arXiv Machine Learning
Jul 10
Structural Bottlenecks on Frequency Representation in End-to-End Audio Models
arXiv:2607. 08545v1 Announce Type: cross Abstract: End-to-end neural audio models achieve high-fidelity compression and generation.
Constrained Hebbian Learning Supports Efficient Representational Allocation under Structural Constraints
arXiv:2607. 16027v1 Announce Type: new Abstract: Introduction: Biological systems face anatomical and metabolic constraints, including costly synaptic maintenance and limited connectivity.
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arXiv AI
Aug 11
Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models
arXiv:2608. 09227v1 Announce Type: new Abstract: Omnimodal language models (OLMs) enable unified audio-visual understanding, but processing long joint token sequences makes inference computationally prohibitive.
arXiv AI
Aug 11
Scaling Audio Models Efficiently: A Joint Study of Compute Constraints and Optimization Behavior
arXiv:2606. 22790v2 Announce Type: replace-cross Abstract: In this paper, we investigate the tradeoffs between compute allocation and model performance for two speech processing tasks: Automatic Speech Recognition (ASR) and Speech Emotion Recognition (SER).
arXiv Machine Learning
Aug 4
Interpretable MEG Decoding of Perceived Speech: Cortical Sources and the Stimulus Features That Drive Retrieval
arXiv:2608. 01481v1 Announce Type: new Abstract: Short segments of perceived speech can be retrieved from non-invasive magnetoencephalographic (MEG) recordings by deep networks trained with a CLIP-style objective against wav2vec 2.
Hugging Face Trending Papers
Aug 10
Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models
Omnimodal language models (OLMs) enable unified audio-visual understanding, but processing long joint token sequences makes inference computationally prohibitive. While recent token compression methods attempt to alleviate this burden, compressing modalities in isolation often destroys the temporal cross-modal anchors necessary for coherent reasoning.
arXiv Machine Learning
Jun 16
MUNI: Multimodal Unified Latent Diffusion for Coherent Any-to-Any Generation
arXiv:2606. 16408v1 Announce Type: new Abstract: We introduce MUNI, an end-to-end multimodal latent diffusion framework for any-to-any generation that unifies subset-conditioned cross-modal generation and unconditional joint sampling through a shared stochastic latent.
arXiv Machine Learning
Jun 10
SynIB: Informational Bottleneck for Maximizing Synergy in Multimodal Learning
arXiv:2606. 09853v1 Announce Type: new Abstract: A central objective in multimodal learning is to capture synergy: task-relevant information that arises only from the joint use of multiple modalities, and is not available from any single modality alone.
arXiv AI
Aug 11
VoxZip: Semantic-Anchored Temporal KV Cache Compression for Long-Context Audio Inference
arXiv:2608. 08569v1 Announce Type: new Abstract: Recent advancements in Speech Large Language Models have demonstrated remarkable capabilities in understanding complex audio tasks.
arXiv AI
Aug 11
Topographic Constraints Shape Brain-Like Component Structure in Auditory Models
arXiv:2509. 24039v2 Announce Type: replace-cross Abstract: If topography is a fundamental feature of the brain, it should influence both how neurons are arranged in space (i.
arXiv AI
Jul 14
Encoder-Side Neuron Identification and Amplification for Acoustic Perception in Large Audio-Language Models
arXiv:2607. 11801v1 Announce Type: cross Abstract: Large audio-language models (LALMs) often underperform on fine-grained, non-semantic attributes of speech, such as a speaker's emotion, despite strong performance on speech content.
arXiv AI
Jul 7
OmniFocus: Query-Guided Modality-Balanced Token Compression for Omni-Modal Large Language Models
arXiv:2607. 03050v1 Announce Type: cross Abstract: Omni modal large language models (OmniLLMs) have attracted wide attention for their ability to jointly process audio and video, but they generate large token sequences under audio-visual inputs, leading to substantial inference cost.
arXiv AI
Jul 2
Adaptive Perturbation Selection for Contrastive Audio Decoding
arXiv:2607. 00247v1 Announce Type: cross Abstract: Large audio-language models (LALMs) frequently hallucinate by overriding acoustic evidence with language priors.