# X-CoT: Explainable Text-to-Video Retrieval via LLM-based Chain-of-Thought Reasoning

arXiv: https://arxiv.org/abs/2509.21559 · HTML: https://mlsanity.com/paper/2509.21559/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 28 of its 40 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Placed in

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

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://mlsanity.com/paper/2311.10122/) (2023)
- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [Localizing Moments in Video with Natural Language](https://mlsanity.com/paper/1708.01641/) (2017)
- [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://mlsanity.com/paper/1911.03429/) (2019)
- [MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action](https://mlsanity.com/paper/2303.11381/) (2023)
- [LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment](https://mlsanity.com/paper/2310.01852/) (2023)
- [X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval](https://mlsanity.com/paper/2207.07285/) (2022)
- [VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks](https://mlsanity.com/paper/2410.05160/) (2024)
- [Unmasked Teacher: Towards Training-Efficient Video Foundation Models](https://mlsanity.com/paper/2303.16058/) (2023)
- [A Dataset for Movie Description](https://mlsanity.com/paper/1501.02530/) (2015)
- [Align and Prompt: Video-and-Language Pre-training with Entity Prompts](https://mlsanity.com/paper/2112.09583/) (2021)
- [OmniVL:One Foundation Model for Image-Language and Video-Language Tasks](https://mlsanity.com/paper/2209.07526/) (2022)
- [X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval](https://mlsanity.com/paper/2203.15086/) (2022)
- [LamRA: Large Multimodal Model as Your Advanced Retrieval Assistant](https://mlsanity.com/paper/2412.01720/) (2024)
- [HiTeA: Hierarchical Temporal-Aware Video-Language Pre-training](https://mlsanity.com/paper/2212.14546/) (2022)
- [VideoRAG: Retrieval-Augmented Generation over Video Corpus](https://mlsanity.com/paper/2501.05874/) (2025)
- [BT-Adapter: Video Conversation is Feasible Without Video Instruction Tuning](https://mlsanity.com/paper/2309.15785/) (2023)
- [HowToCaption: Prompting LLMs to Transform Video Annotations at Scale](https://mlsanity.com/paper/2310.04900/) (2023)
- [Text Is MASS: Modeling as Stochastic Embedding for Text-Video Retrieval](https://mlsanity.com/paper/2403.17998/) (2024)
- [MILES: Visual BERT Pre-training with Injected Language Semantics for Video-text Retrieval](https://mlsanity.com/paper/2204.12408/) (2022)
- [Embodied VideoAgent: Persistent Memory from Egocentric Videos and Embodied Sensors Enables Dynamic Scene Understanding](https://mlsanity.com/paper/2501.00358/) (2024)
- [STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical Question-Answering](https://mlsanity.com/paper/2406.19973/) (2024)
- [SQ-LLaVA: Self-Questioning for Large Vision-Language Assistant](https://mlsanity.com/paper/2403.11299/) (2024)
- [TVTSv2: Learning Out-of-the-box Spatiotemporal Visual Representations at Scale](https://mlsanity.com/paper/2305.14173/) (2023)

## Built on by (most cited first, 2 of 2)

- [Prompting-MammAlps: Fine-Grained Text-to-Video Retrieval for Camera-Trap Data](https://mlsanity.com/paper/2607.09876/) (2026)
- [TraVEL: Trajectory-Guided Video Embedding Learning for Driving-Video Retrieval](https://mlsanity.com/paper/2608.13495/) (2026)

## Abstract (arXiv)

Prevalent text-to-video retrieval systems mainly adopt embedding models for feature extraction and compute cosine similarities for ranking. However, this design presents two limitations. Low-quality text-video data pairs could compromise the retrieval, yet are hard to identify and examine. Cosine similarity alone provides no explanation for the ranking results, limiting the interpretability. We ask that can we interpret the ranking results, so as to assess the retrieval models and examine the text-video data? This work proposes X-CoT, an explainable retrieval framework upon LLM CoT reasoning in place of the embedding model-based similarity ranking. We first expand the existing benchmarks with additional video annotations to support semantic understanding and reduce data bias. We also devise a retrieval CoT consisting of pairwise comparison steps, yielding detailed reasoning and complete ranking. X-CoT empirically improves the retrieval performance and produces detailed rationales. It also facilitates the model behavior and data quality analysis. Code and data are available at: https://github.com/PrasannaPulakurthi/X-CoT.