DynaSchedBench: Calibrated Dynamic Scheduling Benchmarks and Observability Insights for LLM Agents

Forum topic · 小凯 · 2026-05-29

Summary

DynaSchedBench (arXiv:2605.27566) is a diagnostic framework for the Dynamic Flexible Job Shop Scheduling Problem (DFJSP) that addresses a methodological tension in neural combinatorial optimization: static benchmarks encourage overfitting, while uncalibrated generators mask true algorithmic capability with stochastic noise. Instead of parameter sampling, the framework uses a Sequential Event-Space Calibrator (SESC) that computes a novel Schedule Stress Index (SSI) to stratify instances by difficulty. The authors show that SESC is substantially more computationally efficient than evolutionary baselines while converging reliably to target metrics. The framework includes modular components for instance generation, snapshot-based simulation, agents, evaluation, and visualization. Using this calibrated environment, the study reveals a key limitation of LLM scheduling agents: an observability paradox, where providing full structural information degrades policy performance in step-by-step dynamic scheduling, while concise information works better. Tool augmentation and refinement strategies also fail to reliably improve performance despite heavy token consumption, and most LLM agents consistently fail to outperform strong heuristic baselines.

Paper Overview

Research area: AI Authors: Shijie Cao, Yuan Yuan, Jing Liu Published: 2026-05-28 arXiv: 2605.27566

English Summary

Progress in neural combinatorial optimization for the 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, the authors introduce DynaSchedBench, a diagnostic framework for DFJSP that rigorously controls the instance-generation process. Instead of relying on parameter sampling, the approach uses a Sequential Event-Space Calibrator (SESC) that computes a novel Schedule Stress Index (SSI) to stratify instances by difficulty.

Key Findings

Efficient calibration: SESC is substantially more computationally efficient than evolutionary baselines while converging reliably to target metrics.
Modular framework: DynaSchedBench integrates modular components for instance generation, snapshot-based simulation, agents, evaluation, and visualization.
Observability paradox: In step-by-step online decision-making for dynamic scheduling, giving LLM agents full structural information actually degrades policy performance; concise information works better.
Tool augmentation underdelivers: Despite consuming large amounts of tokens, tool-augmented and refinement strategies fail to reliably improve performance.
Heuristics remain strong: Most LLM agents consistently fail to outperform strong heuristic baselines.

Significance

DynaSchedBench offers a controlled, difficulty-stratified benchmark suite that enables fairer evaluation of neural and LLM-based schedulers, while its findings challenge the assumption that more information and tooling automatically improve LLM agent performance in dynamic optimization settings.

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*Auto-collected on 2026-05-29.*