5arXiv cs.AI (Artificial Intelligence)·
E-VQA: Evidence-Backed Video Question Answering with Spatio-Temporal Grounding
Researchers from Salesforce AI Research propose Evidence-Backed Video Question Answering (E-VQA), a task requiring Video LLMs to jointly produce semantic answers and precise spatio-temporal evidence via dense tracked object segmentation masklets. They introduce ST-Evidence, the first human-verified pixel-level grounding benchmark for video QA, and ST-Evidence-Instruct, a 160k-scale training dataset. Fine-tuning on this data yields substantial gains over size-matched baselines (+27.2 t-mean, +13.8 J&F on a 7B model), revealing a critical gap between QA accuracy and true visual perception that scaling alone does not close.
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EG-VQA benchmark exposes gap between answer correctness and evidence grounding in Video-LLMs
Researchers introduce EG-VQA, a benchmark of 2,067 videos and 11,838 QA pairs designed to evaluate Video Large Language Models on both answer correctness and temporal evidence grounding simultaneously. A new metric, Evidence-Grounded F1 (EG-F1), jointly measures temporal alignment and semantic consistency against annotated ground-truth evidence. Experiments show that even strong proprietary models fail to reliably localize supporting evidence, revealing a fundamental gap between surface-level accuracy and faithful reasoning. The authors also propose EG-Reasoner, an evidence-supervised open-source model that achieves competitive results against proprietary systems.
Benchmark for view-level visual evidence identification in multi-view MLLMs for autonomous driving
A new arXiv preprint introduces a multi-view visual question answering benchmark targeting evidence-source identification in autonomous driving scenarios. Given six synchronized NuScenes camera views and a question, models must identify which camera view supports the answer — not just produce a correct answer. The 122-pair benchmark spans causality, counterfactual reasoning, and intent prediction, and exposes grounding failures that answer-only evaluation misses. The work addresses a meaningful gap between answer accuracy and correct visual grounding in safety-critical multimodal systems.
Act2Answer: Benchmarking commonsense and world knowledge retention in Vision-Language-Action models
Researchers introduce Act2Answer, a protocol for evaluating how much commonsense and factual knowledge VLA models retain after fine-tuning on robotics data. The approach converts knowledge benchmark questions into tabletop object-placement episodes, yielding action-grounded success rates that reduce confounds from low-level control failures. A large-scale study of 7 VLA models and 9 VLM baselines finds that VLAs retain solid performance on simple concepts but show larger gaps on richer semantic categories compared to their source VLMs, and that VQA co-training is associated with better knowledge retention.
AnyGroundBench: Domain-adaptation benchmark for spatio-temporal video grounding in specialized fields
Researchers introduce AnyGroundBench, a benchmark designed to evaluate Vision-Language Models on Spatio-Temporal Video Grounding (STVG) across five specialized domains: animal behavior, industry, sports, surgery, and public security. Unlike existing benchmarks that focus on zero-shot general-domain evaluation, AnyGroundBench includes dedicated training subsets to measure domain adaptability and in-context learning. Evaluation of 15 state-of-the-art VLMs reveals significant failures in both zero-shot and ICL-based adaptation, exposing weaknesses in spatio-temporal reasoning under domain shift.
AUTOPILOT-VQA: Incident-centric dashcam benchmark for evaluating VLMs on safety-critical driving scenarios
Researchers introduce AUTOPILOT-VQA, a visual question answering benchmark focused on real-world driving incidents and near-incidents captured by dashcams. The benchmark covers safety-relevant categories including weather, road conditions, accident occurrence, impact location, and avoidability reasoning, pushing models beyond object recognition toward temporally grounded safety-aware reasoning. It is released as part of the AUTOPILOT competition at CVPR 2026 and targets evaluation of vision-language models in autonomous driving contexts.
LAVE: Zero-shot VQA Evaluation on Docmatix with LLMs - Do We Still Need Fine-Tuning?
This Hugging Face blog post introduces LAVE (LLM-Assisted Visual Evaluation), a zero-shot VQA evaluation methodology applied to the Docmatix dataset. The post investigates whether large vision-language models can perform document visual question answering without task-specific fine-tuning by leveraging LLM-based evaluation metrics. The analysis probes the gap between zero-shot and fine-tuned performance on document understanding tasks, raising questions about the continued necessity of supervised adaptation for VQA.
Moment-Video: Benchmark Diagnosing Temporal Fidelity of Video MLLMs on Momentary Visual Events
Moment-Video is a new benchmark of 1,000 human-verified video-QA pairs designed to evaluate how well video multimodal large language models (MLLMs) handle brief, localized visual events that may span only a few frames. The benchmark covers 7 domains and 25 subcategories across four task types: Temporal Occurrence, Temporal Counting, Action Description, and Temporal Reasoning. Evaluation of 33 proprietary and open-source models reveals severe deficiencies: the best model (Seed-2.0-Pro) achieves only 39.6% accuracy, while most open-source models score below 25%. Diagnostic analyses show that denser frame sampling helps but does not resolve the bottleneck, pointing to fundamental limitations in how current video MLLMs represent and preserve transient visual evidence.
WikiVQABench: A Knowledge-Grounded Visual Question Answering Benchmark from Wikipedia and Wikidata
WikiVQABench is a new human-curated VQA benchmark that requires external knowledge beyond visual perception, constructed by combining Wikipedia images, captions, and Wikidata structured knowledge with LLM-generated question candidates reviewed by human annotators. The benchmark evaluates knowledge-intensive reasoning in vision-language models, covering 15 VLMs ranging from 256M to 90B parameters. Accuracy spans 24.7% to 75.6%, indicating meaningful discrimination across model scales. The dataset and code are publicly released.