3 citations · 6 across the 15 of their papers we have counts for
16 papers
Decoupled Visual Processing: Efficient Multimodal Adaptation via Modality-Specific Transformer Substitution
Mingkuan Feng, Zhengqi Wen, Jianhua Tao
Multimodal large language models (MLLMs) have demonstrated remarkable capabilities by integrating visual and textual understanding within a unified transformer architecture. Howeve…
SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning
Jinyang Wu, Shuo Yang, Zhengxi Lu +8
Large language models are increasingly trained as interactive agents for long-horizon tasks involving multi-turn interaction, tool use, and environment feedback. Outcome-based rein…
TACO: Tool-Augmented Credit Optimization for Agentic Tool Use
Mingkuan Feng, Jinyang Wu, Hao Gu +5
Agentic multimodal models perform diverse operations on an image via code and reason over the returned view, an effective paradigm for fine-grained visual question answering. Howev…
OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning
Shuo Yang, Jinyang Wu, Zhengxi Lu +8
Outcome-based reinforcement learning provides a stable optimization backbone for language agents, but its sparse trajectory-level rewards provide little guidance on which intermedi…
Maestro: Reinforcement Learning to Orchestrate Hierarchical Model-Skill Ensembles
Jinyang Wu, Guocheng Zhai, Ruihan Jin +7
The proliferation of large language models (LLMs) and modular skills has endowed autonomous agents with increasingly powerful capabilities. Existing frameworks typically rely on mo…
Spark: Strategic Policy-Aware Exploration via Dynamic Branching for Long-Horizon Agentic Learning
Jinyang Wu, Shuo Yang, Changpeng Yang +4
Reinforcement learning has empowered large language models to act as intelligent agents, yet training them for long-horizon tasks remains challenging due to the scarcity of high-qu…
RadialRouter: Structured Representation for Efficient and Robust Large Language Models Routing
Ruihan Jin, Pengpeng Shao, Zhengqi Wen +4
The rapid advancements in large language models (LLMs) have led to the emergence of routing techniques, which aim to efficiently select the optimal LLM from diverse candidates to t…
RPRA-ADD: Forgery Trace Enhancement-Driven Audio Deepfake Detection
Ruibo Fu, Xiaopeng Wang, Zhengqi Wen +8
Existing methods for deepfake audio detection have demonstrated some effectiveness. However, they still face challenges in generalizing to new forgery techniques and evolving attac…
Two-Stage Regularization-Based Structured Pruning for LLMs
Mingkuan Feng, Jinyang Wu, Siyuan Liu +7
The deployment of large language models (LLMs) is largely hindered by their large number of parameters. Structural pruning has emerged as a promising solution. Prior structured pru…
TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning
Jinyang Wu, Chonghua Liao, Mingkuan Feng +6
Reinforcement learning (RL) has emerged as an effective paradigm for enhancing model reasoning. However, existing RL methods like GRPO typically rely on unstructured self-sampling…
AStar: Boosting Multimodal Reasoning with Automated Structured Thinking
Jinyang Wu, Mingkuan Feng, Guocheng Zhai +7
Multimodal large language models excel across diverse domains but struggle with complex visual reasoning tasks. To enhance their reasoning capabilities, current approaches typicall…
DReSS: Data-driven Regularized Structured Streamlining for Large Language Models
Mingkuan Feng, Jinyang Wu, Shuai Zhang +5
Large language models (LLMs) have achieved significant progress across various domains, but their increasing scale results in high computational and memory costs. Recent studies ha…
Beyond Examples: High-level Automated Reasoning Paradigm in In-Context Learning via MCTS
Jinyang Wu, Mingkuan Feng, Shuai Zhang +4
In-context learning (ICL) enables large language models (LLMs) to perform downstream tasks through advanced prompting and high-quality demonstrations. However, traditional ICL para…
Pandora's Box or Aladdin's Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language Models
Jinyang Wu, Shuai Zhang, Feihu Che +4
Retrieval-Augmented Generation (RAG) has emerged as a crucial method for addressing hallucinations in large language models (LLMs). While recent research has extended RAG models to…
Can large language models understand uncommon meanings of common words?
Jinyang Wu, Feihu Che, Xinxin Zheng +5
Large language models (LLMs) like ChatGPT have shown significant advancements across diverse natural language understanding (NLU) tasks, including intelligent dialogue and autonomo…
KS-LLM: Knowledge Selection of Large Language Models with Evidence Document for Question Answering
Xinxin Zheng, Feihu Che, Jinyang Wu +4
Large language models (LLMs) suffer from the hallucination problem and face significant challenges when applied to knowledge-intensive tasks. A promising approach is to leverage ev…