DynaSchedBench: Calibrated Dynamic Scheduling Benchmarks and the LLM Observability Paradox

Forum topic · 小凯 · 2026-05-29

Summary

DynaSchedBench (arXiv:2605.27566) is a diagnostic benchmark framework for the Dynamic Flexible Job Shop Scheduling Problem (DFJSP) in neural combinatorial optimization. It addresses a methodological tension: static benchmarks encourage overfitting, while uncalibrated generators obscure 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. SESC is substantially more computationally efficient than evolutionary baselines while converging reliably to target metrics, and the framework integrates modular components for instance generation, snapshot-based simulation, agents, evaluation, and visualization. Using this calibrated environment, the authors expose key limitations of LLM-based scheduling agents in step-by-step online decision making: an observability paradox, in which giving agents full structural information actually degrades policy performance while concise information works better; and a failure of tool augmentation and refinement strategies, which consume many tokens without reliably improving performance. Most LLM agents never surpass strong heuristic baselines. This makes DynaSchedBench a useful reference point for evaluating dynamic scheduling methods and LLM agents under controlled difficulty.

Overview

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

Key points

Problem: Progress in neural combinatorial optimization for the Dynamic Flexible Job Shop Scheduling Problem (DFJSP) is hindered by a methodological tension — static benchmarks encourage benchmark overfitting, while uncalibrated generators obscure algorithmic capability with stochastic noise.
Solution: DynaSchedBench, a diagnostic framework for DFJSP that rigorously controls the instance-generation process.
Calibration: Instead of relying on parameter sampling, the framework uses a Sequential Event-Space Calibrator (SESC) that computes a novel Schedule Stress Index (SSI) to stratify instances by difficulty.
Efficiency: SESC is substantially more computationally efficient than evolutionary baselines while converging reliably to the target metrics.
Modularity: The framework integrates modular components for instance generation, snapshot-based simulation, agents, evaluation, and visualization.

Findings on LLM scheduling agents

Using the calibrated environment, the paper reveals critical limitations of LLM-based scheduling agents:

Observability paradox: In step-by-step online decision making for dynamic scheduling, giving agents complete structural information actually *degrades* policy performance; concise information works better.
Tool augmentation is unreliable: Despite consuming large numbers of tokens, tool-augmented and refinement strategies fail to reliably improve performance.
Heuristics remain strong: Most LLM agents never surpass strong heuristic baselines.