DarkEQA: Benchmarking Vision-Language Models for Embodied Question Answering in Low-Light Indoor Environments

• URL: http://arxiv.org/abs/2512.24985v2
• Date: Tue, 06 Jan 2026 05:24:09 GMT
• Title: DarkEQA: Benchmarking Vision-Language Models for Embodied Question Answering in Low-Light Indoor Environments
• Authors: Yohan Park, Hyunwoo Ha, Wonjun Jo, Tae-Hyun Oh,
• Abstract summary: Vision Language Models (VLMs) are increasingly adopted as central reasoning modules for embodied agents.<n>Existing benchmarks evaluate their capabilities under ideal, well-lit conditions, yet robust 24/7 operation demands performance under a wide range of visual degradations.<n>We present DarkEQA, an open-source benchmark for evaluating EQA-relevant primitives under multi-level low-light conditions.
• Score: 24.527536145236894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision Language Models (VLMs) are increasingly adopted as central reasoning modules for embodied agents. Existing benchmarks evaluate their capabilities under ideal, well-lit conditions, yet robust 24/7 operation demands performance under a wide range of visual degradations, including low-light conditions at night or in dark environments--a core necessity that has been largely overlooked. To address this underexplored challenge, we present DarkEQA, an open-source benchmark for evaluating EQA-relevant perceptual primitives under multi-level low-light conditions. DarkEQA isolates the perception bottleneck by evaluating question answering from egocentric observations under controlled degradations, enabling attributable robustness analysis. A key design feature of DarkEQA is its physical fidelity: visual degradations are modeled in linear RAW space, simulating physics-based illumination drop and sensor noise followed by an ISP-inspired rendering pipeline. We demonstrate the utility of DarkEQA by evaluating a wide range of state-of-the-art VLMs and Low-Light Image Enhancement (LLIE) models. Our analysis systematically reveals VLMs' limitations when operating under these challenging visual conditions. Project website: https://darkeqa-benchmark.github.io/

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