AI RESEARCH
From Agent Loops to Deterministic Graphs: Execution Lineage for Reproducible AI-Native Work
arXiv CS.AI
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ArXi:2605.06365v1 Announce Type: new Large language model systems are increasingly deployed as agentic workflows that interleave reasoning, tool use, memory, and iterative refinement. These systems are effective at producing answers, but they often rely on implicit conversational state, making it difficult to preserve stable work products, isolate irrelevant updates, or propagate changes through intermediate artifacts. We compare execution-lineage replay against loop-centric update baselines on two controlled policy-memo update tasks.