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20242026
most citedLifelong Learning of Large Language Model based Agents: A Roadmap

4 citations · 4 across the 18 of their papers we have counts for

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Showing 2025 · cs.SDShow all

8 papers · 2 filters

cs.SD2025

PhyAVBench: A Challenging Audio Physics-Sensitivity Benchmark for Physically Grounded Text-to-Audio-Video Generation

Tianxin Xie, Wentao Lei, Kai Jiang +27

Text-to-audio-video (T2AV) generation is central to applications such as filmmaking and world modeling. However, current models often fail to produce physically plausible sounds. P…

cs.SD2025

AudioRAG+: Feedback-driven Retrieval-augmented Audio Generation with Large Audio Language Models

Junqi Zhao, Chenxing Li, Jinzheng Zhao +4

We propose a general feedback-driven retrieval-augmented generation (RAG) approach that leverages Large Audio Language Models (LALMs) to address the missing or imperfect synthesis…

cs.SD2025

AudioGenie-Reasoner: A Training-Free Multi-Agent Framework for Coarse-to-Fine Audio Deep Reasoning

Yan Rong, Chenxing Li, Dong Yu +1

Audio deep reasoning is a challenging task that requires expert-level perception, multi-step logical inference, and the integration of contextual knowledge. However, existing model…

cs.SD2025

GACA-DiT: Diffusion-based Dance-to-Music Generation with Genre-Adaptive Rhythm and Context-Aware Alignment

Jinting Wang, Chenxing Li, Li Liu

Dance-to-music (D2M) generation aims to automatically compose music that is rhythmically and temporally aligned with dance movements. Existing methods typically rely on coarse rhyt…

cs.SD2025

VCB Bench: An Evaluation Benchmark for Audio-Grounded Large Language Model Conversational Agents

Jiliang Hu, Wenfu Wang, Zuchao Li +6

Recent advances in large audio language models (LALMs) have greatly enhanced multimodal conversational systems. However, existing benchmarks remain limited -- they are mainly Engli…

cs.SD2025

Audio-Thinker: Guiding Audio Language Model When and How to Think via Reinforcement Learning

Shu Wu, Chenxing Li, Wenfu Wang +4

Recent advancements in large language models, multimodal large language models, and large audio language models (LALMs) have significantly improved their reasoning capabilities thr…

cs.SD2025

EmoSteer-TTS: Fine-Grained and Training-Free Emotion-Controllable Text-to-Speech via Activation Steering

Tianxin Xie, Shan Yang, Chenxing Li +2

Text-to-speech (TTS) has shown great progress in recent years. However, most existing TTS systems offer only coarse and rigid emotion control, typically via discrete emotion labels…

cs.SD2025

Dopamine Audiobook: A Training-free MLLM Agent for Emotional and Immersive Audiobook Generation

Yan Rong, Shan Yang, Chenxing Li +2

Audiobook generation aims to create rich, immersive listening experiences from multimodal inputs, but current approaches face three critical challenges: (1) the lack of synergistic…