{"slug": "behavioral-controllability-of-agentic-models-for-information-extraction-from-to", "title": "Behavioral Controllability of Agentic Models for Information Extraction: From Fixed Workflows to Reflective Agents", "summary": "A new arXiv preprint (2607.15715v1) investigates whether agentic components like reflection and memory in LLM agents produce observable and controllable improvements over fixed workflows for information extraction. The study compares a fixed workflow baseline with reflective agent variants on conference-paper dataset extraction, emphasizing process-level behavior such as tool execution, retries, and failure recovery. The authors characterize when agentic mechanisms change system behavior and how failure modes motivate an optimized agent design.", "body_md": "arXiv:2607.15715v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/behavioral-controllability-of-agentic-models-for-information-extraction-from-to", "canonical_source": "https://arxiv.org/abs/2607.15715", "published_at": "2026-07-20 04:00:00+00:00", "updated_at": "2026-07-20 13:53:34.886703+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "natural-language-processing"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/behavioral-controllability-of-agentic-models-for-information-extraction-from-to", "markdown": "https://wpnews.pro/news/behavioral-controllability-of-agentic-models-for-information-extraction-from-to.md", "text": "https://wpnews.pro/news/behavioral-controllability-of-agentic-models-for-information-extraction-from-to.txt", "jsonld": "https://wpnews.pro/news/behavioral-controllability-of-agentic-models-for-information-extraction-from-to.jsonld"}}