4arXiv cs.AI (Artificial Intelligence)·
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.
Related guides (2)
Related events (8)
TempGlitch: Benchmark for Evaluating VLMs on Temporal Glitch Detection in Gameplay Videos
TempGlitch is a new benchmark designed to evaluate vision-language models on temporal glitch detection in gameplay videos, distinguishing temporal anomalies (visible only across ordered frames) from spatial ones (visible in a single frame). The benchmark covers five temporal glitch types with paired glitch-free videos for binary evaluation, and tests 12 proprietary and open-weight VLMs across multiple frame-sampling settings. Results show current VLMs perform near chance on temporal glitches, with neither denser frame sampling nor larger model size reliably improving detection. The work highlights a systematic gap in VLM temporal reasoning capabilities relevant to automated video quality assurance.
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.
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.
VisAnomReasoner: Efficient VLM for Time-Series Anomaly Detection via VisAnomBench
Researchers introduce VisAnomBench, a curated benchmark augmenting public time-series anomaly datasets with natural-language rationales generated and selected from multiple large VLMs using task-specific rewards. Fine-tuning on this benchmark produces VisAnomReasoner, a parameter-efficient vision-language model that outperforms all baselines by at least 21.23 and 23.87 percentage points in precision and F1 on VisAnomBench. Cross-benchmark evaluation on TSB-AD-U shows further generalization gains of 9.57 and 13.39 percentage points in precision and F1, respectively.
STORM: Internalized Spatial-Temporal Reasoning for Video-Language Models via Latent Trajectories
STORMS is a two-stage training framework that teaches large vision-language models to perform spatial-temporal video reasoning through bounded continuous latent trajectories rather than explicit textual chain-of-thought, keyframe selection, or external tool use. In Stage I, latent tokens are aligned with thought-video representations derived from generated videos; in Stage II, answer-only supervision internalizes the reasoning process. At inference time, no video regeneration or frame reinsertion is required, reducing latency and engineering complexity. Evaluations on VideoMME, MVBench, TempCompass, and MMVU show improved accuracy with substantially lower inference overhead versus tool-based pipelines.
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.
HarmVideoBench: Multi-layered benchmark for harmful video understanding in large multimodal models
Researchers introduce HarmVideoBench, a diagnostic benchmark of 1,379 videos paired with 4,137 multiple-choice questions designed to evaluate harmful video understanding across three hierarchical dimensions: Observable Evidence, Clip-Internal Meaning, and Beyond-Clip Reasoning. The benchmark addresses limitations in existing work by moving beyond binary classification and requiring explanatory rationales. The authors evaluate 19 leading models and introduce BCR, a method that dynamically retrieves context based on predicted reasoning boundaries, improving macro average performance from 61.7% to 84.4%.
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.