19 citations · 137 across the 64 of their papers we have counts for
72 papers
Bounded-Density Vertex Deletion Beyond Vertex Cover
Adrian Miclăuş, Alexandru Popa, Andrei Popa
We study \textsc{Bounded-Density Vertex Deletion} (BDVD), where one asks whether at most \(k\) vertices can be deleted so that every remaining subgraph has density at most a ration…
SEA-LM: Egocentric Spatial Audio Understanding for Wearable Microphone Arrays
Sonal Kumar, Sinan Hersek, Artem Dementyev +6
Embodied, ego-centric intelligence fundamentally requires the ability to comprehend spatial audio within complex environments. While large audio-language models excel at mono-chann…
What Should World Models Forget? Stratified Retention for Continual Adaptation
Nishit Anand, Ramani Duraiswami, Dinesh Manocha
Continual learning treats degradation on previously seen data as evidence of failure, a convention inherited from settings with a stationary prediction target, where a correct labe…
ParA-LLM: A Unified Approach to Paralinguistic and Acoustic Speech Understanding
Nishit Anand, Jiaqi Su, Ke Chen +5
Recent advances in Audio LLMs have achieved human-level speech recognition, yet existing systems struggle to capture paralinguistic aspects such as speaker traits, expressive varia…
Self-similar swirl between contracting porous walls: the GD1998 exact Navier-Stokes solution revisited in the similarity variables of the OpenAI 2026 forced blow-up construction
Ramani Duraiswami
The finite-time blow-up construction for the forced Navier-Stokes equations released by OpenAI in Sep. 2026 (OpenAI 2026) has as its object an axisymmetric swirl core in anisotropi…
SteerDuplex: Steerable Duplex Speech Dialogue Models
Utkarsh Tyagi, Ramaneswaran Selvakumar, Advait Gosai +13
Full-duplex spoken dialogue models support low-latency turn taking, interruption handling, and backchanneling, yet a key capability remains underexplored: steerability, the ability…
VIBE: Video Instruction-aligned Background music gEneration
Aryan Vijay Bhosale, Vaibhavi Lokegaonkar, Vishnu Raj +5
Current video-to-music (V2M) models lack semantic control and fail to penalize instruction violations, largely due to their reliance on reconstruction objectives and the representa…
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…
Rubrics as Visual-Repair Context for Self-Evolving UI-to-Code Generation
Tianyi Xiong, Zhengyuan Yang, Xiaofei Wang +10
Large vision-language models have shown strong progress in UI-to-code generation, yet their test-time self-evolution remains unstable. We first identify a fundamental obstacle, ter…
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…
GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems
Meenakshi Krishnan, Pranav Pulijala, Ke Chen +2
Operator learning for partial differential equations (PDEs) on arbitrary geometries builds fast neural surrogates for large-scale simulation. Although recent geometry-adaptive neur…
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…
FIGMA: Towards FIne-Grained Music retrievAl
Nishit Anand, Ashish Seth, Sreyan Ghosh +2
Retrieving music using natural language descriptions has improved with contrastive audio-text models such as CLAP, but current systems remain limited to coarse semantic queries. Wh…
Learning Illumination Control in Diffusion Models
Nishit Anand, Manan Suri, Christopher Metzler +2
Controlling illumination in images is essential for photography and visual content creation. While closed-source models have demonstrated impressive illumination control, open-sour…
Video-Robin: Autoregressive Diffusion Planning for Intent-Grounded Video-to-Music Generation
Vaibhavi Lokegaonkar, Aryan Vijay Bhosale, Vishnu Raj +5
Video-to-music (V2M) is the fundamental task of creating background music for an input video. Recent V2M models achieve audiovisual alignment by typically relying on visual conditi…
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…
On The Application of Linear Attention in Multimodal Transformers
Armin Gerami, Seyedehanita Madani, Ramani Duraiswami
Multimodal Transformers serve as the backbone for state-of-the-art vision-language models, yet their quadratic attention complexity remains a critical barrier to scalability. In th…
Audio Hallucination Attacks: Probing the Reliability of Large Audio Language Models
Ashish Seth, Sonal Kumar, Ramaneswaran Selvakumar +5
Large Audio Language Models (LALMs) achieve strong performance on audio-language tasks; however, their reliability in real-world settings remains underexplored. We introduce Audio…
Analytical Exploration of Spatial Audio Cues: A Differentiable Multi-Sphere Scattering Model
Siminfar Samakoush Galougah, Pranav Pulijala, Ramani Duraiswami
A primary challenge in developing synthetic spatial hearing systems, particularly underwater, is accurately modeling sound scattering. Biological organisms achieve 3D spatial heari…
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…
Quantifying Document Impact in RAG-LLMs
Armin Gerami, Kazem Faghih, Ramani Duraiswami
Retrieval Augmented Generation (RAG) enhances Large Language Models (LLMs) by connecting them to external knowledge, improving accuracy and reducing outdated information. However,…
ORCA: Open-ended Response Correctness Assessment for Audio Question Answering
Šimon Sedláček, Sara Barahona, Bolaji Yusuf +9
Reliable assessment of the abilities of large audio language models (LALMs) is essential to advancing the state of the art. As benchmarks rapidly evolve to incorporate complex reas…
Auditing Algorithmic Bias in Transformer-Based Trading
Armin Gerami, Ramani Duraiswami
Transformer models have become increasingly popular in financial applications, yet their potential risk making and biases remain under-explored. The purpose of this work is to audi…
Music Flamingo: Scaling Music Understanding in Audio Language Models
Sreyan Ghosh, Arushi Goel, Lasha Koroshinadze +8
We introduce Music Flamingo, a novel large audio-language model designed to advance music (including song) understanding in foundational audio models. While audio-language research…