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.
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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.
PGT: Procedurally Generated Tasks for Improving Visual Grounding in MLLMs
This paper introduces Procedurally Generated Tasks (PGT), a data-driven framework that overlays geometric primitives on images to create dense supervision signals for fine-grained visual grounding in multimodal large language models. PGT serves both as a training augmentation method and a diagnostic tool to isolate perception failures from semantic priors. Instruction tuning on LLaVA-v1.5-Instruct augmented with PGT data yields gains of up to +20% on the What'sUp benchmark and +13.3% on CV-Bench-2D. The results suggest that spatial reasoning deficits in MLLMs stem primarily from inadequate supervision rather than architectural or resolution constraints.
TimeScope: How Long Can Your Video Large Multimodal Model Go?
Hugging Face introduces TimeScope, a benchmark designed to evaluate video large multimodal models (LMMs) across varying video lengths and temporal reasoning demands. The benchmark targets a known gap in existing evaluations: most video benchmarks use short clips, leaving long-video understanding largely untested. TimeScope aims to systematically probe how model performance degrades or holds as video duration increases.
EpiCurveBench: A Benchmark for Evaluating VLMs on Epidemic Curve Digitization
EpiCurveBench introduces a benchmark of 1,000 real-world epidemic curve images and a new evaluation metric (EpiCurveSimilarity, ECS) designed to assess vision-language models on time-series chart extraction, addressing limitations of existing metrics that ignore temporal structure. Evaluating six methods including three frontier closed VLMs, one open VLM, and two specialized chart-extraction systems, the best model achieves only 52.3% ECS, revealing substantial headroom compared to saturating scores on ChartQA. ECS is validated against downstream epidemiological statistics and shown to correlate 1.5–3.6× more strongly than Dynamic Time Warping across four summary metrics. The benchmark targets the public-health use case of digitizing historical outbreak data trapped in published figures, but generalizes to any structured time-series chart-extraction task.
PaSBench-Video: A Streaming Video Benchmark for Proactive Safety Warning in MLLMs
PaSBench-Video is a 740-video benchmark designed to evaluate whether multimodal large language models can issue timely, accurate safety warnings during the window between a visible danger sign and an accident. Videos span four domains (driving, healthcare, daily life, industrial production) and are annotated with frame-level risk onset and accident boundaries, requiring causal temporal reasoning rather than static scene classification. Testing 13 MLLMs reveals no model exceeds 20% on the strictest metric, with recall strongly coupled to false-positive rate (Pearson r=0.64), indicating models rely on scene-level activity cues rather than genuine hazard reasoning. Performance varies sharply by domain, with driving being particularly problematic due to visual similarity between routine and hazardous scenes.
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.
Self-improving VLMs can silently regress when verifier quality is task-mismatched
A new arXiv paper demonstrates that verifier-driven self-DPO, a common recipe for self-improving visual-language models, can silently degrade student model performance when the verifier's task-rubric accuracy is insufficient for the target task. Experiments on Qwen-3-VL-2B and Qwen-2.5-VL-3B across MathVista, MMMU, and BLINK show regressions of 3.4–10.9 percentage points below frozen baselines, with the counterintuitive finding that more accurate-but-still-wrong verifiers cause larger regressions than near-random ones. The authors provide a mechanistic explanation via a variance theorem for progress-gated replay and offer operational guidance: measure target-task rubric accuracy before running any verifier-driven loop and rank verifiers by task-specific quality rather than parameter count.