ViMU is the first benchmark for evaluating video models on metaphorical and subtextual understanding using hint-free questions grounded in multimodal evidence.
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Mmar: A challenging benchmark for deep reasoning in speech, audio, music, and their mix
Baseline reference. 67% of citing Pith papers use this work as a benchmark or comparison.
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abstract
We introduce MMAR, a new benchmark designed to evaluate the deep reasoning capabilities of Audio-Language Models (ALMs) across massive multi-disciplinary tasks. MMAR comprises 1,000 meticulously curated audio-question-answer triplets, collected from real-world internet videos and refined through iterative error corrections and quality checks to ensure high quality. Unlike existing benchmarks that are limited to specific domains of sound, music, or speech, MMAR extends them to a broad spectrum of real-world audio scenarios, including mixed-modality combinations of sound, music, and speech. Each question in MMAR is hierarchically categorized across four reasoning layers: Signal, Perception, Semantic, and Cultural, with additional sub-categories within each layer to reflect task diversity and complexity. To further foster research in this area, we annotate every question with a Chain-of-Thought (CoT) rationale to promote future advancements in audio reasoning. Each item in the benchmark demands multi-step deep reasoning beyond surface-level understanding. Moreover, a part of the questions requires graduate-level perceptual and domain-specific knowledge, elevating the benchmark's difficulty and depth. We evaluate MMAR using a broad set of models, including Large Audio-Language Models (LALMs), Large Audio Reasoning Models (LARMs), Omni Language Models (OLMs), Large Language Models (LLMs), and Large Reasoning Models (LRMs), with audio caption inputs. The performance of these models on MMAR highlights the benchmark's challenging nature, and our analysis further reveals critical limitations of understanding and reasoning capabilities among current models. We hope MMAR will serve as a catalyst for future advances in this important but little-explored area.
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representative citing papers
Introduces CASU benchmark with four tasks to evaluate context-aware auditory scene understanding in LALMs via semi-synthetic audio compositions of speech, events, and environments.
PolySpeech-100 is a new benchmark for native-level speech comprehension across 110 linguistic variants that evaluates 22 models and reports E2E advantages on dialects, robustness gaps on low-resource languages, and degradation from Chain-of-Thought prompting.
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.
AQUA-Bench evaluates audio QA models on three unanswerability scenarios: missing correct answers, mismatched choice sets, and questions irrelevant to the audio.
A single DiT-based diffusion model unifies video-to-audio, text-to-audio, and joint video-text-to-audio generation, supported by a new 470k-pair dataset and three-stage progressive training that resolves task competition.
MECAT is a multi-expert benchmark for audio AI offering fine-grained captions and QA pairs generated via expert models and LLM reasoning, paired with the DATE metric that combines semantic similarity and cross-sample discriminability to favor detailed outputs.
Audex unifies audio understanding and generation on a strong text MoE backbone with multi-stage SFT plus text-only Cascade RL, matching open SOTA audio scores while mostly retaining text capability.
Introduces a benchmark for mechanistic analysis of temporal failures in LALMs and shows attention scaling at bottleneck layers improves accuracy from 55.9% to 59.1%.
Introduces RAIL, a CHC-grounded benchmark with five core auditory capabilities to assess LALMs beyond task-centric metrics, showing uneven model performance.
CoAT adds a continuous latent thinking space to LALMs via expert distillation to retain acoustic information, yielding gains on audio reasoning, understanding, music, emotion, and transcription benchmarks across three models.
A data pipeline, 14-dimension benchmark, and decoupled fine-tuning model are presented to advance fine-grained multi-dimensional speech understanding in LLMs.
EchoDistill applies noisy-to-clean self-distillation with GRPO to boost Audio LLM robustness, reporting 4.18% average GSR gains under strong noise.
Self-distillation of Qwen3-Omni-Thinking on 545k Cogito-Pipe audio reasoning traces yields the best open-source MMAR CoT scores and top-tier challenge ranking.
SURE is a new standardized framework for evaluating and training speech foundation models and Speech LLMs to improve comparability and reproducibility under realistic conditions.
MAPO is a dual-branch RL framework using modality relevance masks from cross-modal differential entropy and auxiliary attention losses to reduce late-stage modality collapse in audio reasoning models and improve benchmark results.
A survey of Large Audio Language Models that establishes a taxonomy of trustworthiness vulnerabilities and proposes a Defense-in-Depth roadmap for audio intelligence.
A statistical sign-off protocol for audio compressors ensures worst-case answer preservation across query families in LALMs.
Qwen3.5-Omni scales an omnimodal model to hundreds of billions of parameters with 256k context, introduces ARIA for stable speech synthesis, and reports SOTA performance on 215 audio-visual benchmarks while adding multilingual and audio-visual coding capabilities.
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.
Irrelevant audio including silence reduces accuracy and increases volatility in text reasoning for large audio-language models, with effects worsening at longer durations, higher amplitudes, and higher temperatures.
TinyGiantALM, a compact 1.5B audio-language model with instruction-aware refinement, achieves 46.4% zero-shot accuracy on MMAR and outperforms models up to 8x larger in mixed-modality tasks.
MOSS-Audio is an audio-language model using a 12.5 Hz encoder, DeepStack cross-layer injection, time markers, and an event-preserving annotation pipeline for unified audio understanding.
Audio-Mind introduces a conditional, auditable agentic framework for audio understanding that preserves frontend judgment and acquires bounded external evidence only when needed, reporting 80.4% on MMAR and 82.8% on MSU-Bench.
citing papers explorer
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ViMU: Benchmarking Video Metaphorical Understanding
ViMU is the first benchmark for evaluating video models on metaphorical and subtextual understanding using hint-free questions grounded in multimodal evidence.
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From Sounds to Scenes: A Benchmark for Evaluating Context-Aware Auditory Scene Understanding in Large Audio Language Models
Introduces CASU benchmark with four tasks to evaluate context-aware auditory scene understanding in LALMs via semi-synthetic audio compositions of speech, events, and environments.
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PolySpeech-100: A Large-Scale Benchmark for Speech Understanding Across 100+ Languages and Dialects
PolySpeech-100 is a new benchmark for native-level speech comprehension across 110 linguistic variants that evaluates 22 models and reports E2E advantages on dialects, robustness gaps on low-resource languages, and degradation from Chain-of-Thought prompting.
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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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AQUA-Bench: Beyond Finding Answers to Knowing When There Are None in Audio Question Answering
AQUA-Bench evaluates audio QA models on three unanswerability scenarios: missing correct answers, mismatched choice sets, and questions irrelevant to the audio.
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Omni2Sound: Towards Unified Video-Text-to-Audio Generation
A single DiT-based diffusion model unifies video-to-audio, text-to-audio, and joint video-text-to-audio generation, supported by a new 470k-pair dataset and three-stage progressive training that resolves task competition.
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MECAT: A Multi-Experts Constructed Benchmark for Fine-Grained Audio Understanding Tasks
MECAT is a multi-expert benchmark for audio AI offering fine-grained captions and QA pairs generated via expert models and LLM reasoning, paired with the DATE metric that combines semantic similarity and cross-sample discriminability to favor detailed outputs.
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Unified Audio Intelligence Without Regressing on Text Intelligence
Audex unifies audio understanding and generation on a strong text MoE backbone with multi-stage SFT plus text-only Cascade RL, matching open SOTA audio scores while mostly retaining text capability.
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A Closer Look at Failure Modes in Temporal Understanding of Large Audio-Language Models
Introduces a benchmark for mechanistic analysis of temporal failures in LALMs and shows attention scaling at bottleneck layers improves accuracy from 55.9% to 59.1%.
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RAIL: Rethinking Auditory Intelligence in Large Audio-Language Models with a CHC-Grounded Benchmark
Introduces RAIL, a CHC-grounded benchmark with five core auditory capabilities to assess LALMs beyond task-centric metrics, showing uneven model performance.
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Continuous Audio Thinking for Large Audio Language Models
CoAT adds a continuous latent thinking space to LALMs via expert distillation to retain acoustic information, yielding gains on audio reasoning, understanding, music, emotion, and transcription benchmarks across three models.
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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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EchoDistill:Alignment Noisy-to-Clean Self-Distillation for Robust Audio LLMs
EchoDistill applies noisy-to-clean self-distillation with GRPO to boost Audio LLM robustness, reporting 4.18% average GSR gains under strong noise.
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Audio-Cogito: Towards Deep Audio Reasoning in Large Audio Language Models
Self-distillation of Qwen3-Omni-Thinking on 545k Cogito-Pipe audio reasoning traces yields the best open-source MMAR CoT scores and top-tier challenge ranking.
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A Unified and Reproducible Experimentation Framework for Speech Understanding
SURE is a new standardized framework for evaluating and training speech foundation models and Speech LLMs to improve comparability and reproducibility under realistic conditions.
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Escape the Language Prior: Mitigating Late-Stage Modality Collapse in Audio Reasoning via Modality-Aware Policy Optimization
MAPO is a dual-branch RL framework using modality relevance masks from cross-modal differential entropy and auxiliary attention losses to reduce late-stage modality collapse in audio reasoning models and improve benchmark results.
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A Survey of Large Audio Language Models: Generalization, Trustworthiness, and Outlook
A survey of Large Audio Language Models that establishes a taxonomy of trustworthiness vulnerabilities and proposes a Defense-in-Depth roadmap for audio intelligence.
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Task-Aware Answer Preservation under Audio Compression for Large Audio Language Models
A statistical sign-off protocol for audio compressors ensures worst-case answer preservation across query families in LALMs.
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Qwen3.5-Omni Technical Report
Qwen3.5-Omni scales an omnimodal model to hundreds of billions of parameters with 256k context, introduces ARIA for stable speech synthesis, and reports SOTA performance on 215 audio-visual benchmarks while adding multilingual and audio-visual coding capabilities.
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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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When Silence Matters: The Impact of Irrelevant Audio on Text Reasoning in Large Audio-Language Models
Irrelevant audio including silence reduces accuracy and increases volatility in text reasoning for large audio-language models, with effects worsening at longer durations, higher amplitudes, and higher temperatures.
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TinyGiantALM: A Compact Audio-Language Model for Intent-Aware Reasoning under Resource Constraints
TinyGiantALM, a compact 1.5B audio-language model with instruction-aware refinement, achieves 46.4% zero-shot accuracy on MMAR and outperforms models up to 8x larger in mixed-modality tasks.
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MOSS-Audio Technical Report
MOSS-Audio is an audio-language model using a 12.5 Hz encoder, DeepStack cross-layer injection, time markers, and an event-preserving annotation pipeline for unified audio understanding.
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Audio-Mind: An Auditable Agentic Framework for Audio Understanding
Audio-Mind introduces a conditional, auditable agentic framework for audio understanding that preserves frontend judgment and acquires bounded external evidence only when needed, reporting 80.4% on MMAR and 82.8% on MSU-Bench.
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OmniFysics: Towards Physical Intelligence Evolution via Omni-Modal Signal Processing and Network Optimization
OmniFysics is an omni-modal network using a dynamic physical data engine and evolutive tuning to improve performance on multimodal benchmarks and physics-oriented tasks.
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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.