Newest Papers
Maximizing Social Welfare with Side Payments
Ivan Geffner, Caspar Oesterheld, Vincent Conitzer
arXiv·2025
We examine normal-form games in which players may \emph{pre-commit} to outcome-contingent transfers before choosing their actions. In the one-shot version of this model, Jackson and Wilkie showed that side contracting can backfire: even a game with a Pareto-optimal Nash equilibrium can devolve into inefficient equilibria once unbounded, simultaneous commitments are allowed. The root cause is a prisoner's dilemma effect, where each player can exploit her commitment power to reshape the equilibrium in her favor, harming overall welfare.
To circumvent this problem we introduce a \emph{staged-commitment} protocol. Players may pledge transfers only in small, capped increments over multiple rounds, and the phase continues only with unanimous consent. We prove that, starting from any finite game $Γ$ with a non-degenerate Nash equilibrium $\vecσ$, this protocol implements every welfare-maximizing payoff profile that \emph{strictly} Pareto-improves $\vecσ$. Thus, gradual and bounded commitments restore the full efficiency potential of side payments while avoiding the inefficiencies identified by Jackson and Wilkie.
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View paper →Building a web search engine from scratch in two months with 3 billion neural embeddings
Wilson Lin
Wilson Lin·2025
End-to-end deep dive of the project, spanning a large GPU cluster, distributed RocksDB, and terabytes of sharded HNSW.
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View paper →The Erotics of Intelligence - Helen Higgins
Helen Higgins
Substack·2025
On synapses, sentences, and the thrill of being undressed by thought.
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View paper →Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation
Yue Liao, Pengfei Zhou, Siyuan Huang, Donglin Yang, Shengcong Chen, Yuxin Jiang, Yue Hu, Jingbin Cai, Si Liu, Jianlan Luo, Liliang Chen, Shuicheng Yan, Maoqing Yao, Guanghui Ren
arXiv·2025
We introduce Genie Envisioner (GE), a unified world foundation platform for robotic manipulation that integrates policy learning, evaluation, and simulation within a single video-generative framework. At its core, GE-Base is a large-scale, instruction-conditioned video diffusion model that captures the spatial, temporal, and semantic dynamics of real-world robotic interactions in a structured latent space. Built upon this foundation, GE-Act maps latent representations to executable action trajectories through a lightweight, flow-matching decoder, enabling precise and generalizable policy inference across diverse embodiments with minimal supervision. To support scalable evaluation and training, GE-Sim serves as an action-conditioned neural simulator, producing high-fidelity rollouts for closed-loop policy development. The platform is further equipped with EWMBench, a standardized benchmark suite measuring visual fidelity, physical consistency, and instruction-action alignment. Together, these components establish Genie Envisioner as a scalable and practical foundation for instruction-driven, general-purpose embodied intelligence. All code, models, and benchmarks will be released publicly.
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View paper →None of us are easy to be with
hasif 💌
Substack·2025
Sometimes the hardest person to be with is yourself.
★ 4.0 (1)
View paper →The Term 'Agent' Has Been Diluted Beyond Utility and Requires Redefinition
Brinnae Bent
arXiv·2025
The term 'agent' in artificial intelligence has long carried multiple interpretations across different subfields. Recent developments in AI capabilities, particularly in large language model systems, have amplified this ambiguity, creating significant challenges in research communication, system evaluation and reproducibility, and policy development. This paper argues that the term 'agent' requires redefinition. Drawing from historical analysis and contemporary usage patterns, we propose a framework that defines clear minimum requirements for a system to be considered an agent while characterizing systems along a multidimensional spectrum of environmental interaction, learning and adaptation, autonomy, goal complexity, and temporal coherence. This approach provides precise vocabulary for system description while preserving the term's historically multifaceted nature. After examining potential counterarguments and implementation challenges, we provide specific recommendations for moving forward as a field, including suggestions for terminology standardization and framework adoption. The proposed approach offers practical tools for improving research clarity and reproducibility while supporting more effective policy development.
★ 3.0 (1)
View paper →Live Music Models
Lyria Team, Antoine Caillon, Brian McWilliams, Cassie Tarakajian, Ian Simon, Ilaria Manco, Jesse Engel, Noah Constant, Pen Li, Timo I. Denk, Alberto Lalama, Andrea Agostinelli, Anna Huang, Ethan Manilow, George Brower, Hakan Erdogan, Heidi Lei, Itai Rolnick, Ivan Grishchenko, Manu Orsini, Matej Kastelic, Mauricio Zuluaga, Mauro Verzetti, Michael Dooley, Ondrej Skopek, Rafael Ferrer, Zalán Borsos, Äaron van den Oord, Douglas Eck, Eli Collins, Jason Baldridge, Tom Hume, Chris Donahue, Kehang Han, Adam Roberts
arXiv·2025
We introduce a new class of generative models for music called live music models that produce a continuous stream of music in real-time with synchronized user control. We release Magenta RealTime, an open-weights live music model that can be steered using text or audio prompts to control acoustic style. On automatic metrics of music quality, Magenta RealTime outperforms other open-weights music generation models, despite using fewer parameters and offering first-of-its-kind live generation capabilities. We also release Lyria RealTime, an API-based model with extended controls, offering access to our most powerful model with wide prompt coverage. These models demonstrate a new paradigm for AI-assisted music creation that emphasizes human-in-the-loop interaction for live music performance.
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View paper →On the Stability of Racial Capitalism
Liam Kofi Bright, Nathan Gabriel, Cailin O'Connor, Olúfémi O. Táíwò
Ergo an Open Access Journal of Philosophy·2025·1 citations
<jats:p>What is the connection between capitalism and racial hierarchy? In line with the tradition known as ‘the theory of racial capitalism’ we show that the latter can functionally support the former. As a social construction, race has just those features which allow it to facilitate the sort of stable, inequitable distributions of resources that tend to emerge in capitalist systems. We support this claim using techniques from evolutionary game theory and cultural evolutionary theory, and end by discussing the normative political consequences of this relationship.</jats:p>
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View paper →‘Written in the Style of’: ChatGPT and the Literary Canon · Issue 7.4, Fall 2025
Gabi Kirilloff, Claudia Carroll, Zeina Daboul, Arianwyn Frank, Razi Khan, Marielle Hinrichs-Morrow, Rebecca Weingart
Harvard Data Science Review·2025
No abstract available
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View paper →Unravelling the Probabilistic Forest: Arbitrage in Prediction Markets
Oriol Saguillo, Vahid Ghafouri, Lucianna Kiffer, Guillermo Suarez-Tangil
arXiv·2025
Polymarket is a prediction market platform where users can speculate on future events by trading shares tied to specific outcomes, known as conditions. Each market is associated with a set of one or more such conditions. To ensure proper market resolution, the condition set must be exhaustive -- collectively accounting for all possible outcomes -- and mutually exclusive -- only one condition may resolve as true. Thus, the collective prices of all related outcomes should be \$1, representing a combined probability of 1 of any outcome. Despite this design, Polymarket exhibits cases where dependent assets are mispriced, allowing for purchasing (or selling) a certain outcome for less than (or more than) \$1, guaranteeing profit. This phenomenon, known as arbitrage, could enable sophisticated participants to exploit such inconsistencies.
In this paper, we conduct an empirical arbitrage analysis on Polymarket data to answer three key questions: (Q1) What conditions give rise to arbitrage (Q2) Does arbitrage actually occur on Polymarket and (Q3) Has anyone exploited these opportunities. A major challenge in analyzing arbitrage between related markets lies in the scalability of comparisons across a large number of markets and conditions, with a naive analysis requiring $O(2^{n+m})$ comparisons. To overcome this, we employ a heuristic-driven reduction strategy based on timeliness, topical similarity, and combinatorial relationships, further validated by expert input.
Our study reveals two distinct forms of arbitrage on Polymarket: Market Rebalancing Arbitrage, which occurs within a single market or condition, and Combinatorial Arbitrage, which spans across multiple markets. We use on-chain historical order book data to analyze when these types of arbitrage opportunities have existed, and when they have been executed by users. We find a realized estimate of 40 million USD of profit extracted.
★ 4.0 (1)
View paper →Shtetl-Optimized » Blog Archive » ChatGPT and the Meaning of Life: Guest Post by Harvey Lederman
Unknown Author
Shtetl-Optimized·2025
Scott Aaronson's Brief Foreword: Harvey Lederman is a distinguished analytic philosopher who moved from Princeton to UT Austin a few years ago. Since his arrival, he's become one of my best friends among the UT professoriate. He's my favorite kind of philosopher, the kind who sees scientists as partners in discovering the truth, and also…
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View paper →Trustworthy scientific inference with generative models
James Carzon, Luca Masserano, Joshua D. Ingram, Alex Shen, Antonio Carlos Herling Ribeiro Junior, Tommaso Dorigo, Michele Doro, Joshua S. Speagle, Rafael Izbicki, Ann B. Lee
arXiv·2025
Generative artificial intelligence (AI) excels at producing complex data structures (text, images, videos) by learning patterns from training examples. Across scientific disciplines, researchers are now applying generative models to "inverse problems" to directly predict hidden parameters from observed data along with measures of uncertainty. While these predictive or posterior-based methods can handle intractable likelihoods and large-scale studies, they can also produce biased or overconfident conclusions even without model misspecifications. We present a solution with Frequentist-Bayes (FreB), a mathematically rigorous protocol that reshapes AI-generated posterior probability distributions into (locally valid) confidence regions that consistently include true parameters with the expected probability, while achieving minimum size when training and target data align. We demonstrate FreB's effectiveness by tackling diverse case studies in the physical sciences: identifying unknown sources under dataset shift, reconciling competing theoretical models, and mitigating selection bias and systematics in observational studies. By providing validity guarantees with interpretable diagnostics, FreB enables trustworthy scientific inference across fields where direct likelihood evaluation remains impossible or prohibitively expensive.
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