Reason in Chains, Learn in Trees: Self-Rectification and Grafting for Multi-turn Agent Policy Optimization explores A framework that optimizes multi-turn agent policies by recovering latent reward structures and synthesizing corrective reasoning through a cognitive tree.. Commercial viability score: 7/10 in Agents.
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Canonical route: /paper/reason-in-chains-learn-in-trees-self-rectification-and-grafting-for-multi-turn-agent-policy-optimization
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Canonical ID reason-in-chains-learn-in-trees-self-rectification-and-grafting-for-multi-turn-agent-policy-optimization | Route /paper/reason-in-chains-learn-in-trees-self-rectification-and-grafting-for-multi-turn-agent-policy-optimization
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/reason-in-chains-learn-in-trees-self-rectification-and-grafting-for-multi-turn-agent-policy-optimizationMCP example
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}Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
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