6 citations · 14 across the 21 of their papers we have counts for
23 papers
Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation
Kaiji Zhou, Aleš Leonardis, Yue Feng
Enhancing the reasoning capabilities of large language model (LLM) agents requires effective orchestration of diverse expert models and tools. However, existing frameworks typicall…
RouteRec: Strict Evaluation of Recommender-Agent Selection and Aggregation
Kaiji Zhou, Vladimir Kalmykov, Yue Feng
Recommender systems increasingly face a choice among heterogeneous agents -- collaborative filters, sequential models, content-based retrievers, and LLM-based rerankers -- yet no s…
ProReviewer: Evidence-Driven Investigation for Scientific Peer Review
Haishuo Fang, Yue Feng, Iryna Gurevych
Effective peer review requires adaptive investigation that responds to evidence emerging from each paper. Existing LLM-based reviewers largely organize the review process around pr…
When to Think Deeply: Inhibitory Deliberation for LLM Reasoning
Zhixuan He, Yue Feng
Reasoning Large Language Models can improve problem-solving performance through deliberative inference, but invoking slow reasoning for every input is computationally expensive and…
Certified Program Synthesis with a Multi-Modal Verifier
Yueyang Feng, Dipesh Kafle, Vladimir Gladshtein +5
Certified program synthesis (aka vericoding) is the process of automatically generating a program, its formal specification, and a machine-checkable proof of their alignment from a…
From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation
Mingfei Lu, Yi Zhang, Mengjia Wu +1
Legal consultation question answering (Legal CQA) presents unique challenges compared to traditional legal QA tasks, including the scarcity of high-quality training data, complex t…
ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders
Ofer Meshi, Krisztian Balog, Sally Goldman +5
The promise of LLM-based user simulators to improve conversational AI is hindered by a critical "realism gap," leading to systems that are optimized for simulated interactions, but…
Robust Bias Evaluation with FilBBQ: A Filipino Bias Benchmark for Question-Answering Language Models
Lance Calvin Lim Gamboa, Yue Feng, Mark Lee
With natural language generation becoming a popular use case for language models, the Bias Benchmark for Question-Answering (BBQ) has grown to be an important benchmark format for…
SAM3-I: Segment Anything with Instructions
Jingjing Li, Yue Feng, Yuchen Guo +10
Segment Anything Model 3 (SAM3) advances open-vocabulary segmentation through promptable concept segmentation, enabling users to segment all instances associated with a given conce…
The 2nd Workshop on Human-Centered Recommender Systems
Kaike Zhang, Jiakai Tang, Du Su +6
Recommender systems shape how people discover information, form opinions, and connect with society. Yet, as their influence grows, traditional metrics, e.g., accuracy, clicks, and…
Med-VRAgent: A Framework for Medical Visual Reasoning-Enhanced Agents
Guangfu Guo, Xiaoqian Lu, Yue Feng
Visual Language Models (VLMs) achieve promising results in medical reasoning but struggle with hallucinations, vague descriptions, inconsistent logic and poor localization. To addr…
Unleashing Diverse Thinking Modes in LLMs through Multi-Agent Collaboration
Zhixuan He, Yue Feng
Large Language Models (LLMs) demonstrate strong performance but often lack interpretable reasoning. This paper introduces the Multi-Agent Collaboration Framework for Diverse Thinki…
Direct Retrieval-augmented Optimization: Synergizing Knowledge Selection and Language Models
Zhengliang Shi, Lingyong Yan, Weiwei Sun +7
Retrieval-augmented generation (RAG) integrates large language models ( LLM s) with retrievers to access external knowledge, improving the factuality of LLM generation in knowledge…
An Empirical Study of Qwen3 Quantization
Xingyu Zheng, Yuye Li, Haoran Chu +7
The Qwen series has emerged as a leading family of open-source Large Language Models (LLMs), demonstrating remarkable capabilities in natural language understanding tasks. With the…
The 1st EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval
Junchen Fu, Xuri Ge, Xin Xin +5
Multimodal representation learning has garnered significant attention in the AI community, largely due to the success of large pre-trained multimodal foundation models like LLaMA,…
PolicyEvol-Agent: Evolving Policy via Environment Perception and Self-Awareness with Theory of Mind
Yajie Yu, Yue Feng
Multi-agents has exhibited significant intelligence in real-word simulations with Large language models (LLMs) due to the capabilities of social cognition and knowledge retrieval.…
UoB-NLP at SemEval-2025 Task 11: Leveraging Adapters for Multilingual and Cross-Lingual Emotion Detection
Frances Laureano De Leon, Yixiao Wang, Yue Feng +1
Emotion detection in natural language processing is a challenging task due to the complexity of human emotions and linguistic diversity. While significant progress has been made in…
Self-Adaptive Cognitive Debiasing for Large Language Models in Decision-Making
Yougang Lyu, Shijie Ren, Yue Feng +4
Large language models (LLMs) have shown potential in supporting decision-making applications, particularly as personal assistants in the financial, healthcare, and legal domains. W…
Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation
Shijie Wang, Wenqi Fan, Yue Feng +4
Recommender systems have become increasingly vital in our daily lives, helping to alleviate the problem of information overload across various user-oriented online services. The em…
JAILJUDGE: A Comprehensive Jailbreak Judge Benchmark with Multi-Agent Enhanced Explanation Evaluation Framework
Fan Liu, Yue Feng, Zhao Xu +4
Despite advancements in enhancing LLM safety against jailbreak attacks, evaluating LLM defenses remains a challenge, with current methods often lacking explainability and generaliz…
Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents
Zhengliang Shi, Shen Gao, Lingyong Yan +6
Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extend their utility, enabling them to solve practical tasks. Previous methods ma…
Lending Interaction Wings to Recommender Systems with Conversational Agents
Jiarui Jin, Xianyu Chen, Fanghua Ye +5
Recommender systems trained on offline historical user behaviors are embracing conversational techniques to online query user preference. Unlike prior conversational recommendation…
Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals
Zhiyuan Hu, Yue Feng, Yang Deng +4
Recently, the development of large language models (LLMs) has been significantly enhanced the question answering and dialogue generation, and makes them become increasingly popular…