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[2602.18884] TPRU: Advancing Temporal and Procedural Understanding in Large Multimodal Models
Summary
The paper introduces TPRU, a dataset aimed at improving temporal and procedural understanding in Multimodal Large Language Models (MLLMs), addressing a critical gap in their application for embodied AI.
Why It Matters
As MLLMs become integral to real-world applications, enhancing their ability to understand temporal and procedural data is crucial. TPRU addresses this by providing a robust dataset that enables better training and performance of these models, potentially advancing AI capabilities in robotics and other fields.
Key Takeaways
- TPRU dataset enhances MLLMs' understanding of temporal and procedural data.
- The dataset includes challenging tasks that improve model performance.
- Significant accuracy improvements were observed in experiments with TPRU-7B.
Computer Science > Artificial Intelligence arXiv:2602.18884 (cs) [Submitted on 21 Feb 2026] Title:TPRU: Advancing Temporal and Procedural Understanding in Large Multimodal Models Authors:Zhenkun Gao, Xuhong Wang, Xin Tan, Yuan Xie View a PDF of the paper titled TPRU: Advancing Temporal and Procedural Understanding in Large Multimodal Models, by Zhenkun Gao and 3 other authors View PDF HTML (experimental) 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 ...
