1 citations · 2 across the 20 of their papers we have counts for
21 papers
Shape Your Feed: An LLM-based Agentic System for Conversational Recommendation
Ziyun Xu, Bosen Ding, Yue Zhang +10
Industrial recommendation systems predominantly adopt a passive ranking paradigm that infers user preferences from implicit behavioral signals (e.g., clicks, dwell time) rather tha…
Woodpecker Distillation: Weak Models Diagnose Reasoning Bugs in Strong Models
Dayu Wang, Jiaye Yang, Weikang Li +4
Large language models often fail on reasoning tasks despite possessing the capability to solve them. We argue that many such failures arise from localized reasoning bugs in interme…
Learning User-Aware Recall: Personalized Retrieval in Long-Term Conversational Memory
ZhiShu Jiang, Haibo Liu, Xin Shen +6
Long-term conversational agents are expected to remember past interactions, but memory is useful only when the right evidence is recalled for the right user. Existing memory-augmen…
It Takes 8 Tokens: Weak-to-Strong Off-Policy RL via Auxiliary Branches
Dayu Wang, Jiaye Yang, Weikang Li +4
Reinforcement learning with verifiable rewards has emerged as a standard approach for enhancing reasoning in large language models, which typically optimizes the policy by contrast…
MapAgent: An Industrial-Grade Agentic Framework for City-scale Lane-level Map Generation
Deguo Xia, Zihan Li, Haochen Zhao +6
Lane-level maps are critical infrastructure for autonomous driving and lane-level navigation, yet constructing and maintaining standardized lane networks for hundreds of cities rem…
RepoZero: Can LLMs Generate a Code Repository from Scratch?
Zhaoxi Zhang, Yiming Xu, Jiahui Liang +7
Large Language Models (LLMs) have recently shown remarkable progress in code generation, yet their ability to construct complete software repositories from scratch remains poorly u…
DuIVRS-2: An LLM-based Interactive Voice Response System for Large-scale POI Attribute Acquisition
Le Zhang, Shengming Zhang, Rui Zha +3
Accurate Point of Interest (POI) attribute acquisition is essential for location-based services, yet traditional modular Interactive Voice Response (IVR) systems suffer from error…
VISOR: Agentic Visual Retrieval-Augmented Generation via Iterative Search and Over-horizon Reasoning
Yucheng Shen, Jiulong Wu, Jizhou Huang +3
Visual Retrieval-Augmented Generation (VRAG) empowers Vision-Language Models to retrieve and reason over visually rich documents. To tackle complex queries requiring multi-step rea…
DuCCAE: A Hybrid Engine for Immersive Conversation via Collaboration, Augmentation, and Evolution
Xin Shen, Zhishu Jiang, Jiaye Yang +13
Immersive conversational systems in production face a persistent trade-off between responsiveness and long-horizon task capability. Real-time interaction is achievable for lightwei…
Beyond End-to-End Video Models: An LLM-Based Multi-Agent System for Educational Video Generation
Lingyong Yan, Jiulong Wu, Dong Xie +3
Although recent end-to-end video generation models demonstrate impressive performance in visually oriented content creation, they remain limited in scenarios that require strict lo…
Stay in Character, Stay Safe: Dual-Cycle Adversarial Self-Evolution for Safety Role-Playing Agents
Mingyang Liao, Yichen Wan, shuchen wu +6
LLM-based role-playing has rapidly improved in fidelity, yet stronger adherence to persona constraints commonly increases vulnerability to jailbreak attacks, especially for risky o…
Video-MSR: Benchmarking Multi-hop Spatial Reasoning Capabilities of MLLMs
Rui Zhu, Xin Shen, Shuchen Wu +6
Spatial reasoning has emerged as a critical capability for Multimodal Large Language Models (MLLMs), drawing increasing attention and rapid advancement. However, existing benchmark…
Decide Then Retrieve: A Training-Free Framework with Uncertainty-Guided Triggering and Dual-Path Retrieval
Wang Chen, Guanqiang Qi, Weikang Li +3
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge, but existing approaches indiscriminately trigger retrieval and rely…
One Tool Is Enough: Reinforcement Learning for Repository-Level LLM Agents
Zhaoxi Zhang, Yitong Duan, Yanzhi Zhang +9
Locating files and functions requiring modification in large software repositories is challenging due to their scale and structural complexity. Existing LLM-based methods typically…
FingerCap: Fine-grained Finger-level Hand Motion Captioning
Xin Shen, Rui Zhu, Lei Shen +10
Understanding fine-grained human hand motion is fundamental to visual perception, embodied intelligence, and multimodal communication. In this work, we propose Fine-grained Finger-…
SciEGQA: A Dataset for Scientific Evidence-Grounded Question Answering and Reasoning
Wenhan Yu, Zhaoxi Zhang, Wang Chen +5
Scientific documents contain complex multimodal structures, which makes evidence localization and scientific reasoning in Document Visual Question Answering particularly challengin…
Facial-R1: Aligning Reasoning and Recognition for Facial Emotion Analysis
Jiulong Wu, Yucheng Shen, Lingyong Yan +4
Facial Emotion Analysis (FEA) extends traditional facial emotion recognition by incorporating explainable, fine-grained reasoning. The task integrates three subtasks: emotion recog…
Probabilistic Modeling of Intentions in Socially Intelligent LLM Agents
Feifan Xia, Yuyang Fang, Defang Li +5
We present a probabilistic intent modeling framework for large language model (LLM) agents in multi-turn social dialogue. The framework maintains a belief distribution over a partn…
CMRAG: Co-modality-based visual document retrieval and question answering
Wang Chen, Wenhan Yu, Guanqiang Qi +5
Retrieval-Augmented Generation (RAG) has become a core paradigm in document question answering tasks. However, existing methods have limitations when dealing with multimodal docume…
Cross-LoRA: A Data-Free LoRA Transfer Framework across Heterogeneous LLMs
Feifan Xia, Mingyang Liao, Yuyang Fang +6
Traditional parameter-efficient fine-tuning (PEFT) methods such as LoRA are tightly coupled with the base model architecture, which constrains their applicability across heterogene…
PAIRS: Parametric-Verified Adaptive Information Retrieval and Selection for Efficient RAG
Wang Chen, Guanqiang Qi, Weikang Li +3
Retrieval-Augmented Generation (RAG) has become a cornerstone technique for enhancing large language models (LLMs) with external knowledge. However, current RAG systems face two cr…