{"slug": "dynaschedbench-calibrated-dynamic-scheduling-benchmarks-and-observability-in-llm", "title": "DynaSchedBench: Calibrated Dynamic Scheduling Benchmarks and Observability Paradox in LLM-based Scheduling Agents", "summary": "Researchers introduced DynaSchedBench, a diagnostic framework for the Dynamic Flexible Job Shop Scheduling Problem that uses a Sequential Event-Space Calibrator to control instance difficulty through a novel Schedule Stress Index. Testing LLM-based scheduling agents within this calibrated environment revealed an \"Observability Paradox,\" where providing agents with complete structural information degraded performance compared to concise data. The study found that most LLM agents failed to consistently outperform strong dispatching baselines, behaving as robust heuristic approximators rather than superior optimizers.", "body_md": "arXiv:2605.27566v1 Announce Type: new\nAbstract: Progress in neural combinatorial optimization for Dynamic Flexible Job Shop Scheduling Problem (DFJSP) is currently hindered by a methodological tension: static benchmarks encourage benchmark overfitting, while uncalibrated generators obscure algorithmic capability with stochastic noise. To resolve this, we introduce \\textbf{DynaSchedBench}, a diagnostic framework for DFJSP that rigorously controls the instance-generation process. Instead of relying on parameter sampling, our approach utilizes Sequential Event-Space Calibrator (SESC) that computes a novel Schedule Stress Index (SSI) to stratify instances by difficulty. We demonstrate that SESC is substantially more computationally efficient than evolutionary baselines while converging reliably to the target metrics. The framework integrates modular components for instance generation, snapshot-based simulation, agents, evaluation, and visualization, thereby enabling rigorous testing of reactive and lookahead-based policies. Leveraging this calibrated environment, we identify key limitations of LLM-based scheduling agents. Specifically, in step-wise online decision-making for dynamic scheduling, we identify an ``Observability Paradox'': providing agents with oracle access to full structural information can degrade policy performance, underperforming concise information. Furthermore, despite substantial token overhead, tool-augmented and refinement strategies fail to reliably improve performance, and most LLM agents fail to consistently surpass strong dispatching baselines-behaving more like robust heuristic approximators than superior optimizers.", "url": "https://wpnews.pro/news/dynaschedbench-calibrated-dynamic-scheduling-benchmarks-and-observability-in-llm", "canonical_source": "https://arxiv.org/abs/2605.27566", "published_at": "2026-05-28 04:00:00+00:00", "updated_at": "2026-05-28 04:31:15.324045+00:00", "lang": "en", "topics": ["ai-research", "machine-learning", "large-language-models", "ai-agents"], "entities": ["DynaSchedBench", "Sequential Event-Space Calibrator", "Schedule Stress Index", "LLM"], "alternates": {"html": "https://wpnews.pro/news/dynaschedbench-calibrated-dynamic-scheduling-benchmarks-and-observability-in-llm", "markdown": "https://wpnews.pro/news/dynaschedbench-calibrated-dynamic-scheduling-benchmarks-and-observability-in-llm.md", "text": "https://wpnews.pro/news/dynaschedbench-calibrated-dynamic-scheduling-benchmarks-and-observability-in-llm.txt", "jsonld": "https://wpnews.pro/news/dynaschedbench-calibrated-dynamic-scheduling-benchmarks-and-observability-in-llm.jsonld"}}