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
9 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 9representative citing papers
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.
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.
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.
citing papers explorer
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EgoSound: Benchmarking Sound Understanding in Egocentric Videos
EgoSound is a new benchmark with 7315 QA pairs across seven tasks to evaluate egocentric sound understanding in multimodal large language models.
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Script-a-Video: Deep Structured Audio-visual Captions via Factorized Streams and Relational Grounding
MTSS replaces monolithic video captions with factorized streams and relational grounding, yielding reported gains in understanding benchmarks and generation consistency.
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Towards Fine-Grained and Multi-Granular Contrastive Language-Speech Pre-training
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.
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Towards Fine-Grained Multi-Dimensional Speech Understanding: Data Pipeline, Benchmark, and Model
A data pipeline, 14-dimension benchmark, and decoupled fine-tuning model are presented to advance fine-grained multi-dimensional speech understanding in LLMs.
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Building a Precise Video Language with Human-AI Oversight
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.
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Temporal and Cross-Modal Alignment for Enhanced Audiovisual Video Captioning
TCA-Captioner introduces an Observer-Checker-Corrector refinement loop and TCA-Bench to address modality detachment and temporal incoherence in audiovisual video captioning.
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PASK: Toward Intent-Aware Proactive Agents with Long-Term Memory
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.
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VISA: A Visual Information Strengthened Audio-Reasoning System for the Interspeech 2026 ARC Agent Track
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.
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A Survey of Audio Reasoning in Multimodal Foundation Models
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.