2 citations · 4 across the 9 of their papers we have counts for
11 papers
Approximating Combinatorial Contracts with Arbitrary Costs
Xiaotie Deng, Hanyu Li, Chenghua Liu
We study single-agent combinatorial contracts under linear payments. Under a reward share , an agent chooses a subset of hidden actions, generating reward …
Cardinality-Constrained Randomized Assortments with Balanced Market Share
Xiaotie Deng, Hanyu Li, Chenghua Liu
Assortment optimization asks a seller which of products to display to each customer. Under the multinomial-logit (MNL) model, a revenue-maximizing policy may concentrate most p…
Reinforcement Learning for Chain of Thought Compression with One-Domain-to-All Generalization
Hanyu Li, Jiangshan Duo, Bofei Gao +4
Chain-of-thought reasoning in large language models can trigger an "overthinking trap": longer rollouts raise cost and latency yet often yield unreliable accuracy gains. Existing m…
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…
Large-Scale Contextual Market Equilibrium Computation through Deep Learning
Yunxuan Ma, Yide Bian, Hao Xu +5
Market equilibrium is one of the most fundamental solution concepts in economics and social optimization analysis. Existing works on market equilibrium computation primarily focus…
A survey on algorithms for Nash equilibria in finite normal-form games
Hanyu Li, Wenhan Huang, Zhijian Duan +4
Nash equilibrium is one of the most influential solution concepts in game theory. With the development of computer science and artificial intelligence, there is an increasing deman…
The Search-and-Mix Paradigm in Approximate Nash Equilibrium Algorithms
Xiaotie Deng, Dongchen Li, Hanyu Li
AI in Math deals with mathematics in a constructive manner so that reasoning becomes automated, less laborious, and less error-prone. For algorithms, the question becomes how to au…