activity
20242026
most citedWhen AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments

4 citations · 4 across the 1 of their papers we have counts for

collaborators

20 papers

q-fin.TR20264 cited

When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments

Chong Zhang, Xinyi Liu, Zhongmou Zhang +10

Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.g., macroeconomics, policy changes, company fund…

cs.CL2026

LogitTrace: Detecting Benchmark Contamination via Layerwise Logit Trajectories

Zirui He, Haiyan Zhao, Yingcong Li +2

Large language models (LLMs) are commonly evaluated on challenging benchmarks such as AIME and Math500, where benchmark contamination can make memorized solutions appear as genuine…

cs.CL2026

Rep2Text: Decoding Full Text from a Single LLM Token Representation

Haiyan Zhao, Zirui He, Yiming Tang +4

Large language models (LLMs) have achieved remarkable progress across diverse tasks, yet their internal mechanisms remain largely opaque. In this work, we investigate a fundamental…

cs.CL2026

SAGE: An Agentic Explainer Framework for Interpreting SAE Features in Language Models

Jiaojiao Han, Wujiang Xu, Mingyu Jin +1

Large language models (LLMs) have achieved remarkable progress, yet their internal mechanisms remain largely opaque, posing a significant challenge to their safe and reliable deplo…

cs.LG2025

Beyond Input Activations: Identifying Influential Latents by Gradient Sparse Autoencoders

Dong Shu, Xuansheng Wu, Haiyan Zhao +2

Sparse Autoencoders (SAEs) have recently emerged as powerful tools for interpreting and steering the internal representations of large language models (LLMs). However, conventional…

cs.LG2025

A Survey on Sparse Autoencoders: Interpreting the Internal Mechanisms of Large Language Models

Dong Shu, Xuansheng Wu, Haiyan Zhao +4

Large Language Models (LLMs) have transformed natural language processing, yet their internal mechanisms remain largely opaque. Recently, mechanistic interpretability has attracted…