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cs.SD2026

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…

cs.SD2026

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…

cs.SD2026

DuplexWorld: Can voice agents help you get through the day?

Aryan Vijay Bhosale, Harshit Rajgarhia, Akhil Pothanapalli +3

Speech-to-speech (S2S) voice agents are increasingly being incorporated into enterprise for customer care and as daily companions for consumers owing to the ease of the conversatio…

cs.SD2026

TORUS: A Test of Rendering-Understanding Self-Coherence for Unified Audio Models

Aryan Vijay Bhosale, Harshit Rajgarhia, Abhishek Mukherji +1

Unified audio models capable of audio understanding, audio generation and, increasingly, audio editing are proliferating rapidly. Yet a basic question about them remains unanswered…

cs.SD2026

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…

cs.SD2026

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…