The pain point of multi-turn literature search is "the user is iterating, the agent is improvising". Existing systems either hide the flow in chain-of-thought, or apply a fixed pipeline, both hard to debug and hard to align with user preferences. PaperPilot (arXiv:2607.00597) jointly proposed by UIUC, Penn, Stanford, and Together AI redefines this as "workflow induction": given an anchor paper and query, the model automatically constructs an executable DAG — stringing keyword search, citation expansion, filtering, scoring, reranking, evidence extraction as node-level operators; user feedback isn't "run again", but directly locally editing the workflow, with the query and workflow iterating together. Training is in two steps: first use supervised workflow imitation to learn high-quality trajectories, then layer on a "controlled workflow corruption" preference optimization, letting the model actively avoid branches that would crash. PaperPilot-9B trained on Qwen3.5-9B improves Hit@5 from 58.0 to 77.0 (+19pp), MRR from 47.5 to 59.4, nDCG@10 from 26.8 to 32.5, with the most critical being workflow execution error rate dropping directly from 9.5% to 0% — for researchers doing dozens of rounds of retrieval a day, a workflow crash means a re-do, this is an order-of-magnitude usability improvement. A deeper significance lies in "workflow as interface". It extends the Agent's capability boundary from "natural language prompt" to "auditable, version-controllable, human-intervenable retrieval pipeline", in line with the broader trends of agentic RL and tool-use standardization. For scenarios like enterprise knowledge bases, legal retrieval, and medical reviews that need traceable retrieval paths, PaperPilot gives an engineering paradigm reference.