Pause and Think: A Dataset and Benchmark for Video-Grounded Assistive Action Suggestion

2026-05-30 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Expert, quick

Summary

The "Pause and Think" project introduces a new reasoning-centric training dataset, "pause-and-think-T", and a benchmark, "pause-and-think-B", to address Vision-Language Models' (VLMs) struggles with grounded reasoning, temporal consistency, and context-aware planning in videos. "pause-and-think-T" encourages models to reason over visual evidence before generating concise, actionable responses. A compact 4B-parameter model, fine-tuned on this dataset, achieved 58.0% accuracy on "pause-and-think-B", matching Qwen3-VL-235B (58.9%) with 59x fewer parameters, and surpassed GPT-4o on scene understanding. It also demonstrated strong out-of-distribution performance on EgoThink and TempCompass, showing gains in affordance, assistance, and temporal order, indicating that targeted reasoning supervision enables efficient, generalizable, visually grounded guidance without large model expansion.

Key takeaway

For Machine Learning Engineers developing video-grounded AI assistants, optimizing VLM performance and resource efficiency, you should explore reasoning-centric training datasets like "pause-and-think-T" to improve model accuracy and generalization. This approach allows compact 4B-parameter models to rival larger VLMs like Qwen3-VL-235B and GPT-4o, offering significant computational savings without sacrificing performance on complex video understanding tasks.

Key insights

Targeted reasoning supervision enables compact VLMs to achieve high accuracy and generalization in video-grounded assistive tasks.

Principles

• Structured reasoning improves VLM performance.
• Compact models can match larger ones with targeted training.
• Reasoning-centric datasets enhance out-of-distribution generalization.

Method

Fine-tuning a 4B-parameter VLM using the "pause-and-think-T" dataset, which promotes structured reasoning prior to answer generation for video-grounded action suggestions.

In practice

• Use "pause-and-think-T" for VLM fine-tuning.
• Evaluate VLMs on "pause-and-think-B" benchmark.
• Apply reasoning supervision for compact VLM deployment.

Topics

• Video-Language Models
• Assistive AI
• Dataset Benchmarking
• Grounded Reasoning
• Temporal Consistency
• Model Efficiency

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

Related on AIssential

• Pause and Think: A Dataset and Benchmark for Video-Grounded Assistive Action Suggestion — Targeted reasoning supervision enables compact models to deliver actionable, visually grounded guidance.
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• Visual Latents Know More Than They Say: Unsilencing Latent Reasoning in MLLMs — Visual latents in MLLMs can be "silenced" by training objectives, but their reasoning capacity can be unleashed at inference time.
• Children's Intelligence Tests Pose Challenges for MLLMs? KidGym: A 2D Grid-Based Reasoning Benchmark for MLLMs — KidGym evaluates MLLM cognitive abilities using a 2D grid-based benchmark inspired by child intelligence tests.
• MM-Conv: A Multimodal Dataset and Benchmark for Context-Aware Grounding in 3D Dialogue — Decoupling linguistic disambiguation from visual localization significantly improves multimodal grounding in dynamic dialogue.
• Stress Tests REVEAL Fragile Temporal and Visual Grounding in Video-Language Models — Current Video-Language Models exhibit fragile temporal and visual grounding, often relying on linguistic priors over actual video evidence.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.