# QTrack: Query-Driven Reasoning for Multi-modal MOT

arXiv: https://arxiv.org/abs/2603.13759 · HTML: https://mlsanity.com/paper/2603.13759/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 21 of its 78 references that are in the map.
- First posted on arXiv 14 Mar 2026.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 21 of 21)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Instruction-Following Evaluation for Large Language Models](https://mlsanity.com/paper/2311.07911/) (2023)
- [OpenAI GPT-5 System Card](https://mlsanity.com/paper/2601.03267/) (2025)
- [Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs](https://mlsanity.com/paper/2406.16860/) (2024)
- [DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding](https://mlsanity.com/paper/2412.10302/) (2024)
- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [MOT20: A benchmark for multi object tracking in crowded scenes](https://mlsanity.com/paper/2003.09003/) (2020)
- [Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement](https://mlsanity.com/paper/2503.06520/) (2025)
- [MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking](https://mlsanity.com/paper/1504.01942/) (2015)
- [SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes](https://mlsanity.com/paper/2304.05170/) (2023)
- [Ministral 3](https://mlsanity.com/paper/2601.08584/) (2026)
- [Perception-Aware Policy Optimization for Multimodal Reasoning](https://mlsanity.com/paper/2507.06448/) (2025)
- [VisionReasoner: Unified Reasoning-Integrated Visual Perception via Reinforcement Learning](https://mlsanity.com/paper/2505.12081/) (2025)
- [Open Vision Reasoner: Transferring Linguistic Cognitive Behavior for Visual Reasoning](https://mlsanity.com/paper/2507.05255/) (2025)
- [Tracking Different Ant Species: An Unsupervised Domain Adaptation Framework and a Dataset for Multi-object Tracking](https://mlsanity.com/paper/2301.10559/) (2023)

## Abstract (arXiv)

Category-level multi-object trackers estimate trajectories for all detected instances, whereas some applications require propagating only targets specified by a user. We study offline, reference-grounded video tracking: given an ordered video clip, a natural-language query, and the reference-frame boxes of the specified targets, a model predicts their subsequent trajectories. We introduce QTrack, a vision-language policy trained with structured trajectory rewards and Temporal Perception-Aware Policy Optimization (TAPO). TAPO contrasts the likelihood of a generated response under the observed clip and a reference-frame repetition, encouraging the output to depend on visual evidence beyond the reference frame. We also introduce RMOT26, a controlled diagnostic benchmark constructed from existing tracking datasets. Under the reported protocol, QTrack obtains a motion- consistency score of 0.30, MOTP of 0.75, center-location error of 44.61 pixels, and normalized distance error of 0.39; its motion-consistency score ties the strongest comparison method. Component ablations suggest complementary effects from the motion reward and temporal contrast, while an exploratory four- clip, 50-frame evaluation examines transfer beyond the primary short-clip setting. The results support further study of temporal-evidence learning in trajectory- generating vision-language models. Code and data are available at https://github.com/gaash-lab/QTrack