Predicting When to Trust Vision-Language Models for Spatial Reasoning

TL;DR

This paper introduces a vision-based confidence estimation framework for VLMs that predicts when to trust spatial reasoning outputs, significantly improving reliability and safety in applications like robotics.

Contribution

The authors develop a geometric verification method combining multiple signals to accurately predict VLM spatial reasoning trustworthiness, outperforming text-based confidence approaches.

Findings

Achieves 0.674 AUROC on BLIP-2, 0.583 on CLIP, outperforming baselines.

Enables selective prediction with 61.9% coverage at 60% accuracy.

Improves scene graph precision from 52.1% to 78.3% through confidence-based pruning.

Abstract

Vision-Language Models (VLMs) demonstrate impressive capabilities across multimodal tasks, yet exhibit systematic spatial reasoning failures, achieving only 49% (CLIP) to 54% (BLIP-2) accuracy on basic directional relationships. For safe deployment in robotics and autonomous systems, we need to predict when to trust VLM spatial predictions rather than accepting all outputs. We propose a vision-based confidence estimation framework that validates VLM predictions through independent geometric verification using object detection. Unlike text-based approaches relying on self-assessment, our method fuses four signals via gradient boosting: geometric alignment between VLM claims and coordinates, spatial ambiguity from overlap, detection quality, and VLM internal uncertainty. We achieve 0.674 AUROC on BLIP-2 (34.0% improvement over text-based baselines) and 0.583 AUROC on CLIP (16.1% improvement), generalizing across generative and classification architectures. Our framework enables selective prediction: at 60% target accuracy, we achieve 61.9% coverage versus 27.6% baseline (2.2x improvement) on BLIP-2. Feature analysis reveals vision-based signals contribute 87.4% of model importance versus 12.7% from VLM confidence, validating that external geometric verification outperforms self-assessment. We demonstrate reliable scene graph construction where confidence-based pruning improves precision from 52.1% to 78.3% while retaining 68.2% of edges.