2 citations · 4 across the 9 of their papers we have counts for
5 papers · 1 filter
Will AI Trade? A Computational Inversion of the No-Trade Theorem
Hanyu Li, Xiaotie Deng
Classic no-trade theorems attribute trade to heterogeneous beliefs. We re-examine this conclusion for AI agents, asking if trade can arise from computational limitations, under com…
How Large Language Models Need Symbolism
Xiaotie Deng, Hanyu Li
We argue that AI's future requires more than scaling. To unlock genuine discovery, large language models need a compass: human-crafted symbols to guide their powerful but blind int…
Discovering Expert-Level Nash Equilibrium Algorithms with Large Language Models
Hanyu Li, Dongchen Li, Xiaotie Deng
Designing polynomial-time algorithms for approximate Nash equilibria (ANE) with provable worst-case guarantees is a fundamental open problem in algorithmic game theory. While large…
IDA-Bench: Evaluating LLMs on Interactive Guided Data Analysis
Hanyu Li, Haoyu Liu, Tingyu Zhu +4
Large Language Models (LLMs) show promise as data analysis agents, but existing benchmarks overlook the iterative nature of the field, where experts' decisions evolve with deeper i…
Approximating N-Player Nash Equilibrium through Gradient Descent
Dongge Wang, Xiang Yan, Zehao Dou +3
Decoding how rational agents should behave in shared systems remains a critical challenge within theoretical computer science, artificial intelligence and economics studies. Centra…