arXiv AI August 21, 2026 By Umberto Cappellazzo, Xubo Liu, Stavros Petridis, Maja Pantic

Listening Forward: Next Patch Embedding Prediction Enables Scalable Audio Learners

arXiv:2608. 19863v1 Announce Type: cross Abstract: Self-supervised learning (SSL) has driven substantial progress in audio representation learning, though existing methods have increasingly relied on elaborate pre-training recipes to reach competitive performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 25

MJEPA: A Simple and Scalable Joint-Embedding Predictive Architecture for Audio-Visual Learning

arXiv:2606. 25225v1 Announce Type: cross Abstract: Self-supervised learning from large-scale video data has emerged as a dominant paradigm for visual representation learning.

By Revant Teotia, Adrien Bardes, Michael Rabbat, Sumit Chopra, Matthew J. Muckley, Nicolas Ballas

rag multimodal benchmarks

arXiv Machine Learning

Aug 17

Trajectory Dynamics in Self-Supervised Learning Latent Space for Audio Deepfake Detection

arXiv:2608. 13817v1 Announce Type: cross Abstract: Human speech production is constrained by physiology, giving rise to characteristic temporal structure on acoustic signals.

By Tom\'as Andrade Weber

diffusion benchmarks

Hugging Face Trending Papers

Jun 23

MJEPA: A Simple and Scalable Joint-Embedding Predictive Architecture for Audio-Visual Learning

Self-supervised learning from large-scale video data has emerged as a dominant paradigm for visual representation learning. Since audio and visual streams naturally co-occur in video data, extending this success to jointly learn from both modalities is a natural next step, yet it remains challenging.

rag multimodal benchmarks

arXiv AI

Jul 1

Revisiting Audio-language Pretraining for Learning General-purpose Audio Representation

arXiv:2511. 16757v2 Announce Type: replace-cross Abstract: Audio-language pretraining (ALP) holds promise for learning general-purpose audio representation, yet remains underexplored.

By Wei-Cheng Tseng, Xuanru Zhou, Mingyue Huo, Yiwen Shao, Hao Zhang, Dong Yu

llms

arXiv AI

Jun 6

Predict and Reconstruct: Joint Objectives for Self-Supervised Language Representation Learning

arXiv:2606. 05173v1 Announce Type: cross Abstract: Masked language modelling (MLM) has been the dominant pre-training objective for text encoders since BERT, yet it encourages representations that are strongly anchored to surface-form token identity rather than deeper semantic structure.

By Aimen Boukhari

rag diffusion benchmarks

arXiv AI

Oct 12

AudioTETRIS: Token Elimination via Transitional Importance Signals for Efficient Audio LLMs

AudioTETRIS is a training‑free, two‑stage token elimination framework designed for large audio language models. It leverages the geometry of audio encoder representations as an acoustic prior to compute per‑token importance scores, which guide progressive top‑k token selection across decoder layers. Experiments on Qwen2‑Audio‑7B, Phi‑4‑Multimodal, and Audio Flamingo 3 show that AudioTETRIS retains near‑full performance while using only 25% of audio tokens and outperforms existing training‑free baselines.

By Shreya Sajid, Anna Vettoruzzo, Joaquin Vanschoren

llms multimodal

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MJEPA: A Simple and Scalable Joint-Embedding Predictive Architecture for Audio-Visual Learning

Trajectory Dynamics in Self-Supervised Learning Latent Space for Audio Deepfake Detection

MJEPA: A Simple and Scalable Joint-Embedding Predictive Architecture for Audio-Visual Learning

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Predict and Reconstruct: Joint Objectives for Self-Supervised Language Representation Learning

AudioTETRIS: Token Elimination via Transitional Importance Signals for Efficient Audio LLMs