4 citations · 17 across the 34 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
Activation Reward Models for Few-Shot Model Alignment
Tianning Chai, Chancharik Mitra, Brandon Huang +8
Aligning Large Language Models (LLMs) and Large Multimodal Models (LMMs) to human preferences is a central challenge in improving the quality of the models' generative outputs for…
ChartGen: Scaling Chart Understanding Via Code-Guided Synthetic Chart Generation
Jovana Kondic, Pengyuan Li, Dhiraj Joshi +12
Chart-to-code reconstruction -- the task of recovering executable plotting scripts from chart images -- provides important insights into a model's ability to ground data visualizat…
Instructify: Demystifying Metadata to Visual Instruction Tuning Data Conversion
Jacob Hansen, Wei Lin, Junmo Kang +6
Visual Instruction Tuning (VisIT) data, commonly available as human-assistant conversations with images interleaved in the human turns, are currently the most widespread vehicle fo…
CAV-MAE Sync: Improving Contrastive Audio-Visual Mask Autoencoders via Fine-Grained Alignment
Edson Araujo, Andrew Rouditchenko, Yuan Gong +7
Recent advances in audio-visual learning have shown promising results in learning representations across modalities. However, most approaches rely on global audio representations t…
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…
Visualizing Thought: Conceptual Diagrams Enable Robust Planning in LMMs
Nasim Borazjanizadeh, Roei Herzig, Eduard Oks +3
Human reasoning relies on constructing and manipulating mental models -- simplified internal representations of situations used to understand and solve problems. Conceptual diagram…
Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence
Granite Vision Team, Leonid Karlinsky, Assaf Arbelle +60
We introduce Granite Vision, a lightweight large language model with vision capabilities, specifically designed to excel in enterprise use cases, particularly in visual document un…
mWhisper-Flamingo for Multilingual Audio-Visual Noise-Robust Speech Recognition
Andrew Rouditchenko, Samuel Thomas, Hilde Kuehne +2
Audio-Visual Speech Recognition (AVSR) combines lip-based video with audio and can improve performance in noise, but most methods are trained only on English data. One limitation i…
M+: Extending MemoryLLM with Scalable Long-Term Memory
Yu Wang, Dmitry Krotov, Yuanzhe Hu +6
Equipping large language models (LLMs) with latent-space memory has attracted increasing attention as they can extend the context window of existing language models. However, retai…