GigaChat Audio: Time-aware Large Audio Language Model
Authors: name: Aleksandr Kutsakov, name: Mariia Sadovina, name: Georgii Gospodinov, name: Alexandr Maximenko, name: Oleg Kutuzov, name: Pavel Bogomolov, name: Fyodor Minkin
Organization: ai-sage

Temporal grounding in long recordings remains challenging for audio-conditioned LLMs. We present a time-aware audio LLM that answers questions with explicit timestamps over up to 120 minutes of input. Our approach interleaves periodic time markers with continuous audio tokens using large-scale synthetic supervision from a cascaded pipeline. Our model achieves strong temporal-grounding accuracy on short and long benchmarks and supports time-anchored fragment descriptions and summaries. Extensive ablations examine how time representation, marker frequency, tokenization, and duration-mixture design affect accuracy and computational cost. We release model weights and datasets to support further research on time-aware audio understanding, available at https://huggingface.co/ai-sage/GigaChat3.1-Audio-10B-A1.8B.