4 citations · 5 across the 11 of their papers we have counts for
13 papers
Cogentic: Multi-Agent Orchestration for Automated Proof Discovery
Yang Cai, Vineet Gupta, Yanchen Jiang +4
We present Cogentic, a multi-agent harness for automated proof discovery on open research problems. While frontier language models can generate strong mathematical ideas in a singl…
Efficiency of Generalized Proportional First-Price Auctions Under Auto-bidding
Yang Cai, Vineet Gupta, Yanchen Jiang +4
Auto-bidding is now widely adopted in online advertising platforms, allowing advertisers to specify high-level campaign objectives--such as maximizing total value subject to a retu…
One Model, Two Markets: Bid-Aware Generative Recommendation
Yanchen Jiang, Zhe Feng, Christopher P. Mah +2
Generative Recommender Systems using semantic ids, such as TIGER (Rajput et al., 2023), have emerged as a widely adopted competitive paradigm in sequential recommendation. However,…
The Prosocial Ranking Challenge: Reducing Polarization on Social Media without Sacrificing Engagement
Jonathan Stray, Ian Baker, George Beknazar-Yuzbashev +41
We report the first direct comparisons of multiple alternative social media algorithms on multiple platforms on outcomes of societal interest. We used a browser extension to modify…
LLM Active Alignment: A Nash Equilibrium Perspective
Tonghan Wang, Yuqi Pan, Xinyi Yang +3
We develop a game-theoretic framework for predicting and steering the behavior of populations of large language models (LLMs) through Nash equilibrium (NE) analysis. To avoid the i…
Incentive-Aligned Multi-Source LLM Summaries
Yanchen Jiang, Zhe Feng, Aranyak Mehta
Large language models (LLMs) are increasingly used in modern search and answer systems to synthesize multiple, sometimes conflicting, texts into a single response, yet current pipe…
LLM Bidders Preserve the Mechanism-Level Orderings of Human Bidders
Anand Shah, Kehang Zhu, Yanchen Jiang +4
Training on vast amounts of human-generated data has motivated growing interest in using large language models (LLMs) to simulate human behavior. We ask which features of human beh…
BundleFlow: Deep Menus for Combinatorial Auctions by Diffusion-Based Optimization
Tonghan Wang, Yanchen Jiang, David C. Parkes
Differentiable economics -- the use of deep learning for auction design -- has driven progress in the automated design of multi-item auctions with additive or unit-demand valuation…
LLM-Powered Preference Elicitation in Combinatorial Assignment
Ermis Soumalias, Yanchen Jiang, Kehang Zhu +3
We study the potential of large language models (LLMs) as proxies for humans to simplify preference elicitation (PE) in combinatorial assignment. While traditional PE methods rely…
Charting the Shapes of Stories with Game Theory
Constantinos Daskalakis, Ian Gemp, Yanchen Jiang +3
Stories are records of our experiences and their analysis reveals insights into the nature of being human. Successful analyses are often interdisciplinary, leveraging mathematical…
On Diffusion Models for Multi-Agent Partial Observability: Shared Attractors, Error Bounds, and Composite Flow
Tonghan Wang, Heng Dong, Yanchen Jiang +2
Multiagent systems grapple with partial observability (PO), and the decentralized POMDP (Dec-POMDP) model highlights the fundamental nature of this challenge. Whereas recent approa…
GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learning
Tonghan Wang, Yanchen Jiang, David C. Parkes
Automated mechanism design (AMD) uses computational methods for mechanism design. Differentiable economics is a form of AMD that uses deep learning to learn mechanism designs and h…
Data Market Design through Deep Learning
Sai Srivatsa Ravindranath, Yanchen Jiang, David C. Parkes
The data market design problem is a problem in economic theory to find a set of signaling schemes (statistical experiments) to maximize expected revenue to the information seller,…