ArXiv

SCHEDBench: A Benchmark for Evaluating LLM Constraint Faithfulness in Natural-Language Combinatorial Scheduling

2026-08-02 · 04:41 UTC ·Shrenil Shaun Sharma, Avi Sharma ·1 min read
Authors
Shrenil Shaun Sharma, Avi Sharma
Categories
cs.AI, cs.CL
arXiv
https://arxiv.org/abs/2608.00991v1
PDF
https://arxiv.org/pdf/2608.00991v1

Brief

SCHEDBench is a 1,132-instance benchmark that measures LLM faithfulness to combinatorial scheduling constraints under natural-language surface-form variation. It templates canonical JSP, RCPSP (single/multi-mode), nurse rostering, and timetabling problems and uses solver-derived, feasibility- and optimality-verified references. Evaluation of 13 LLMs finds brittle behavior: surface rephrasings lower feasibility and raise hard-constraint violations, especially when constraints are reordered.

Why it matters

SCHEDBench provides 1,132 canonical scheduling instances across job-shop scheduling (JSP), single- and multi-mode RCPSP, nurse rostering, and curriculum timetabling; instances are rendered into varied natural-language surface forms (domain templates, themed entities, lexical/syntactic rephrasing, constraint-level variation) and include solver-verified reference solutions for feasibility and objective optimality.

Key details

  • Across evaluations of thirteen frontier and open-weight LLMs, models were not reliably invariant to semantically equivalent renderings: surface-form variation reduced feasibility and produced above-noise per-instance increases in hard-constraint violations, with constraint reordering showing the clearest sensitivity.
Source evidence

Abstract

This paper introduces SCHEDBench, a natural-language benchmark for evaluating combinatorial scheduling constraint faithfulness under surface-form variation. Grounded in canonical scheduling instances and solver-derived feasibility and optimality, SCHEDBench assesses whether large language models (LLMs) generate schedules with the same constraint-feasible behavior across varied natural-language (NL) surface forms. SCHEDBench spans 1,132 instances across job-shop scheduling problems (JSP), single and multi-mode resource-constrained project scheduling problems (RCPSP), nurse rostering/scheduling, and curriculum timetabling problems of varying difficulty. Instances are templated into natural language problems using domain-specific templates, themed entities, lexical-syntactic template rephrasing, and constraint-level surface-form variation, with reference solutions verified for feasibility and objective optimality. Across thirteen frontier and open-weight LLMs, we find that models are not reliably invariant to semantically equivalent renderings of the same scheduling problem. Surface-form variation reduces feasibility and induces above-noise shifts in per-instance hard-constraint violations on matched instances. Among the tested isolated axes, constraint reordering yields the clearest above-noise sensitivity.

Comment: 19 Pages