# Listening with Time: Precise Temporal Awareness for Long-Form Audio Understanding arXiv: https://arxiv.org/abs/2604.22245 ยท HTML: https://mlsanity.com/paper/2604.22245/ ## Key facts - Cited by 5 papers in the map (data through 19 Oct 2026). - Placed in Audio language models and spoken dialogue, one of 11 directions in Speech and audio language models. - Builds on 26 of its 36 references that are in the map. - First posted on arXiv 24 Apr 2026. ## Placed in - Speech and audio language models / [Audio language models and spoken dialogue](https://mlsanity.com/speech/audio-language-models-and-spoken-dialogue/) ## Builds on (most cited first, 26 of 26) - [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024) - [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023) - [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025) - [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024) - [Dense-Captioning Events in Videos](https://mlsanity.com/paper/1705.00754/) (2017) - [SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning](https://mlsanity.com/paper/2408.05517/) (2024) - [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023) - [Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models](https://mlsanity.com/paper/2507.08128/) (2025) - [Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers](https://mlsanity.com/paper/2506.23918/) (2025) - [MiMo-Audio: Audio Language Models are Few-Shot Learners](https://mlsanity.com/paper/2512.23808/) (2025) - [The Benefit Of Temporally-Strong Labels In Audio Event Classification](https://mlsanity.com/paper/2105.07031/) (2021) - [Thinking With Videos: Multimodal Tool-Augmented Reinforcement Learning for Long Video Reasoning](https://mlsanity.com/paper/2508.04416/) (2025) - [LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling](https://mlsanity.com/paper/2511.20785/) (2025) - [Can Audio Captions Be Evaluated with Image Caption Metrics?](https://mlsanity.com/paper/2110.04684/) (2021) - [AudioMarathon: A Comprehensive Benchmark for Long-Context Audio Understanding and Efficiency in Audio LLMs](https://mlsanity.com/paper/2510.07293/) (2025) - [AudioTime: A Temporally-aligned Audio-text Benchmark Dataset](https://mlsanity.com/paper/2407.02857/) (2024) - [TACOS: Temporally-aligned Audio CaptiOnS for Language-Audio Pretraining](https://mlsanity.com/paper/2505.07609/) (2025) - [TagSpeech: End-to-End Multi-Speaker ASR and Diarization with Fine-Grained Temporal Grounding](https://mlsanity.com/paper/2601.06896/) (2026) - [LLM-ForcedAligner: A Non-Autoregressive and Accurate LLM-Based Forced Aligner for Multilingual and Long-Form Speech](https://mlsanity.com/paper/2601.18220/) (2026) - [Listening Between the Frames: Bridging Temporal Gaps in Large Audio-Language Models](https://mlsanity.com/paper/2511.11039/) (2025) - [LongSpeech: A Scalable Benchmark for Transcription, Translation and Understanding in Long Speech](https://mlsanity.com/paper/2601.13539/) (2026) - [Enhancing Temporal Understanding in Audio Question Answering for Large Audio Language Models](https://mlsanity.com/paper/2409.06223/) (2024) - [ActivityNet Challenge 2017 Summary](https://mlsanity.com/paper/1710.08011/) (2017) - [CoLLAP: Contrastive Long-form Language-Audio Pretraining with Musical Temporal Structure Augmentation](https://mlsanity.com/paper/2410.02271/) (2024) - [Speech-XL: Towards Long-Form Speech Understanding in Large Speech Language Models](https://mlsanity.com/paper/2602.05373/) (2026) - [FastSLM: Hierarchical Temporal Abstraction for Efficient Long-Form Speech Adaptation](https://mlsanity.com/paper/2601.06199/) (2026) ## Built on by (most cited first, 5 of 5) - [Audio-Mind: An Auditable Agentic Framework for Audio Understanding](https://mlsanity.com/paper/2605.28480/) (2026) - [LongAudioSpan: Spanning the Duration and Depth of Audio Comprehension](https://mlsanity.com/paper/2608.26431/) (2026) - [TAG-Bench: Benchmarking Temporal Audio Grounding in Large Audio Language Models](https://mlsanity.com/paper/2609.01542/) (2026) - [LEAP: Learned Block-wise Evidence Retrieval for Long Audio-Video Perception](https://mlsanity.com/paper/2609.39938/) (2026) - [RefTAG: Reflection-aware Temporal Audio Grounding in Large Audio Language Models](https://mlsanity.com/paper/2610.16217/) (2026) ## Abstract (arXiv) While Large Audio Language Models (LALMs) achieve strong performance on short audio, they degrade on long-form inputs. This degradation is more severe in temporal awareness tasks, where temporal alignment becomes increasingly inaccurate as audio duration grows. We attribute these limitations to the lack of data, benchmarks, and modeling approaches tailored for long-form temporal awareness. To bridge this gap, we first construct LAT-Chronicle, a 1.2k hour long-form audio dataset with temporal annotations across real-world scenarios. We further develop LAT-Bench, the first human-verified benchmark supporting audio up to 30 minutes while covering three core tasks: Dense Audio Caption, Temporal Audio Grounding, and Targeted Audio Caption. Leveraging these resources, we propose LAT-Audio, formulating temporal awareness as a progressive global-to-local reasoning paradigm. A global timeline is first constructed as an aligned temporal-semantic context,and the Think-With-Audio Chain-of-Thought (TWA-CoT) is then introduced to perform iterative reasoning by incorporating local audio information via tool use. Experiments show that LAT-Audio surpasses existing models on long-form audio temporal awareness tasks and improves robustness to input duration. We release the dataset, benchmark, and model to facilitate future research at https://github.com/alanshaoTT/LAT-Audio-Repo.