Behavioral Controllability of Agentic Models for Information Extraction: From Fixed Workflows to Reflective Agents
Large language model (LLM) agents are increasingly used for complex information-extraction tasks, yet it remains unclear whether agentic components such as reflection and memory lead to observable and controllable improvements over fixed LLM workflows. We study this question through conference-paper dataset extraction, where a system must identify datasets mentioned in scholarly PDFs and produce structured records. We compare a fixed workflow baseline with reflective agent variants and specify an optimized agent condition (S2) that extends the same task with richer PDF tools and dynamic tool selection. Our evaluation emphasizes process-level behavior--including tool execution, retries, re...
| Authors | Lujia Zhang, Xingzhou Chen, Hongwei Feng |
|---|---|
| Primary category | cs.AI |
| Published | 17 July 2026 |
| Last updated at source | 29 July 2026 |
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