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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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6 papers · 1 filter

cs.CL2026

Type-Balanced Contextual Learning for Incremental Named Entity Recognition

Duzhen Zhang, Yahan Yu, Xiuyi Chen +2

Incremental Named Entity Recognition (INER) stands as a pivotal task in information extraction, emphasizing the successive identification of new entity types within unstructured te…

cs.SD2026

VoiceTTA: Enhancing Zero-Shot Text-to-Speech via Reinforcement Learning-Based Test-Time Adaptation

Tianxin Xie, Chenxing Li, Dong Yu +1

Recently, zero-shot text-to-speech (TTS) has enabled high-fidelity and expressive speech synthesis, but it often fails to imitate unseen speaking styles from uncommon scenarios (e.…

cs.CL2026

Escape the Language Prior: Mitigating Late-Stage Modality Collapse in Audio Reasoning via Modality-Aware Policy Optimization

Cihan Xiao, Yiwen Shao, Chenxing Li +5

Audio and omni-modal large language models exhibit impressive cross-modal reasoning capabilities. However, standard reinforcement learning methods such as GRPO do not explicitly we…

cs.SD2026

Audio-DeepThinker: Progressive Reasoning-Aware Reinforcement Learning for High-Quality Chain-of-Thought Emergence in Audio Language Models

Xiang He, Chenxing Li, Jinting Wang +5

Large Audio-Language Models (LALMs) have made significant progress in audio understanding, yet they primarily operate as perception-and-answer systems without explicit reasoning pr…

cs.CV2026

Frequency-guided Multi-level Reasoning for Scene Graph Generation in Video

Chenxing Li, Yiping Duan, Xiaoming Tao

Video Scene Graph Generation aims to obtain structured semantic representations of objects and their relationships in videos for high-level understanding. However, existing methods…

cs.SD2026

SemanticVocoder: Bridging Audio Generation and Audio Understanding via Semantic Latents

Zeyu Xie, Chenxing Li, Qiao Jin +6

Recent audio generation models typically rely on Variational Autoencoders (VAEs) and perform generation within the VAE latent space. Although VAEs excel at compression and reconstr…