10 papers
Light-Omni: Reflex over Reasoning in Agentic Video Understanding with Long-Term Memory
Chang Nie, Jiaju Wei, Junlan Feng +2
Agentic video understanding equips models with long-term memory to autonomously process and respond to continuous, long-horizon multimodal streams. However, advanced video agents o…
EvoEmbedding: Evolvable Representations for Long-Context Retrieval and Agentic Memory
Chang Nie, Chaoyou Fu, Junlan Feng +1
Existing embedding models are inherently static: they encode text segments in isolation, ignoring their surrounding context and temporal order. This paper introduces EvoEmbedding,…
ISCSLP 2026 CoT-TTS Challenge: Chain-of-Thought Reasoning for Context-Aware Text-to-Speech
Wei Xue, Junlan Feng, Shilei Zhang +9
Recent advances in text-to-speech (TTS) have greatly improved speech naturalness, speaker similarity, and controllability. However, most existing controllable TTS systems still rel…
Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs
Yanyan Luo, Xue Han, Ruiqiao Bai +10
Large Language Models (LLMs) have enabled increasingly personalized interactions by adapting to users' preferences, contexts, and long-term histories. However, the mechanisms that…
ChildEval: When large language models meet children's personalities
Yanyan Luo, Xue Han, Chunxu Zhao +5
While LLMs enable personalized chatbots, their effectiveness in child-centered personalization remains unclear, as systematic evaluation of child-specific preferences is still lack…
Strategy-Aware Optimization Modeling with Reasoning LLMs
Ruiqing Zhao, Fengzhi Li, Yuan Zuo +5
Large language models (LLMs) can generate syntactically valid optimization programs, yet often struggle to reliably choose an effective modeling strategy, leading to incorrect form…
DR-Eval: Towards Realistic and Reproducible Deep Research Evaluation
Qianqian Xie, Qingheng Xiong, He Zhu +16
Deep Research Agents (DRAs) aim to solve complex, long-horizon research tasks involving planning, retrieval, multimodal understanding, and report generation, yet their evaluation r…
Beyond One-Size-Fits-All: Adaptive Subgraph Denoising for Zero-Shot Graph Learning with Large Language Models
Fengzhi Li, Liang Zhang, Yuan Zuo +5
Graph-based tasks in the zero-shot setting remain a significant challenge due to data scarcity and the inability of traditional Graph Neural Networks (GNNs) to generalize to unseen…
R3S: Refining and Recovering Reinforcement Signals for Multilingual Understanding and Reasoning
Junxiao Liu, Zhijun Wang, Yixiao Li +6
Large reasoning models often default to English reasoning when processing non-English questions, yet their performance drops substantially when reasoning in the question language.…
PEGRL: Improving Machine Translation by Post-Editing Guided Reinforcement Learning
Yunzhi Shen, Hao Zhou, Xin Huang +3
Reinforcement learning (RL) has shown strong promise for LLM-based machine translation, with recent methods such as GRPO demonstrating notable gains; nevertheless, translation-orie…