 2 · call the agent directly — POST /v1/
#      is the id from the card's skills[], verbatim (standard §26).
#     We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }'

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