1 citations · 2 across the 9 of their papers we have counts for
11 papers
Data Strategies for Large Language Models: A Unified Perspective on the Training Lifecycle
Yuanjian Xu, Jianing Hao, Tianze Sun +7
Data strategy plays a central role in training large language models (LLMs). It governs the selection, weighting, and scheduling of training data, thereby determining the capabilit…
AgentPerturb: Evaluating LLM Agent Robustness under Controlled Interaction Perturbations
Yuanjian Xu, Jinyi Han, Jianing Hao +5
Real-world tasks are often complex and noisy, posing substantial challenges for LLM agents. Yet existing evaluations lack a systematic framework for jointly characterizing potentia…
Skill-Use: Can LLMs Actually Use Skills in Agentic Harnesses?
Jinyi Han, Yuanjian Xu, Ying Liao +6
Large language model (LLM) agents increasingly rely on skills, structured documents that specify when to act, which procedure to follow, and which tools are allowed. Existing evalu…
Towards Efficient LLMs Annealing with Principled Sample Selection
Yuanjian Xu, Jianing Hao, Wanbo Zhang +2
The annealing phase is a pivotal convergence stage in LLM pre-training that ultimately determines final model quality. However, effectively selecting training data during this phas…
D: Dynamic Directional Graph-Constrained Data Scheduling for LLM Training
Yuanjian Xu, Jianing Hao, Guang Zhang +1
Training data plays a central role in large language models (LLMs) optimization, motivating extensive research on data scheduling strategies. Most existing approaches concentrate o…
BizCompass: Benchmarking the Reasoning Capabilities of LLMs in Business Knowledge and Applications
Jianing Hao, Yuhe Wu, Yuanjian Xu +5
Large language models (LLMs) hold great promise for business applications, yet business analysis remains inherently complex, demanding rigorous reasoning and the integration of div…
Rethinking Data Mixing from the Perspective of Large Language Models
Yuanjian Xu, Tianze Sun, Changwei Xu +7
Data mixing strategy is essential for large language model (LLM) training. Empirical evidence shows that inappropriate strategies can significantly reduce generalization. Although…
Game-Theoretic Lens on LLM-based Multi-Agent Systems
Jianing Hao, Han Ding, Yuanjian Xu +5
Large language models (LLMs) have demonstrated strong reasoning, planning, and communication abilities, enabling them to operate as autonomous agents in open environments. While si…
HGAN-SDEs: Learning Neural Stochastic Differential Equations with Hermite-Guided Adversarial Training
Yuanjian Xu, Yuan Shuai, Jianing Hao +1
Neural Stochastic Differential Equations (Neural SDEs) provide a principled framework for modeling continuous-time stochastic processes and have been widely adopted in fields rangi…
FinRipple: Aligning Large Language Models with Financial Market for Event Ripple Effect Awareness
Yuanjian Xu, Jianing Hao, Kunsheng Tang +4
Financial markets exhibit complex dynamics where localized events trigger ripple effects across entities. Previous event studies, constrained by static single-company analyses and…
LENS: Large Pre-trained Transformer for Exploring Financial Time Series Regularities
Yuanjian Xu, Anxian Liu, Jianing Hao +3
Modeling large-scale time series has gained significant attention in recent years. However, its direct application in finance remains challenging due to substantial differences in…