PRISM-VLM: A Multi-Axis Discriminative Benchmark for Compact Vision-Language Models

Abstract:Compact vision-language models (VLMs) now power a growing share of multimodal applications. The benchmarks used to compare them, however, inherit a frontier-centric design: each model is reduced to a single accuracy number, narrowing the inter-model gap on saturated suites and pressing models into low-score bands on harder ones. We introduce PRISM-VLM, a multi-axis discriminative benchmark that scores every item along seven axes covering the recurring failure modes (task quality, behavioral robustness, and capability bottlenecks) and combines them into a single PScore, with items recycled from fifteen public benchmarks. Across compact VLMs from the past two years, PScore separates model pairs more reliably than prior single-axis benchmarks under an item-level paired bootstrap, and surfaces behavioral differences these benchmarks average away. Even models with statistically indistinguishable PScores diverge sharply along the per-axis profile, particularly on sycophancy, which is nearly orthogonal to single-prompt accuracy. We will release the full pipeline, prompts, and per-item annotations.

Accepted to EMNLP 2026 Findings. 29 pages, 22 figures, 21 tables

Computation and Language (cs.CL)

arXiv:2609.27395 [cs.CL]

(or arXiv:2609.27395v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.27395 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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