# Listening Between the Frames: Bridging Temporal Gaps in Large Audio-Language Models

arXiv: https://arxiv.org/abs/2511.11039 · HTML: https://mlsanity.com/paper/2511.11039/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2025.

## Built on by (most cited first, 8 of 8)

- [TiCo: Time-Controllable Spoken Dialogue Model](https://mlsanity.com/paper/2603.22267/) (2026)
- [Towards Fine-grained Temporal Perception: Post-Training Large Audio-Language Models with Audio-Side Time Prompt](https://mlsanity.com/paper/2604.13715/) (2026)
- [SPEARBench: A Benchmark for Naturalness Evaluation in Streaming Speech-to-Speech Language Models](https://mlsanity.com/paper/2607.05365/) (2026)
- [MuLA-Bench: A Multilingual Long-Form Audio Understanding Benchmark via Multi-Tier Auditing](https://mlsanity.com/paper/2609.23416/) (2026)
- [SpectCount: Spectrotemporal Counting via Synthetic Signals Improves Large Audio Language Models](https://mlsanity.com/paper/2606.06907/) (2026)
- [Auto-AEG: Scalable Data Construction for Open-Vocabulary Audio Event Grounding](https://mlsanity.com/paper/2607.04383/) (2026)
- [GigaChat Audio: Time-aware Large Audio Language Model](https://mlsanity.com/paper/2607.10387/) (2026)
- [Augmenting Large Audio-Language Models with Frame-Level Grounding for Fine-Grained Temporal Perception](https://mlsanity.com/paper/2609.15215/) (2026)

## Abstract (arXiv)

Recent Large Audio-Language Models (LALMs) exhibit impressive capabilities in understanding audio content for conversational QA tasks. However, these models struggle to accurately understand timestamps for temporal localization (e.g., Temporal Audio Grounding) and are restricted to short audio perception, leading to constrained capabilities on fine-grained tasks. We identify three key aspects that limit their temporal localization and long audio understanding: (i) timestamp representation, (ii) architecture, and (iii) data. To address this, we introduce TimeAudio, a novel method that empowers LALMs to connect their understanding of audio content with precise temporal perception. Specifically, we incorporate unique temporal markers to improve time-sensitive reasoning and apply an absolute time-aware encoding that explicitly grounds the acoustic features with absolute time information. Moreover, to achieve end-to-end long audio understanding, we introduce a segment-level token merging module to substantially reduce audio token redundancy and enhance the efficiency of information extraction. Due to the lack of suitable datasets and evaluation metrics, we consolidate existing audio datasets into a new dataset focused on temporal tasks and establish a series of metrics to evaluate the fine-grained performance. Evaluations show strong performance across a variety of fine-grained tasks, such as dense captioning, temporal grounding, and timeline speech summarization, demonstrating TimeAudio's robust temporal localization and reasoning capabilities.