SPIN enforces DAG-valid plans and prefix-based stopping for LLM agents, cutting executed tasks from 1061 to 623 and tool calls from 11.81 to 6.82 per run on AssetOpsBench while raising success from 0.638 to 0.706.
Learning to generate structured output with schema reinforcement learning
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ADEMA is a knowledge-state orchestration architecture for LLM agents that uses explicit epistemic bookkeeping, checkpoint-resumable persistence, and artifact-first assembly to support reliable long-horizon knowledge synthesis.
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SPIN: Structural LLM Planning via Iterative Navigation for Industrial Tasks
SPIN enforces DAG-valid plans and prefix-based stopping for LLM agents, cutting executed tasks from 1061 to 623 and tool calls from 11.81 to 6.82 per run on AssetOpsBench while raising success from 0.638 to 0.706.
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ADEMA: A Knowledge-State Orchestration Architecture for Long-Horizon Knowledge Synthesis with LLMAgents
ADEMA is a knowledge-state orchestration architecture for LLM agents that uses explicit epistemic bookkeeping, checkpoint-resumable persistence, and artifact-first assembly to support reliable long-horizon knowledge synthesis.