7 papers · 1 filter
TEMPO: Temporally-grounded Multi-task Post-training for Large Audio-Language Models
Apoorva Kulkarni, Kaousheik Jayakumar, Sreyan Ghosh +3
Large audio-language models (LALMs) describe audio at the clip level but cannot assign timestamps to the events, speakers, or sounds they identify. Despite being essential for down…
Audio-Visual Flamingo: Open Audio-Visual Intelligence for Long and Complex Videos
Sreyan Ghosh, Arushi Goel, Kaousheik Jayakumar +19
We present Audio-Visual Flamingo (AV-Flamingo), a fully open state-of-the-art audio-visual large language model (AV-LLM) for joint understanding and reasoning over audio, images, a…
A Closer Look at Failure Modes in Temporal Understanding of Large Audio-Language Models
Apoorva Kulkarni, Kaousheik Jayakumar, Sreyan Ghosh +3
Large Audio Language Models (LALMs) achieve strong performance on a variety of audio understanding tasks but continue to struggle with temporal reasoning, a fundamental capability…
Audio Flamingo Next: Next-Generation Open Audio-Language Models for Speech, Sound, and Music
Sreyan Ghosh, Arushi Goel, Kaousheik Jayakumar +15
We present Audio Flamingo Next (AF-Next), the next-generation and most capable large audio-language model in the Audio Flamingo series, designed to advance understanding and reason…
Do Audio-Visual Large Language Models Really See and Hear?
Ramaneswaran Selvakumar, Kaousheik Jayakumar, S Sakshi +3
Audio-Visual Large Language Models (AVLLMs) are emerging as unified interfaces to multimodal perception. We present the first mechanistic interpretability study of AVLLMs, analyzin…
MMOU: A Massive Multi-Task Omni Understanding and Reasoning Benchmark for Long and Complex Real-World Videos
Arushi Goel, Sreyan Ghosh, Vatsal Agarwal +16
Multimodal Large Language Models (MLLMs) have shown strong performance in visual and audio understanding when evaluated in isolation. However, their ability to jointly reason over…
Sparks of Cooperative Reasoning: LLMs as Strategic Hanabi Agents
Mahesh Ramesh, Kaousheik Jayakumar, Aswinkumar Ramkumar +3
Cooperative reasoning under incomplete information remains challenging for both humans and multi-agent systems. The card game Hanabi embodies this challenge, requiring theory-of-mi…