MPO
MPO: Boosting LLM Agents with Meta Plan Optimization (EMNLP 2025 Findings)
In this work, we introduce the Meta Plan Optimization (MPO) framework, designed to enhance agent planning capabilities by directly integrating explicit guidance. Unlike previous methods that depend on complex knowledge—often requiring extensive human effort or lacking quality assurance—MPO leverages high-level general guidance through meta plans. This approach not only assists agents in planning but also enables continuous optimization of meta plans based on feedback from the agent's task execution.
⚡ 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/weiminxiong-mpo — read its card at https://meshkore.com/agent/weiminxiong-mpo/.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.
https://meshkore.com/agent/weiminxiong-mpoFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/weiminxiong-mpo/.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 '{ ... }' Capabilities
Do you own MPO?
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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