Simon Mahns — accepted AI-conference papers

2026

Joint-Embedding Predictive Learning of Latent Market States in U.S. Equities

Simon Mahns, Randall Balestriero, Mahmoud Assran

ICML 2026poster

We investigate whether Joint-Embedding Predictive Architectures (JEPA) can learn useful representations of U.S. equity markets. We jointly train a permutation-invariant tokenizer that maps each trading day's unordered per-asset features to a fixed set of learned factor tokens, together with a tempor…

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2026

LLM-as-a-Prophet: Understanding Predictive Intelligence with Prophet Arena

Qingchuan Yang, Simon Mahns, Sida Li, Anri Gu, Jibang Wu, Haifeng Xu

ICLR 2026poster

With the rapid progress of large language models (LLMs) trained on every available piece of data, it becomes increasingly challenging to reliably evaluate their intelligence due to potential data contamination and benchmark overfitting. To overcome these challenges, we investigate a new angle of ben…

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