arXiv Machine Learning

Enhancing Automatic Chord Recognition via Pseudo-Labeling and Knowledge Distillation

arXiv:2602. 19778v4 Announce Type: replace-cross Abstract: Automatic Chord Recognition (ACR) is constrained by the scarcity of aligned chord labels, as well-aligned annotations are costly to acquire.

arXiv Machine Learning

Jul 28

Analyzing the Importance of Blank for CTC-Based Knowledge Distillation

arXiv:2506. 01503v2 Announce Type: replace Abstract: With the rise of large pre-trained foundation models for automatic speech recognition new challenges appear.

By Benedikt Hilmes, Nick Rossenbach, Ralf Schl\"uter

efficiency multimodal

arXiv Machine Learning

Jun 8

Making the Most of Limited Data: Score-Aware Training for Text-to-Music Generation

arXiv:2606. 07387v1 Announce Type: new Abstract: State-of-the-art text-to-music generation systems rely on massive proprietary datasets and industrial-scale compute, making it impossible to disentangle architectural contributions from resource advantages.

By Yun-Chen Cheng, Tzu-Hung Huang, Chih-Pin Tan

benchmarks safety

arXiv AI

Jun 2

ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition

arXiv:2601. 19919v2 Announce Type: replace-cross Abstract: Knowledge distillation (KD) is one of the most effective paradigms for compressing large-scale foundation models into deployable architectures.

By Junseok Lee, Nahun Kim, Sangyong Lee, Chang-Jae Chun

efficiency multimodal benchmarks safety

arXiv AI

Jun 12

CMI-RewardBench: Evaluating Music Reward Models with Compositional Multimodal Instruction

arXiv:2603. 00610v3 Announce Type: replace-cross Abstract: While music generation models have evolved to handle complex multimodal inputs mixing text, lyrics, and reference audio, evaluation mechanisms have lagged behind.

By Yinghao Ma, Haiwen Xia, Hewei Gao, Weixiong Chen, Yuxin Ye, Yuchen Yang, Sungkyun Chang, Mingshuo Ding, Yizhi Li, Ruibin Yuan, Simon Dixon, Emmanouil Benetos

reinforcement-learning multimodal benchmarks safety

arXiv AI

Jul 8

TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios

arXiv:2607. 06179v1 Announce Type: cross Abstract: There are some datasets of varying scales for audio classification (AC) applied to different tasks.

By Hong Lyu, Mingru Yang, Qianhua He, Yanxiong Li, Jinxin Huang, Zhengyu Pei

arXiv AI

Jul 7

Unified Audio Intelligence Without Regressing on Text Intelligence

arXiv:2607. 05196v1 Announce Type: cross Abstract: Audio intelligence involves understanding, reasoning about, and generating both audio and speech.

By Zhifeng Kong, Sang-gil Lee, Jaehyeon Kim, Boxin Wang, Zihan Liu, Sungwon Kim, Yang Chen, Arushi Goel, Rajarshi Roy, Wenliang Dai, Zhuolin Yang, Yangyi Chen, Dongfu Jiang, Sreyan Ghosh, Tuomas Rintamaki, Andrew Tao, Jonathan Raiman, Mohammad Shoeybi, Bryan Catanzaro, Wei Ping

llms rag agents reinforcement-learning efficiency multimodal benchmarks safety

arXiv AI

Jul 1

Dataset Construction for Training LLM to Learn Analog Circuit Knowledge

arXiv:2508. 10409v3 Announce Type: replace-cross Abstract: This paper constructs a textual dataset for training large language models (LLMs) to learn analog circuit knowledge and customizes LLM training techniques.

By Zihao Chen, Ji Zhuang, Jinyi Shen, Xiaoyue Ke, Xinyi Yang, Mingjie Zhou, Zhuoyao Du, Xu Yan, Zhouyang Wu, Zhenyu Xu, Jiangli Huang, Li Shang, Xuan Zeng, Fan Yang

llms agents fine-tuning benchmarks

Hugging Face Trending Papers

Jul 6

Unified Audio Intelligence Without Regressing on Text Intelligence

Audio intelligence involves understanding, reasoning about, and generating both audio and speech. In this work, we introduce Nemotron-Labs-Audex-30B-A3B (Audex), a unified audio-text LLM built on Nemotron-Cascade-2-30B-A3B, a strong text-only MoE LLM.

llms rag agents reinforcement-learning efficiency multimodal benchmarks safety

arXiv AI

Jul 1

ZEBRA: Zero-Shot Entropy-Regularized Prompt Learning for Base-to-Novel Generalization in Audio-Language Models

arXiv:2606. 31587v1 Announce Type: cross Abstract: Audio-Language Models (ALMs) achieve strong zero-shot performance by aligning audio with textual class descriptions.

By Asif Hanif, Mohammad Yaqub

llms

arXiv Machine Learning

5d ago

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers

arXiv:2608. 12773v1 Announce Type: cross Abstract: Semi-supervised semantic segmentation has long turned on one question, which pseudo-labels to trust, and a generation of selection rules, dynamic thresholds, per-class curricula, soft confidence weights, answered it for the noisy, under-confident ResNet teachers of their day.

By Ebenezer Tarubinga

computer-vision benchmarks safety

arXiv Machine Learning

Jul 7

Labeled-Data-Free Meta-Learning: Efficient Task Generation Using Pre-trained Models and Unlabeled Data

arXiv:2607. 02850v1 Announce Type: new Abstract: Meta-learning without labeled data is crucial for real-world applications, where obtaining labeled datasets can be expensive or restricted due to privacy concerns.

By Lei Sun, Yusuke Tanaka, Tomoharu Iwata

benchmarks safety

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

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