EgoSound is a new benchmark with 7315 QA pairs across seven tasks to evaluate egocentric sound understanding in multimodal large language models.
Omni-captioner: Data pipeline, mod- els, and benchmark for omni detailed perception
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Bagpiper-TTS uses natural language prompts and intent reasoning to derive rich captions that guide a single model for universal speech synthesis across classical TTS, multi-talker, singing, and role-play tasks.
MTSS replaces monolithic video captions with factorized streams and relational grounding, yielding reported gains in understanding benchmarks and generation consistency.
FCaps supplies 19M fine-grained speech style captions on 47k hours of audio via direct grounding, enabling the CLSP model to produce multi-granular representations that improve retrieval, zero-shot classification, and style scoring aligned with human judgments.
Bagpiper-Edit performs zero-shot open-ended audio editing by translating natural-language instructions into edited rich captions that guide generation anchored to the original audio.
A data pipeline, 14-dimension benchmark, and decoupled fine-tuning model are presented to advance fine-grained multi-dimensional speech understanding in LLMs.
CHAI framework pairs AI pre-captions with expert human critiques to produce precise video descriptions, enabling open models to outperform closed ones like Gemini-3.1-Pro and improve fine-grained control in video generation models.
CogniRoute adds a cognitive schema and route-aware RL to an omni-modal MoE, reaching 59.38% accuracy on a new 118K-example social video QA benchmark and beating prior baselines by 15-27 points.
TCA-Captioner introduces an Observer-Checker-Corrector refinement loop and TCA-Bench to address modality detachment and temporal incoherence in audiovisual video captioning.
PASK introduces the DD-MM-PAS paradigm for streaming proactive agents with intent-aware detection, hybrid memory modeling, and a new real-world benchmark where the IntentFlow model matches top LLMs on latency while finding deeper intents.
VISA ranks 2nd in the Interspeech 2026 ARC Agent Track by adding multi-modal feature extraction, consistency-checked model voting, and rubric-aligned routing to large audio language models, reaching 66.23% Rubrics score and 77.40% accuracy.
A survey that provides a unified formulation of audio reasoning and reviews advances across Audio-to-Text, Audio-to-Speech, Audio-Visual, and Agentic paradigms while discussing challenges and future directions.
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