55 citations · 61 across the 6 of their papers we have counts for
6 papers
Conflicts Make Large Reasoning Models Vulnerable to Attacks
Honghao Liu, Chengjin Xu, Xuhui Jiang +5
Large Reasoning Models (LRMs) have achieved remarkable performance across diverse domains, yet their decision-making under conflicting objectives remains insufficiently understood.…
Continual Pretraining on Encrypted Synthetic Data for Privacy-Preserving LLMs
Honghao Liu, Xuhui Jiang, Chengjin Xu +4
Preserving privacy in sensitive data while pretraining large language models on small, domain-specific corpora presents a significant challenge. In this work, we take an explorator…
QuantBench: Benchmarking AI Methods for Quantitative Investment
Saizhuo Wang, Hao Kong, Jiadong Guo +7
The field of artificial intelligence (AI) in quantitative investment has seen significant advancements, yet it lacks a standardized benchmark aligned with industry practices. This…
From Deep Learning to LLMs: A survey of AI in Quantitative Investment
Bokai Cao, Saizhuo Wang, Xinyi Lin +4
Quantitative investment (quant) is an emerging, technology-driven approach in asset management, increasingy shaped by advancements in artificial intelligence. Recent advances in de…
QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model
Saizhuo Wang, Hang Yuan, Lionel M. Ni +1
Autonomous agents based on Large Language Models (LLMs) that devise plans and tackle real-world challenges have gained prominence.However, tailoring these agents for specialized do…
Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph
Jiashuo Sun, Chengjin Xu, Lumingyuan Tang +6
Although large language models (LLMs) have achieved significant success in various tasks, they often struggle with hallucination problems, especially in scenarios requiring deep an…