# LazyVLM: Neuro-Symbolic Approach to Video Analytics

arXiv: https://arxiv.org/abs/2505.21459 · HTML: https://mlsanity.com/paper/2505.21459/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 10 of 10)

- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [MOT20: A benchmark for multi object tracking in crowded scenes](https://mlsanity.com/paper/2003.09003/) (2020)
- [Improving Text Embeddings with Large Language Models](https://mlsanity.com/paper/2401.00368/) (2023)
- [VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks](https://mlsanity.com/paper/2410.05160/) (2024)
- [TAO: A Large-Scale Benchmark for Tracking Any Object](https://mlsanity.com/paper/2005.10356/) (2020)
- [Fine-Grained Scene Graph Generation with Data Transfer](https://mlsanity.com/paper/2203.11654/) (2022)
- [BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics](https://mlsanity.com/paper/1805.01046/) (2018)
- [EQUI-VOCAL: Synthesizing Queries for Compositional Video Events from Limited User Interactions [Technical Report]](https://mlsanity.com/paper/2301.00929/) (2023)
- [Zelda: Video Analytics using Vision-Language Models](https://mlsanity.com/paper/2305.03785/) (2023)
- [VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building [Technical Report]](https://mlsanity.com/paper/2303.04068/) (2023)

## Built on by (most cited first, 1 of 1)

- [FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios](https://mlsanity.com/paper/2604.07413/) (2026)

## Abstract (arXiv)

Current video analytics approaches face a fundamental trade-off between flexibility and efficiency. End-to-end Vision Language Models (VLMs) often struggle with long-context processing and incur high computational costs, while neural-symbolic methods depend heavily on manual labeling and rigid rule design. In this paper, we introduce LazyVLM, a neuro-symbolic video analytics system that provides a user-friendly query interface similar to VLMs, while addressing their scalability limitation. LazyVLM enables users to effortlessly drop in video data and specify complex multi-frame video queries using a semi-structured text interface for video analytics. To address the scalability limitations of VLMs, LazyVLM decomposes multi-frame video queries into fine-grained operations and offloads the bulk of the processing to efficient relational query execution and vector similarity search. We demonstrate that LazyVLM provides a robust, efficient, and user-friendly solution for querying open-domain video data at scale.