Library
Reusable components
The shared library separates mathematical foundations, research-area tools, and application-specific models. The table summarizes the main concepts covered; each area links to its Lean entry point.
| Area | Content | Lines of code |
|---|---|---|
| Foundations: mathematics and discrete structures | Fixed-point theorems; convexity and finite-dimensional geometry; graphs, combinatorics, and counting; asymptotic analysis; Boolean circuits. | 54,675 |
| Foundations: probability and stochastic processes | Concentration and large deviations; coupling and Wasserstein distance; conditional laws and Bayesian updating; order statistics; Markov chains, martingales, Poisson and renewal processes. | 197,122 |
| Foundations: optimization | Convex optimization and duality; gradient, subgradient, and mirror descent; stochastic optimization; proximal maps and projections; argmax stability and envelope theorems. | 20,569 |
| AI and applications | Alignment axioms and welfare; differential privacy and composition; algorithmic fairness; admissions policies; rating systems; recommender systems and producer/user fairness. | 26,487 |
| Learning and statistics | Generalization and sample complexity; calibration, multiaccuracy, and omniprediction; online learning and regret; bandits; reinforcement learning; preference learning and human feedback. | 61,555 |
| Algorithms and complexity | Online allocation and advertising; competitive analysis and regret reductions; primal-dual approximation arguments; computational complexity, reductions, and neural-network verification. | 6,928 |
| Game theory | Best responses and Nash equilibrium; preference games; threshold strategies; multitask incentives; performative prediction and strategic responses. | 27,323 |
| Markets and matching | Stable and many-to-one matching; deferred acceptance and proposer optimality; continuum markets and admission cutoffs; manipulation and blocking; public decisions. | 10,958 |
| Mechanism design and auctions | Incentive compatibility, truthfulness, and payments; VCG and generalized second-price auctions; position, digital-goods, and combinatorial auctions; welfare and revenue. | 12,942 |
| Social choice and fair division | Preference rankings and aggregation; ranked-choice voting and proportionality; indivisible goods and chores; bounded envy and envy freeness up to any item (EFX); Pareto efficiency. | 31,379 |
| Queueing | Single-server and many-server queues; priority service and processor sharing; waiting times, workloads, and stationary distributions; heavy-traffic limits; congestion externalities and pricing. | 151,201 |
Papers
Current formalizations
Formalized means the selected Lean statements have checked proofs; reports explain their correspondence to the paper. Partially formalized marks unfinished developments. Formalization gap notes flag substantial extra assumptions, simplifications, or missing conclusions in the current formalization.
Human review shows how many paper-to-Lean statement comparisons a human has reviewed, out of the selected results. Automated comparisons and independent agent audits provide additional checks; they do not count as human review.
| Paper | Status | Human review | Lines of Code | Notes |
|---|---|---|---|---|
| College Admissions and the Stability of Marriage by D. Gale and L. S. Shapley; American Mathematical Monthly, 1962. | Formalized | 0/6 | 5,467 | |
| The Regulation of Queue Size by Levying Tolls by P. Naor; Econometrica 37(1), 1969. | Formalized | 0/6 | 922 | Few lines of code as much of machinery is in the shared library |
| The Economics of Matching: Stability and Incentives by Alvin E. Roth; Mathematics of Operations Research, 1982. | Formalized | 0/10 | 11,294 | |
| Optimal Incentive-Compatible Priority Pricing for the M/M/1 Queue by Haim Mendelson and Seungjin Whang; Operations Research 38(5), 1990. | Formalized | 0/5 | 3,616 | Few lines of code as much of machinery is in the shared library |
| A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting by Yoav Freund and Robert E. Schapire; Journal of Computer and System Sciences 55(1):119–139, 1997. | Formalized | 0/14 | 1,136 | Few lines of code as much of machinery is in the shared library |
| Competitive Auctions and Digital Goods by Andrew V. Goldberg, Jason D. Hartline, and Andrew Wright; SODA, 2001. | Formalized | 0/12 | 25,382 | Formalizes the SODA paper; Theorem 8.2 uses the refined monotone-auction wording from the journal version. The continuous-outcome extension assumes a common monotone coupling and integrable revenue; deriving such a coupling from arbitrary marginal offer laws remains outside the checked result Goldberg-Hartline-Karlin-Saks-Wright 2006. |
| AdWords and Generalized Online Matching by Aranyak Mehta, Amin Saberi, Umesh Vazirani, and Vijay V. Vazirani; Journal of the ACM, 2007. | Formalized | 0/50 | 28,430 | |
| Internet Advertising and the Generalized Second-Price Auction by Benjamin Edelman, Michael Ostrovsky, and Michael Schwarz; American Economic Review, 2007. | Formalized | 0/13 | 142,501 | Formalization gap: Theorem 8 uniqueness is proved among common symmetric continuation plans; uniqueness among arbitrary bidder-specific plans remains unproved. |
| Maxing and Ranking with Few Assumptions by Moein Falahatgar, Yi Hao, Alon Orlitsky, Venkatadheeraj Pichapati, and Vaishakh Ravindrakumar; NeurIPS, 2017. | Formalized | 0/23 | 33,818 | |
| Designing Optimal Binary Rating Systems by Nikhil Garg, Ramesh Johari; AISTATS / PMLR 89, 2019. | Formalized | 0/33 | 96,988 | |
| Fundamental Limits of Testing the Independence of Irrelevant Alternatives in Discrete Choice by Arjun Seshadri and Johan Ugander; ACM EC 2019; arXiv version (2020). | Formalized | 0/11 | 10,986 | |
| Iterative Local Voting for Collective Decision-making in Continuous Spaces by Nikhil Garg; Vijay Kamble; Ashish Goel; David Marn; Kamesh Munagala; JAIR 64, 2019. | Formalized | 0/12 | 55,549 | Theorem 3 is proved as a constrained alternative in general and as the original statement under the explicit full-space condition. |
| Who is in Your Top Three? Optimizing Learning in Elections with Many Candidates by Nikhil Garg; Lodewijk Gelauff; Sukolsak Sakshuwong; Ashish Goel; HCOMP, 2019. | Formalized | 0/15 | 38,678 | |
| Axioms for Learning from Pairwise Comparisons by Ritesh Noothigattu, Dominik Peters, and Ariel D. Procaccia; NeurIPS, 2020. | Formalized | 0/10 | 2,507 | |
| Designing Informative Rating Systems: Evidence from an Online Labor Market by Nikhil Garg, Ramesh Johari; Manufacturing & Service Operations Management 23(3), 2020. | Formalized | 0/3 | 9,718 | |
| Fair Allocation through Selective Information Acquisition by William Cai, Johann Gaebler, Nikhil Garg, Sharad Goel; AAAI/ACM AIES, 2020. | Formalized | 0/5 | 12,387 | |
| Performative Prediction by Juan C. Perdomo, Tijana Zrnic, Celestine Mendler-Dünner, and Moritz Hardt; ICML, 2020. | Formalized | 0/11 | 13,293 | Formalization gap: Theorem 3.10 gives eventual high-probability containment in dimension > 2 with uniform moments and stronger regularity; the printed sample-count and burn-in bounds remain unproved. |
| Algorithmic Monoculture and Social Welfare by Jon Kleinberg and Manish Raghavan; PNAS, 2021. | Formalized | 0/49 | 116,134 | |
| Test-optional Policies: Overcoming Strategic Behavior and Informational Gaps by Zhi Liu and Nikhil Garg; EAAMO, 2021. | Formalized | 0/15 | 210,358 | Formalization gap: Theorem 3.1 assumes stability against profitable group entry; voluntary Lemma 4.1 assumes maximal participation. Deriving these refinements from the paper’s equilibrium definition remains unproved. |
| An Algorithmic Framework for Bias Bounties by Ira Globus-Harris, Michael Kearns, and Aaron Roth; ACM FAccT, 2022. | Formalized | 0/15 | 14,755 | |
| Driver Surge Pricing by Nikhil Garg and Hamid Nazerzadeh; Management Science, 2022. | Formalized | 0/16 | 203,502 | |
| Strategic ranking by Lydia T. Liu, Nikhil Garg, Christian Borgs; AISTATS / PMLR 151, 2022. | Formalized | 0/18 | 51,185 | Proposition 3.1’s cutoff monotonicity and Proposition B.2’s optimum use the additional regularity conditions stated in the report. |
| Supply-Side Equilibria in Recommender Systems by Meena Jagadeesan, Nikhil Garg, and Jacob Steinhardt; NeurIPS, 2023. | Formalized | 0/36 | 69,062 | Selected theoretical surface formalized: Theorem 2 covers arbitrary finite-dimensional two-user geometry; Theorems 3–4 prove the corrected strictly acute canonical two-genre construction; Lemma 2 uses the tie-aware corrected characterization. See FINAL_VALIDATION_REPORT.md. |
| Axioms for AI Alignment from Human Feedback by Luise Ge, Daniel Halpern, Evi Micha, Ariel D. Procaccia, Itai Shapira, Yevgeniy Vorobeychik, and Junlin Wu; NeurIPS, 2024. | Formalized | 0/27 | 12,227 | |
| Monoculture in Matching Markets by Kenny Peng, Nikhil Garg; NeurIPS, 2024. | Formalized | 0/13 | 27,861 | |
| Quantifying Spatial Under-reporting Disparities in Resident Crowdsourcing by Zhi Liu, Uma Bhandaram, Nikhil Garg; Nature Computational Science, 2024. | Formalized | 0/19 | 30,658 | |
| Reconciling the Accuracy-Diversity Trade-off in Recommendations by Kenny Peng, Manish Raghavan, Emma Pierson, Jon Kleinberg, and Nikhil Garg; The ACM Web Conference, 2024. | Formalized | 0/25 | 64,182 | |
| Redesigning Service Level Agreements: Equity and Efficiency in City Government Operations by Zhi Liu and Nikhil Garg; ACM EC, 2024. | Formalized | 0/10 | 70,448 | |
| User-item fairness tradeoffs in recommendations by Sophie Greenwood, Sudalakshmee Chiniah, and Nikhil Garg; NeurIPS, 2024. | Formalized | 0/23 | 51,656 | |
| Wisdom and Foolishness of Noisy Matching Markets by Kenny Peng, Nikhil Garg; ACM EC, 2024. | Formalized | 0/19 | 75,243 | Appendix polynomial-rate results use the documented lower-tail correction and finite-second-moment condition. |
| A No Free Lunch Theorem for Human-AI Collaboration by Kenny Peng, Nikhil Garg, Jon Kleinberg; AAAI, 2025. | Formalized | 0/5 | 8,372 | |
| Addressing Discretization-Induced Bias in Demographic Prediction by Evan Dong, Aaron Schein, Yixin Wang, and Nikhil Garg; PNAS Nexus, 2025. | Formalized | 0/9 | 27,842 | |
| Balancing Producer Fairness and Efficiency via Prior-Weighted Rating System Design by Thomas Ma, Michael S. Bernstein, Ramesh Johari, and Nikhil Garg; ICWSM, 2025. | Formalized | 0/2 | 633 | |
| Distortion of AI Alignment: Does Preference Optimization Optimize for Preferences? by Paul Gölz, Nika Haghtalab, and Kunhe Yang; NeurIPS, 2025. | Formalized | 0/16 | 23,210 | |
| Capacity Constraints Make Admissions Processes Less Predictable by Evan Dong; Nikhil Garg; Sarah Dean; AAAI-26 published version, DOI 10.1609/aaai.v40i45.41179. | Formalized | 0/42 | 13,310 | |
| Combatting Gerrymandering with Ranked Choice Voting: an Experimental Analysis of Multi-member Districts in the United States by Nikhil Garg; Wes Gurnee; David Rothschild; David Shmoys; Operations Research, 2026, DOI 10.1287/opre.2024.1167. | Formalized | 0/2 | 17,097 | |
| EFX for Additive Chores: Nonexistence, Pareto Incompatibility, and Bi-Valued Existence by Wentao He and Biaoshuai Tao; arXiv:2606.08872v2, 2026. | Formalized | 3/18 | 40,537 | |
| Truth Revelation in Approximately Efficient Combinatorial Auctions by Daniel Lehmann, Liadan Ita O'Callaghan, and Yoav Shoham; Journal of the ACM, 2002. | Partially formalized | 0/12 | 12,186 | Formalization gap: twelve selected auction-theoretic results are proved; Theorem 6.1's native NP-hardness and NP = ZPP consequences remain unproved. |
| On Approximately Fair Allocations of Indivisible Goods by Richard J. Lipton, Evangelos Markakis, Elchanan Mossel, and Amin Saberi; ACM EC, 2004. | Partially formalized | 0/6 | 80,669 | Formalization gap: selected components of Theorems 2.1, 2.3, and 4.2 are proved; their remaining complexity and asymptotic clauses and Theorems 3.1-3.3 remain unproved. |
| How Many Features Can a Language Model Store Under the Linear Representation Hypothesis? by Nikhil Garg, Jon Kleinberg, Kenny Peng; arXiv:2602.11246v1, 2026. | Partially formalized | 0/11 | 11,528 | Theorem 3, Corollary 8, Theorem 12, and Corollary 13 use the cited Alon rank theorem as an external assumption. |
| A Testable Theory of Atomic Features by Kenny Peng, Jon Kleinberg, and Nikhil Garg; Working paper, 2026. | Formalized | — | 39,335 | Maintained in a separate repository. |
Contribute
Interested in contributing or formalizing your own paper?
To get started in formalizing your own paper, clone the repository and open an LLM agent tool (I use Codex 5.5 or Codex 5.6 Terra, with xhigh thinking). Give the agent the paper link and exact source version, and ask it to follow the repository’s formalization and validation workflow. (Please let me know what your experience is like.)
New paper formalizations should start in a private workflow and be proposed to enter the library through a pull request when ready. For questions or contributions, contact ngarg@cornell.edu.