21 papers
Latent Implicit Visual Reasoning
Kelvin Li, Chuyi Shang, Leonid Karlinsky +3
While Large Multimodal Models (LMMs) have made significant progress, they remain largely text-centric, relying on language as their core reasoning modality. As a result, they are l…
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…
PRISMM-Bench: A Benchmark of Peer-Review Grounded Multimodal Inconsistencies
Lukas Selch, Yufang Hou, M. Jehanzeb Mirza +4
Large Multimodal Models (LMMs) are increasingly applied to scientific research, yet it remains unclear whether they can reliably understand and reason over the multimodal complexit…
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…