7 citations · 10 across the 13 of their papers we have counts for
13 papers
Damage Predicts Recovery: When Calibration Data Matters in Compressing Financial LLMs
Junyi Ye, Mengjia Yu, Debapriya Hazra +1
Post-training quantization and pruning rely on a small calibration corpus. Whether specialized domains such as finance require domain-matched calibration data remains unsettled. We…
Same Signal, Opposite Meaning: Direction-Informed Adaptive Learning for LLM Agents
Ziming Li, Jiatan Huang, Xiaoguang Guo +3
Adaptive test-time compute for LLM agents aims to invoke extra computation only when it improves performance. Existing methods typically use confidence-, uncertainty-, or difficult…
MemFlow: Intent-Driven Memory Orchestration for Small Language Model Agents
Jiayi Chen, Yingcong Li, Guiling Wang
Modern language agents must operate over long-horizon, multi-turn histories, yet deploying such agents with Small Language Models (SLMs) remains fundamentally difficult. Full-conte…
MARS-DA: A Hierarchical Reinforcement Learning Framework for Risk-Aware Multi-Agent Bidding in Power Grids
Jiayi Chen, Xuan Zhang, Guiling Wang
The increasing penetration of renewable energy has introduced substantial volatility into wholesale electricity markets, complicating the optimal bidding strategies for power produ…
Illusions in Humans and AI: How Visual Perception Aligns and Diverges
Jianyi Yang, Junyi Ye, Ankan Dash +1
By comparing biological and artificial perception through the lens of illusions, we highlight critical differences in how each system constructs visual reality. Understanding these…
MARS: A Meta-Adaptive Reinforcement Learning Framework for Risk-Aware Multi-Agent Portfolio Management
Jiayi Chen, Jing Li, Guiling Wang
Reinforcement Learning (RL) has shown significant promise in automated portfolio management; however, effectively balancing risk and return remains a central challenge, as many mod…
From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems
Jiayi Chen, Junyi Ye, Guiling Wang
Compound AI Systems (CAIS) are an emerging paradigm that integrates large language models (LLMs) with external components, including retrievers, agents, tools, and orchestrators, t…
Evaluating Financial Sentiment Analysis with Annotators Instruction Assisted Prompting: Enhancing Contextual Interpretation and Stock Prediction Accuracy
A M Muntasir Rahman, Ajim Uddin, Guiling "Grace" Wang
Financial sentiment analysis (FSA) presents unique challenges to LLMs that surpass those in typical sentiment analysis due to the nuanced language used in financial contexts. The p…
Beyond End-to-End VLMs: Leveraging Intermediate Text Representations for Superior Flowchart Understanding
Junyi Ye, Ankan Dash, Wenpeng Yin +1
Flowcharts are typically presented as images, driving the trend of using vision-language models (VLMs) for end-to-end flowchart understanding. However, two key challenges arise: (i…
Assessing the Creativity of LLMs in Proposing Novel Solutions to Mathematical Problems
Junyi Ye, Jingyi Gu, Xinyun Zhao +2
The mathematical capabilities of AI systems are complex and multifaceted. Most existing research has predominantly focused on the correctness of AI-generated solutions to mathemati…
From Blind Solvers to Logical Thinkers: Benchmarking LLMs' Logical Integrity on Faulty Mathematical Problems
A M Muntasir Rahman, Junyi Ye, Wei Yao +5
Consider the math problem: "Lily received 3 cookies from her best friend yesterday and ate 5 for breakfast. Today, her friend gave her 3 more cookies. How many cookies does Lily ha…
From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing
Junyi Ye, Bhaskar Goswami, Jingyi Gu +2
This paper comprehensively reviews the application of machine learning (ML) and AI in finance, specifically in the context of asset pricing. It starts by summarizing the traditiona…
DataFrame QA: A Universal LLM Framework on DataFrame Question Answering Without Data Exposure
Junyi Ye, Mengnan Du, Guiling Wang
This paper introduces DataFrame question answering (QA), a novel task that utilizes large language models (LLMs) to generate Pandas queries for information retrieval and data analy…