2 citations · 3 across the 14 of their papers we have counts for
10 papers · 1 filter
Persona Following Is Not Selective Control: The Neutrality Gap in LLM User Simulation
Jiashen Ren, Wenlin Zhang, Bohan Zhang +5
Persona prompting is widely used to construct user simulations with large language models (LLMs), yet it relies on a largely untested assumption: specifying one user attribute shou…
EvoSkillRec: Skill-Genome Evolution for Recommender Architecture Discovery
Xiaopeng Li, Kuo Cai, Bo Chen +12
Modern recommender systems advance not only by scaling data and parameters, but also by encoding task-specific inductive biases through architecture, including sparse feature inter…
GUI-Lens: Coarse-to-Fine Cropping for GUI Grounding with General-Purpose VLMs
Zichuan Fu, Shirong Wang, Wenlin Zhang +10
GUI grounding maps natural-language instructions to click locations and is essential for reliable GUI agents. The task remains difficult on high-resolution, densely populated inter…
LLM-as-Code: Agentic Programming for Agent Harness
Junjia Qi, Zichuan Fu, Jingtong Gao +4
Every major LLM agent framework gives the LLM the role of orchestrator; the model decides what to do next, when to call tools, and when to stop. We argue that token explosion, cont…
MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning
Yimin Deng, Zhenxi Lin, Yejing Wang +9
Diagnostic prediction and clinical reasoning are critical tasks in healthcare applications. While Large Language Models (LLMs) have shown strong capabilities in commonsense reasoni…
AdapTime: Enabling Adaptive Temporal Reasoning in Large Language Models
Yimin Deng, Yejing Wang, Zhenxi Lin +8
Large language models have demonstrated strong reasoning capabilities in general knowledge question answering. However, their ability to handle temporal information remains limited…
Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning
Zichuan Fu, Xian Wu, Guojing Li +7
Recent advancements in large language models (LLMs) have catalyzed the rise of reasoning-intensive inference paradigms, where models perform explicit step-by-step reasoning before…
Chinese-SkillSpan: A Span-Level Dataset for ESCO-Aligned Competency Extraction from Chinese Job Ads
Guojing Li, Zichuan Fu, Junyi Li +6
Job Skill Named Entity Recognition (JobSkillNER) aims to automatically extract key skill information from large-scale job posting data, which is important for improving talent-mark…
Job Skill Extraction via LLM-Centric Multi-Module Framework
Guojing Li, Zichuan Fu, Junyi Li +8
Span-level skill extraction from job advertisements underpins candidate-job matching and labor-market analytics, yet generative large language models (LLMs) often yield malformed s…
Attention Needs to Focus: A Unified Perspective on Attention Allocation
Zichuan Fu, Wentao Song, Guojing Li +6
The Transformer architecture, a cornerstone of modern Large Language Models (LLMs), has achieved extraordinary success in sequence modeling, primarily due to its attention mechanis…