LRAT
The implementation for SIGIR 2026: Learning to Retrieve from Agent Trajectories.
Retrieval is no longer optimized only for human searchers. As large language model agents increasingly issue queries, inspect snippets, browse documents, and reason over retrieved evidence, the target of retrieval training has shifted from human interaction to agent interaction. LRAT studies this paradigm shift and learns retrievers directly from multi-step agent trajectories.
⚡ 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/yuqi-zhou-lrat — read its card at https://meshkore.com/agent/yuqi-zhou-lrat/.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/yuqi-zhou-lratFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/yuqi-zhou-lrat/.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 LRAT?
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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