2 citations · 2 across the 11 of their papers we have counts for
13 papers
CommuteProp: Decoupled Training for Communication Bound Split LLM Fine-Tuning
CHEN Ding, LUO Haochen, LIU Chen
Split learning has emerged as a promising paradigm for privacy-preserving LLM fine-tuning, yet its practical deployment is severely hindered by the sequential communication-computa…
LiveOption: Evaluating LLM Agents in Structured Option Trading with Nonlinear Payoffs
Haochen Luo, Yifan Li, Binh Minh An +4
Large language models (LLMs) and multi-agent systems (MAS) have shown promise in financial decision-making, yet existing evaluations focus on equity trading and primarily assess di…
Relay, Don't Route: Adaptive Population Handoff for Cost-Efficient LLM-Driven Evolution
Sichun Luo, Yi Huang, Guanzhi Deng +6
Large language model (LLM)-driven evolution has shown promise for program search and algorithm discovery, but relying on strong models throughout long evolutionary runs is costly.…
Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions
Haochen Luo, Yi Huang, Sichun Luo +5
Self-evolving frameworks usually optimize task solutions while treating the surrounding harness as fixed. We introduce Harness-Aware Self-Evolving (HASE), an agentic reinforcement-…
Learning Visual Spatial Planning from Symbolic State via Modality-Gap-Aware Self-Distillation
Haocheng Luo, Jiahui Liu, Ruicheng Zhang +8
While Vision-Language Models excel at general multimodal understanding, they still struggle with visual spatial planning. We attribute this limitation to a perception--reasoning mo…
SeaEvo: Advancing Algorithm Discovery with Strategy Space Evolution
Sichun Luo, Yi Huang, Haochen Luo +7
Large Language Model (LLM)-guided evolutionary search is increasingly used for automated algorithm discovery, yet most current methods track search progress primarily through execu…
PDA: Text-Augmented Defense Framework for Robust Vision-Language Models against Adversarial Image Attacks
Jingning Xu, Haochen Luo, Chen Liu
Vision-language models (VLMs) are vulnerable to adversarial image perturbations. Existing works based on adversarial training against task-specific adversarial examples are computa…
Sharpness-Aware Minimization in Logit Space Efficiently Enhances Direct Preference Optimization
Haocheng Luo, Zehang Deng, Thanh-Toan Do +3
Direct Preference Optimization (DPO) has emerged as a popular algorithm for aligning pretrained large language models with human preferences, owing to its simplicity and training s…
From Natural Language to Executable Option Strategies via Large Language Models
Haochen Luo, Zhengzhao Lai, Junjie Xu +4
Large Language Models (LLMs) excel at general code generation, yet translating natural-language trading intents into correct option strategies remains challenging. Real-world optio…
Evolutionary Factor Searching for Sparse Portfolio Optimization Using Large Language Models
Jiandong Chen, Haochen Luo, Yuan Zhang +2
Sparse portfolio optimization is a fundamental yet challenging problem in quantitative finance. Traditional approaches often use static objectives and thus adapt poorly to dynamic…
Differentially Private Neural Network Training Under the Hidden State Assumption
Ding Chen, Haochen Luo, Xiaofei Wang +1
Current differentially private learning paradigms face a severe utility bottleneck: DP-SGD degrades performance through noise accumulation over training steps, while aggregation-ba…
Which Model Generated This Image? A Model-Agnostic Approach for Origin Attribution
Fengyuan Liu, Haochen Luo, Yiming Li +2
Recent progress in visual generative models enables the generation of high-quality images. To prevent the misuse of generated images, it is important to identify the origin model t…
An Image Is Worth 1000 Lies: Adversarial Transferability across Prompts on Vision-Language Models
Haochen Luo, Jindong Gu, Fengyuan Liu +1
Different from traditional task-specific vision models, recent large VLMs can readily adapt to different vision tasks by simply using different textual instructions, i.e., prompts.…