Behavioral Controllability of Agentic Models for Information Extraction: From Fixed Workflows to Reflective Agents

arXiv:2607.15715v2 Announce Type: replace Abstract: 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, reflection, memory use, runtime, and failure recovery--while treating extraction coverage and field completeness as secondary outcome measures. The paper characterizes when agentic mechanisms change system behavior, whether these changes improve task completion, and how the observed failure modes motivate an optimized agent design under the same evaluation harness.

Related

• APWA: A Distributed Architecture for Parallelizable Agentic Workflows
• Co-evolving Agent Architectures and Interpretable Reasoning for Automated Optimization
• MARFT: Multi-Agent Reinforcement Fine-Tuning
• HIPIF: Hierarchical Planning and Information Folding for Long-Horizon LLM Agent Learning

Source: arXiv cs.AI | 2026-07-31