1 citations · 1 across the 28 of their papers we have counts for
28 papers
Lightweight LLM Agent Memory with Small Language Models
Jiaquan Zhang, Chaoning Zhang, Shuxu Chen +9
Although LLM agents can leverage tools for complex tasks, they still need memory to maintain cross-turn consistency and accumulate reusable information in long-horizon interactions…
Gated Coordination for Efficient Multi-Agent Collaboration in Minecraft Game
HuaDong Jian, Chenghao Li, Haoyu Wang +4
In long-horizon open-world multi-agent systems, existing methods often treat local anomalies as automatic triggers for communication. This default design introduces coordination no…
Weak-Link Optimization for Multi-Agent Reasoning and Collaboration
Haoyu Bian, Chaoning Zhang, Jiaquan Zhang +4
LLM-driven multi-agent frameworks address complex reasoning tasks through multi-role collaboration. However, existing approaches often suffer from reasoning instability, where indi…
Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models
Xudong Wang, Chaoning Zhang, Chenghao Li +10
Large Language Models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, while recent prompting strategies such as Chain-of-Thought (CoT) have further elevate…
Transforming External Knowledge into Triplets for Enhanced Retrieval in RAG of LLMs
Xudong Wang, Chaoning Zhang, Qigan Sun +7
Retrieval-Augmented Generation (RAG) mitigates hallucination in large language models (LLMs) by incorporating external knowledge during generation. However, the effectiveness of RA…
Relaxing Anchor-Frame Dominance for Mitigating Hallucinations in Video Large Language Models
Zijian Liu, Sihan Cao, Pengcheng Zheng +5
Recent Video Large Language Models (Video-LLMs) have demonstrated strong capability in video understanding, yet they still suffer from hallucinations. Existing mitigation methods t…
Bias Redistribution in Visual Machine Unlearning: Does Forgetting One Group Harm Another?
Yunusa Haruna, Adamu Lawan, Ibrahim Haruna Abdulhamid +4
Machine unlearning enables models to selectively forget training data, driven by privacy regulations such as GDPR and CCPA. However, its fairness implications remain underexplored:…
Dual-Rerank: Fusing Causality and Utility for Industrial Generative Reranking
Chao Zhang, Shuai Lin, ChengLei Dai +5
Kuaishou serves over 400 million daily active users, processing hundreds of millions of search queries daily against a repository of tens of billions of short videos. As the final…
Experience Transfer for Multimodal LLM Agents in Minecraft Game
Chenghao Li, Jun Liu, Songbo Zhang +7
Multimodal LLM agents operating in complex game environments must continually reuse past experience to solve new tasks efficiently. In this work, we propose Echo, a transfer-orient…
Immunizing 3D Gaussian Generative Models Against Unauthorized Fine-Tuning via Attribute-Space Traps
Jianwei Zhang, Sihan Cao, Chaoning Zhang +7
Recent large-scale generative models enable high-quality 3D synthesis. However, the public accessibility of pre-trained weights introduces a critical vulnerability. Adversaries can…
RCP: Representation Consistency Pruner for Mitigating Distribution Shift in Large Vision-Language Models
Jianwei Zhang, Chaoning Zhang, Sihan Cao +7
Large Vision-Language Models (LVLMs) suffer from prohibitive inference costs due to the massive number of visual tokens processed by the language decoder. Existing pruning methods…
TDA-RC: Task-Driven Alignment for Knowledge-Based Reasoning Chains in Large Language Models
Jiaquan Zhang, Qigan Sun, Chaoning Zhang +11
Enhancing the reasoning capability of large language models (LLMs) remains a core challenge in natural language processing. The Chain-of-Thought (CoT) paradigm dominates practical…
Efficient and Interpretable Multi-Agent LLM Routing via Ant Colony Optimization
Xudong Wang, Chaoning Zhang, Jiaquan Zhang +8
Large Language Model (LLM)-driven Multi-Agent Systems (MAS) have demonstrated strong capability in complex reasoning and tool use, and heterogeneous agent pools further broaden the…
Language-Guided Token Compression with Reinforcement Learning in Large Vision-Language Models
Sihan Cao, Jianwei Zhang, Pengcheng Zheng +7
Large Vision-Language Models (LVLMs) incur substantial inference costs due to the processing of a vast number of visual tokens. Existing methods typically struggle to model progres…
Learning Global Hypothesis Space for Enhancing Synergistic Reasoning Chain
Jiaquan Zhang, Chaoning Zhang, Shuxu Chen +9
Chain-of-Thought (CoT) has been shown to significantly improve the reasoning accuracy of large language models (LLMs) on complex tasks. However, due to the autoregressive, step-by-…
Optimizing Soft Prompt Tuning via Structural Evolution
Zhenzhen Huang, Chaoning Zhang, Haoyu Bian +8
Soft prompt tuning leverages continuous embeddings to capture task-specific information in large pre-trained language models (LLMs), achieving competitive performance in few-shot s…
Geometric Neural Operators via Lie Group-Constrained Latent Dynamics
Jiaquan Zhang, Fachrina Dewi Puspitasari, Songbo Zhang +7
Neural operators offer an effective framework for learning solutions of partial differential equations for many physical systems in a resolution-invariant and data-driven manner. E…
Rethinking Input Domains in Physics-Informed Neural Networks via Geometric Compactification Mappings
Zhenzhen Huang, Haoyu Bian, Jiaquan Zhang +6
Several complex physical systems are governed by multi-scale partial differential equations (PDEs) that exhibit both smooth low-frequency components and localized high-frequency st…
Text summarization via global structure awareness
Jiaquan Zhang, Chaoning Zhang, Shuxu Chen +9
Text summarization is a fundamental task in natural language processing (NLP), and the information explosion has made long-document processing increasingly demanding, making summar…
Fat-Cat: Document-Driven Metacognitive Multi-Agent System for Complex Reasoning
Tong Yang, Yemin Wang, Chaoning Zhang +1
The effectiveness of LLM-based agents is often limited not by model capacity alone, but by how efficiently contextual information is utilized at runtime. Existing agent frameworks…
LLaVA-FA: Learning Fourier Approximation for Compressing Large Multimodal Models
Pengcheng Zheng, Chaoning Zhang, Jiarong Mo +8
Large multimodal models (LMMs) have achieved impressive performance on various vision-language tasks, but their substantial computational and memory costs hinder their practical de…
GRASP: Guided Region-Aware Sparse Prompting for Adapting MLLMs to Remote Sensing
Qigan Sun, Chaoning Zhang, Jianwei Zhang +8
In recent years, Multimodal Large Language Models (MLLMs) have made significant progress in visual question answering tasks. However, directly applying existing fine-tuning methods…
Code Evolution for Control: Synthesizing Policies via LLM-Driven Evolutionary Search
Ping Guo, Chao Li, Yinglan Feng +1
Designing effective control policies for autonomous systems remains a fundamental challenge, traditionally addressed through reinforcement learning or manual engineering. While rei…
Fast SAM2 with Text-Driven Token Pruning
Avilasha Mandal, Chaoning Zhang, Fachrina Dewi Puspitasari +6
Segment Anything Model 2 (SAM2), a vision foundation model has significantly advanced in prompt-driven video object segmentation, yet their practical deployment remains limited by…