# GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learning arXiv: https://arxiv.org/abs/2406.07428 ยท HTML: https://mlsanity.com/paper/2406.07428/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 29 of its 57 references that are in the map. - First posted on arXiv 11 Jun 2024. ## Builds on (most cited first, 29 of 29) - [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016) - [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018) - [ROMA: Multi-Agent Reinforcement Learning with Emergent Roles](https://mlsanity.com/paper/2003.08039/) (2020) - [RODE: Learning Roles to Decompose Multi-Agent Tasks](https://mlsanity.com/paper/2010.01523/) (2020) - [Optimal Auctions through Deep Learning: Advances in Differentiable Economics](https://mlsanity.com/paper/1706.03459/) (2017) - [Complexity of Mechanism Design](https://mlsanity.com/paper/1408.1486/) (2014) - [Automated Mechanism Design via Neural Networks](https://mlsanity.com/paper/1805.03382/) (2018) - [A Permutation-Equivariant Neural Network Architecture For Auction Design](https://mlsanity.com/paper/2003.01497/) (2020) - [Auction learning as a two-player game](https://mlsanity.com/paper/2006.05684/) (2020) - [Optimal-er Auctions through Attention](https://mlsanity.com/paper/2202.13110/) (2022) - [A Scalable Neural Network for DSIC Affine Maximizer Auction Design](https://mlsanity.com/paper/2305.12162/) (2023) - [PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning](https://mlsanity.com/paper/2106.03215/) (2021) - [Differentiable Economics for Randomized Affine Maximizer Auctions](https://mlsanity.com/paper/2202.02872/) (2022) - [Certifying Strategyproof Auction Networks](https://mlsanity.com/paper/2006.08742/) (2020) - [ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning](https://mlsanity.com/paper/2010.06398/) (2020) - [Strong Duality for a Multiple-Good Monopolist](https://mlsanity.com/paper/1409.4150/) (2014) - [Deep Learning for Two-Sided Matching](https://mlsanity.com/paper/2107.03427/) (2021) - [Learning Revenue-Maximizing Auctions With Differentiable Matching](https://mlsanity.com/paper/2106.07877/) (2021) - [Duality and Optimality of Auctions for Uniform Distributions](https://mlsanity.com/paper/1404.2329/) (2014) - [Deep Contract Design via Discontinuous Networks](https://mlsanity.com/paper/2307.02318/) (2023) - [A Simple and Approximately Optimal Mechanism for an Additive Buyer](https://mlsanity.com/paper/1405.6146/) (2014) - [A Duality-Based Unified Approach to Bayesian Mechanism Design](https://mlsanity.com/paper/1812.01577/) (2018) - [Multi-Sender Persuasion: A Computational Perspective](https://mlsanity.com/paper/2402.04971/) (2024) - [Convergence of Multi-Agent Learning with a Finite Step Size in General-Sum Games](https://mlsanity.com/paper/1903.02868/) (2019) - [Data Market Design through Deep Learning](https://mlsanity.com/paper/2310.20096/) (2023) - [A Myersonian Framework for Optimal Liquidity Provision in Automated Market Makers](https://mlsanity.com/paper/2303.00208/) (2023) - [Welfare-Preserving varepsilon-BIC to BIC Transformation with Negligible Revenue Loss](https://mlsanity.com/paper/2007.09579/) (2020) - [Worst-Case VCG Redistribution Mechanism Design Based on the Lottery Ticket Hypothesis](https://mlsanity.com/paper/2305.11011/) (2023) - [An Efficient varepsilon-BIC to BIC Transformation and Its Application to Black-Box Reduction in Revenue Maximization](https://mlsanity.com/paper/1911.10172/) (2019) ## Built on by (most cited first, 14 of 14) - [LLM Bidders Preserve the Mechanism-Level Orderings of Human Bidders](https://mlsanity.com/paper/2507.09083/) (2025) - [Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts](https://mlsanity.com/paper/2407.18074/) (2024) - [LLM-Powered Preference Elicitation in Combinatorial Assignment](https://mlsanity.com/paper/2502.10308/) (2025) - [Aligned Textual Scoring Rules](https://mlsanity.com/paper/2507.06221/) (2025) - [LLM Advertisement based on Neuron Auctions](https://mlsanity.com/paper/2605.08326/) (2026) - [Incentive-Aligned Multi-Source LLM Summaries](https://mlsanity.com/paper/2509.25184/) (2025) - [Bridging the Gap Between Estimated and True Regret Towards Reliable Regret Estimation in Deep Learning based Mechanism Design](https://mlsanity.com/paper/2601.13489/) (2026) - [Incentive-Aware Multi-Fidelity Optimization for Generative Advertising in Large Language Models](https://mlsanity.com/paper/2604.06263/) (2026) - [Learning Fair and Preferable Allocations through Neural Network](https://mlsanity.com/paper/2410.17500/) (2024) - [Deep Learning for Double Auction](https://mlsanity.com/paper/2504.05355/) (2025) - [Optimal Auction Design in the Joint Advertising](https://mlsanity.com/paper/2507.07418/) (2025) - [LLM Active Alignment: A Nash Equilibrium Perspective](https://mlsanity.com/paper/2602.06836/) (2026) - [Enhancing Affine Maximizer Auctions with Correlation-Aware Payment](https://mlsanity.com/paper/2602.09455/) (2026) - [One Model, Two Markets: Bid-Aware Generative Recommendation](https://mlsanity.com/paper/2603.22231/) (2026) ## Abstract (arXiv) 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 has enabled strong progress in AMD in recent years. Nevertheless, a major open problem has been to learn multi-bidder, general, and fully strategy-proof (SP) auctions. We introduce GEneral Menu-based NETwork (GemNet), which significantly extends the menu-based approach of the single-bidder RochetNet (D"utting et al., 2024) to the multi-bidder setting. The challenge in achieving SP is to learn bidder-independent menus that are feasible, so that the optimal menu choices for each bidder do not over-allocate items when taken together (we call this menu compatibility). GemNet penalizes the failure of menu compatibility during training, and transforms learned menus after training through price changes, by considering a set of discretized bidder values and reasoning about Lipschitz smoothness to guarantee menu compatibility on the entire value space. This approach is general, leaving trained menus that already satisfy menu compatibility undisturbed and reducing to RochetNet for a single bidder. Mixed-integer linear programs are used for menu transforms, and through a number of optimizations enabled by deep learning, including adaptive grids and methods to skip menu elements, we scale to large auction design problems. GemNet learns auctions with better revenue than affine maximization methods, achieves exact SP whereas previous general multi-bidder methods are approximately SP, and offers greatly enhanced interpretability.