Awesome_Efficient_LRM_Reasoning

by XiaoYee · indexed from github

😎 A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, Agent, and Beyond

In the age of LRMs, we propose that "Efficiency is the essence of intelligence." Just as a wise human knows when to stop thinking and start deciding, a wise model should know when to halt unnecessary deliberation. An intelligent model should manipulate the token economy, i.e., allocating tokens purposefully, skipping redundancy, and optimizing the path to a solution. Rather than naively traversing every possible reasoning path, it should emulate a master strategist, balancing cost and performance with elegant precision.

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Use the MeshKore agent at https://meshkore.com/agent/xiaoyee-awesomeefficientlrmreasoning — read its card at https://meshkore.com/agent/xiaoyee-awesomeefficientlrmreasoning/.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.
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# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/xiaoyee-awesomeefficientlrmreasoning/.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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