2 citations · 2 across the 9 of their papers we have counts for
9 papers
SkillGLoW: Procedural-Family Skill Consolidation for Self-Improving Agents on Long-Horizon Task Streams
Ao Yan, Xin Zhang, Jiawei Du +1
LLM agents increasingly self-improve by writing and reusing textual skills, kept either as one global document or as a flat pool of per-task entries, though most of the evidence co…
Draw2Think: Harnessing Geometry Reasoning through Constraint Engine Interaction
Juncheng Hu, Jiawei Du, Xin Zhang +1
Vision-language models solve geometry problems with rising accuracy, yet their intermediate states remain latent and unverifiable: a relation expressed in textual reasoning or draw…
On the development of an AI performance and behavioural measures for teaching and classroom management
Andreea I. Niculescu, Jochen Ehnes, Chen Yi +9
This paper presents a two-year research project focused on developing AI-driven measures to analyze classroom dynamics, with particular emphasis on teacher actions captured through…
Rethinking Agent Design: From Top-Down Workflows to Bottom-Up Skill Evolution
Jiawei Du, Jinlong Wu, Yuzheng Chen +3
Most LLM-based agent frameworks adopt a top-down philosophy: humans decompose tasks, define workflows, and assign agents to execute each step. While effective on benchmark-style ta…
Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents
Tianmi Ma, Jiawei Du, Wenxin Huang +4
Large language models (LLMs) have demonstrated remarkable capabilities in natural language tasks, yet their performance in dynamic, real-world financial environments remains undere…
The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions
Ping Liu, Jiawei Du
Dataset distillation, which condenses large-scale datasets into compact synthetic representations, has emerged as a critical solution for training modern deep learning models effic…
Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment
Jiawei Du, Xin Zhang, Juncheng Hu +2
The sharp increase in data-related expenses has motivated research into condensing datasets while retaining the most informative features. Dataset distillation has thus recently co…
Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator
Xin Zhang, Jiawei Du, Ping Liu +1
Dataset distillation has emerged as a technique aiming to condense informative features from large, natural datasets into a compact and synthetic form. While recent advancements ha…
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation
Yifan Wu, Jiawei Du, Ping Liu +3
Dataset distillation is an advanced technique aimed at compressing datasets into significantly smaller counterparts, while preserving formidable training performance. Significant e…