Tootfinder

Learning Temporal Abstractions via Variational Homomorphisms in Option-Induced Abstract MDPs
Chang Li, Yaren Zhang, Haoran Lv, Qiong Cao, Chao Xue, Xiaodong He
https://arxiv.org/abs/2507.16473

Learning Temporal Abstractions via Variational Homomorphisms in Option-Induced Abstract MDPs
Large Language Models (LLMs) have shown remarkable reasoning ability through explicit Chain-of-Thought (CoT) prompting, but generating these step-by-step textual explanations is computationally expensive and slow. To overcome this, we aim to develop a framework for efficient, implicit reasoning, where the model "thinks" in a latent space without generating explicit text for every step. We propose that these latent thoughts can be modeled as temporally-extended abstract actions, or options, with…

Modeling Loss-Versus-Rebalancing in Automated Market Makers via Continuous-Installment Options
Srisht Fateh Singh, Reina Ke Xin Li, Samuel Gaskin, Yuntao Wu, Jeffrey Klinck, Panagiotis Michalopoulos, Zissis Poulos, Andreas Veneris
https://arxiv.org/abs/2508.02971

Modeling Loss-Versus-Rebalancing in Automated Market Makers via Continuous-Installment Options
This paper mathematically models a constant-function automated market maker (CFAMM) position as a portfolio of exotic options, known as perpetual American continuous-installment (CI) options. This model replicates an AMM position's delta at each point in time over an infinite time horizon, thus taking into account the perpetual nature and optionality to withdraw of liquidity provision. This framework yields two key theoretical results: (a) It proves that the AMM's adverse-selection cost, loss-v…