TPRU: Advancing Temporal and Procedural Understanding in Large Multimodal Models

2026-02-21

AI Summary

This work addresses a critical limitation of multimodal large language models—particularly compact, deployable variants—in comprehending temporal and procedural visual data, which hinders their application in embodied intelligence. To bridge this gap, the authors introduce TPRU, the first large-scale dataset systematically constructed for temporal reasoning, encompassing scenarios such as robotic manipulation and GUI navigation. They formulate three challenging tasks: temporal sequence reordering, next-frame prediction, and previous-frame retrodiction, augmented with hard negative samples to encourage cross-modal active verification. Leveraging reinforcement learning fine-tuning, the proposed TPRU-7B model achieves a substantial improvement on the TPRU-Test benchmark, raising accuracy from 50.33% to 75.70%, outperforming larger models like GPT-4o and demonstrating strong generalization across multiple evaluation benchmarks.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Multimodal Large Language Models (MLLMs), particularly smaller, deployable variants, exhibit a critical deficiency in understanding temporal and procedural visual data, a bottleneck hindering their application in real-world embodied AI. This gap is largely caused by a systemic failure in training paradigms, which lack large-scale, procedurally coherent data. To address this problem, we introduce TPRU, a large-scale dataset sourced from diverse embodied scenarios such as robotic manipulation and GUI navigation. TPRU is systematically designed to cultivate temporal reasoning through three complementary tasks: Temporal Reordering, Next-Frame Prediction, and Previous-Frame Review. A key feature is the inclusion of challenging negative samples, compelling models to transition from passive observation to active, cross-modal validation. We leverage TPRU with a reinforcement learning (RL) fine-tuning methodology, specifically targeting the enhancement of resource-efficient models. Experiments show our approach yields dramatic gains: on our manually curated TPRU-Test, the accuracy of TPRU-7B soars from 50.33\% to 75.70\%, a state-of-the-art result that significantly outperforms vastly larger baselines, including GPT-4o. Crucially, these capabilities generalize effectively, demonstrating substantial improvements on established benchmarks. The codebase is available at https://github.com/Stephen-gzk/TPRU/ .

Problem

• Temporal Understanding
• Procedural Understanding
• Multimodal Large Language Models
• Embodied AI
• Visual Temporal Reasoning

Innovation

• Temporal Reasoning
• Procedural Understanding
• Multimodal Large Language Models
• Reinforcement Learning Fine-tuning
• Negative Sampling