VoxSafeBench reveals that speech language models recognize social norms from text but fail to apply them when acoustic cues like speaker or scene determine the appropriate response.
Mmau-pro: A challenging and comprehensive benchmark for holistic evaluation of audio general intelligence
9 Pith papers cite this work. Polarity classification is still indexing.
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MedMosaic is a large-scale medical audio QA benchmark that shows even state-of-the-art models like Gemini-2.5-pro reach only about 68% accuracy on diverse clinical audio scenarios.
Semantic-level and verification-based uncertainty methods outperform token-level baselines for audio reasoning in ALLMs, but their relative performance on hallucination and unanswerable-question benchmarks is model- and task-dependent.
Audio-language models retain 60-72% of benchmark scores without audio, and most audio-dependent items can be solved from short fragments rather than full clips.
HeadRouter prunes audio tokens more effectively by dynamically routing based on per-head importance for semantic versus acoustic tasks, exceeding baseline performance at 70% token retention on Qwen2.5-Omni models.
A three-stage synthetic data pipeline generates 8800 doctor-patient conversations totaling 1.3k hours of audio and LLM-produced SOAP notes, with evaluation showing cascaded transcription-then-summarization models outperform end-to-end audio models.
Audio-Cogito is an open-source LALM using Cogito-pipe data curation and self-distillation to achieve leading open-source performance on audio reasoning benchmarks.
OmniJigsaw is a self-supervised proxy task that reconstructs shuffled audio-visual clips via joint integration, sample-level selection, and clip-level masking strategies, yielding gains on 15 video, audio, and reasoning benchmarks.
LLMs exhibit a persistent modality gap versus specialized audio encoders on MSEB tasks, with no conclusive evidence favoring audio-native over cascaded architectures.
citing papers explorer
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VoxSafeBench: Not Just What Is Said, but Who, How, and Where
VoxSafeBench reveals that speech language models recognize social norms from text but fail to apply them when acoustic cues like speaker or scene determine the appropriate response.
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MedMosaic: A Challenging Large Scale Benchmark of Diverse Medical Audio
MedMosaic is a large-scale medical audio QA benchmark that shows even state-of-the-art models like Gemini-2.5-pro reach only about 68% accuracy on diverse clinical audio scenarios.
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Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models
Semantic-level and verification-based uncertainty methods outperform token-level baselines for audio reasoning in ALLMs, but their relative performance on hallucination and unanswerable-question benchmarks is model- and task-dependent.
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All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluation
Audio-language models retain 60-72% of benchmark scores without audio, and most audio-dependent items can be solved from short fragments rather than full clips.
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HeadRouter: Dynamic Head-Weight Routing for Task-Adaptive Audio Token Pruning in Large Audio Language Models
HeadRouter prunes audio tokens more effectively by dynamically routing based on per-head importance for semantic versus acoustic tasks, exceeding baseline performance at 70% token retention on Qwen2.5-Omni models.
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Generating Synthetic Doctor-Patient Conversations for Long-form Audio Summarization
A three-stage synthetic data pipeline generates 8800 doctor-patient conversations totaling 1.3k hours of audio and LLM-produced SOAP notes, with evaluation showing cascaded transcription-then-summarization models outperform end-to-end audio models.
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Audio-Cogito: Towards Deep Audio Reasoning in Large Audio Language Models
Audio-Cogito is an open-source LALM using Cogito-pipe data curation and self-distillation to achieve leading open-source performance on audio reasoning benchmarks.
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OmniJigsaw: Enhancing Omni-Modal Reasoning via Modality-Orchestrated Reordering
OmniJigsaw is a self-supervised proxy task that reconstructs shuffled audio-visual clips via joint integration, sample-level selection, and clip-level masking strategies, yielding gains on 15 video, audio, and reasoning benchmarks.
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Benchmarking LLMs on the Massive Sound Embedding Benchmark (MSEB)
LLMs exhibit a persistent modality gap versus specialized audio encoders on MSEB tasks, with no conclusive evidence favoring audio-native over cascaded architectures.