Behavioral Controllability of Agentic Models for Information Extraction: From Fixed Workflows to Reflective Agents
Quick Answer
This study evaluates the impact of agentic components like reflection and memory on large language model (LLM) workflows for information extraction.
Quick Take
By comparing fixed workflows with reflective agent variants, the research identifies an optimized agent condition (S2) that enhances task performance through dynamic tool selection and improved failure recovery mechanisms.
Key Points
- The study focuses on extracting datasets from scholarly PDFs using agents.
- An optimized agent condition (S2) utilizes richer PDF tools for enhanced performance.
- Process-level behaviors like tool execution and memory use were emphasized in evaluations.
- Agentic mechanisms were shown to alter system behavior and improve task completion.
- Failure modes observed inform the design of optimized agents for future tasks.
Paper Resources
Source Excerpt
(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 a
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