12 papers
AVRT: Audio-Visual Reasoning Transfer through Single-Modality Teachers
Edson Araujo, Saurabhchand Bhati, M. Jehanzeb Mirza +5
Recent advances in reasoning models have shown remarkable progress in text-based domains, but transferring those capabilities to multimodal settings, e.g., to allow reasoning over…
TTA-Vid: Generalized Test-Time Adaptation for Video Reasoning
Soumya Shamarao Jahagirdar, Edson Araujo, Anna Kukleva +7
Recent video reasoning models have shown strong results on temporal and multimodal understanding, yet they depend on large-scale supervised data and multi-stage training pipelines,…
CALM: Class-Conditional Sparse Attention Vectors for Large Audio-Language Models
Videet Mehta, Liming Wang, Hilde Kuehne +3
Large audio-language models (LALMs) exhibit strong zero-shot capabilities in multiple downstream tasks, such as audio question answering (AQA) and abstract reasoning; however, thes…
TTRV: Test-Time Reinforcement Learning for Vision Language Models
Akshit Singh, Shyam Marjit, Wei Lin +7
Existing methods for extracting reward signals in Reinforcement Learning typically rely on labeled data and dedicated training splits, a setup that contrasts with how humans learn…
Towards Audio Token Compression in Large Audio Language Models
Saurabhchand Bhati, Samuel Thomas, Hilde Kuehne +2
Large Audio Language Models (LALMs) deliver strong performance across speech and audio tasks, but their audio encoders generate high-rate token sequences (e.g., 25 tokens/s), makin…
Omni-R1: Do You Really Need Audio to Fine-Tune Your Audio LLM?
Andrew Rouditchenko, Saurabhchand Bhati, Edson Araujo +4
We propose Omni-R1 which fine-tunes a recent multi-modal LLM, Qwen2.5-Omni, on an audio question answering dataset with the reinforcement learning method GRPO. This leads to new St…