We present InstructFX2FX, a system for sequential audio effect refinement through multi-turn natural-language instructions. Existing text-to-effect systems are largely single-shot, mapping one textual descriptor to one preset. Real audio engineering is instead sequential: engineers refine an existing effect chain through successive instructions. This poses a stateful problem that single-shot systems do not address: given the current effect parameters state and a new instruction, update the sound while preserving what earlier instructions already achieved. InstructFX2FX addresses this with a hybrid architecture that divides labor between a language model and CLAP-guided optimization. The LLM serves as a high-level planner that selects effects and proposes the initial parameter state, motivated by recent evidence that LLMs can outperform CLAP-based optimization for single-turn text-to-effect mapping; CLAP-guided optimization then refines the existing parameter state, providing a more stable and robust refinement mechanism than LLM reprompting. In the demo, attendees drive a dry recording through successive natural-language instructions: after each turn, they choose how strongly the effect is applied, then issue the next instruction based on what still differs from the sound they intend. In a preliminary evaluation on SocialFX-derived descriptor pairs, CLAP-guided refinement achieves lower DSP-feature MMD than an LLM+LLM initialize-then-reprompt baseline on 9 of 10 pairs. Trajectory analysis further shows that, for differentiable effects, optimization tends to gradually move the audio toward the new target while retaining the effects of the previous instruction, highlighting the potential for gradual refinement.
Robust LLM-based Audio-Visual Speech Recognition with Sparse Modality Alignment and Visual Unit-Guided Refinement
School of Computer Science, Wuhan University, China（武汉大学计算机学院）；School of Artificial Intelligence, Wuhan University, China（武汉大学人工智能学院）；School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen, China（香港中文大学（深圳）人工智能学院）；AI Center, OPPO, China（OPPO人工智能中心）；Digital Innovation Research Center, Duke Kunshan University, China（杜克大学昆山数字创新研究中心）
Coarse-to-Fine Proposal Refinement Framework for Audio Temporal Forgery Detection and Localization
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；State Key Laboratory of Mathematical Engineering and Advanced Computing（数学工程与先进计算国家重点实验室）；Alibaba Group（阿里巴巴集团）；School of Cyber Science and Engineering, Wuhan University（武汉大学网络空间安全学院）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络空间科学与技术学院）
Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation
University of Cambridge（剑桥大学）；University of Southampton（南安普顿大学）
CoSTALA: Compositional Spatio-Temporal Audio-Language Alignment via Multi-Grain Hierarchical Contrastive Learning
Xi’an Jiaotong Liverpool University（西交利物浦大学）；MiLM Plus, Xiaomi Inc.（小米公司MiLM Plus）；University of Oulu（奥卢大学）；Institute of Acoustics, Chinese Academy of Sciences（中国科学院声学研究所）
EditGen: Harnessing Cross-Attention Control for Instruction-Based Auto-Regressive Audio Editing
Department of Informatics and Telecommunications, UoA NCSR "Demokritos"（信息与电信系，雅典国家科研中心"Demokritos"）；School of Electrical and Computer Engineering, NTUA（电气与计算机工程学院，国家技术研究院）
BreathNet: Generalizable Audio Deepfake Detection via Breath-Cue-Guided Feature Refinement
Guangdong Key Laboratory of Information Security, School of Computer Science and Engineering, Sun Yat-sen University（广东信息安全重点实验室，计算机科学与工程学院，中山大学）；State Key Laboratory of Multi-modal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；China Mobile Internet Corporation, China Mobile Communications Corporation（中国移动互联网有限公司，中国移动通信集团）；Guangdong Laboratory of Machine Perception and Intelligent Computing, Faculty of Engineering, Shenzhen MSU-BIT University（广东机器感知与智能计算实验室，工程学院，深圳MSU-BIT大学）
MIRA: A Musical Intent Refinement Agent for Aligning Text-to-Music Generation with User Intent
Shandong University（山东大学）；University of Science and Technology of China（中国科学技术大学）；Central Conservatory of Music（中央音乐学院）；Kunlun Tech Co. Ltd.（昆仑万维科技股份有限公司）；Shanghai Jiao Tong University（上海交通大学）
