# Everything Can Be Described in Words: A Simple Unified Multi-Modal Framework with Semantic and Temporal Alignment

Source: [Everything Can Be Described in Words: A Simple Unified Multi-Modal Framework with Semantic and Temporal Alignment](<https://www.sota2.com/research/paper/68fa793b1e209131cea7cbcc>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:54:52.375Z

Authors: Xiaowei Bi, Zheyuan Xu

Published: 2025-03-12T05:28:24.000Z

## Abstract

While multi-modal learning has advanced significantly, current approaches often create inconsistencies in representation and reasoning of different modalities. We propose UMaT, a theoretically-grounded framework that unifies visual and auditory inputs as structured text for large language models, addressing semantic alignment, temporal synchronization, and efficient sparse information retrieval. It significantly improves state-of-the-art Long Video Question Answering accuracy (up to 13.7%, and 16.9% on long videos) via redundancy minimization and structured textual representation for unified multi-modal reasoning

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2503.09081>)

[PDF](<https://arxiv.org/pdf/2503.09081.pdf>)