Tootfinder

Efficient Prior Sensitivity and Tipping-point Analysis for Medical Research: Revisiting Sampling Importance Resampling
Tomohiro Ohigashi, Shonosuke Sugasawa
https://arxiv.org/abs/2510.10034

Efficient Prior Sensitivity and Tipping-point Analysis for Medical Research: Revisiting Sampling Importance Resampling
Bayesian methods have received increasing attention in medical research, where sensitivity analysis of prior distributions is essential. Such analyses typically require the evaluation of the posterior distribution of a parameter under multiple alternative prior settings. When the posterior distribution of the parameter of interest cannot be derived analytically, the standard approach is to re-fit the Markov chain Monte Carlo (MCMC) algorithm for each setting, which incurs substantial computatio…

Wavefunction Flows: Efficient Quantum Simulation of Continuous Flow Models
David Layden, Ryan Sweke, Vojt\v{e}ch Havl\'i\v{c}ek, Anirban Chowdhury, Kirill Neklyudov
https://arxiv.org/abs/2510.08462

Wavefunction Flows: Efficient Quantum Simulation of Continuous Flow Models
Flow models are a cornerstone of modern machine learning. They are generative models that progressively transform probability distributions according to learned dynamics. Specifically, they learn a continuous-time Markov process that efficiently maps samples from a simple source distribution into samples from a complex target distribution. We show that these models are naturally related to the Schrödinger equation, for an unusual Hamiltonian on continuous variables. Moreover, we prove that the…

Lifted Heston Model: Efficient Monte Carlo Simulation with Large Time Steps
Nicola F. Zaugg, Lech A. Grzelak
https://arxiv.org/abs/2510.08805 https://arxiv…

Lifted Heston Model: Efficient Monte Carlo Simulation with Large Time Steps
The lifted Heston model is a stochastic volatility model emerging as a Markovian lift of the rough Heston model and the class of rough volatility processes. The model encodes the path dependency of volatility on a set of N square-root state processes driven by a common stochastic factor. While the system is Markovian, simulation schemes such as the Euler scheme exist, but require a small-step, multidimensional simulation of the state processes and are therefore numerically challenging. We propo…

Pseudo-MDPs: A Novel Framework for Efficiently Optimizing Last Revealer Seed Manipulations in Blockchains
Maxime Reynouard
https://arxiv.org/abs/2510.07080 https://

Pseudo-MDPs: A Novel Framework for Efficiently Optimizing Last Revealer Seed Manipulations in Blockchains
This study tackles the computational challenges of solving Markov Decision Processes (MDPs) for a restricted class of problems. It is motivated by the Last Revealer Attack (LRA), which undermines fairness in some Proof-of-Stake (PoS) blockchains such as Ethereum (\$400B market capitalization). We introduce pseudo-MDPs (pMDPs) a framework that naturally models such problems and propose two distinct problem reductions to standard MDPs. One problem reduction provides a novel, counter-intuitive per…

Burning wood indoors could cause damage in a similar way to cigarette smoke.
https://www.manchestereveningnews.co.uk/news/uk-news/warning-issued-uk-households-who-32588072

Warning issued to UK households who use wood burners
Many UK homes have wood burners as a modern, efficient alternative to fires

Efficient tensor-network simulations of weakly-measured quantum circuits
Darren Pereira, Leonardo Banchi
https://arxiv.org/abs/2510.07211 https://arxiv.org…

Efficient tensor-network simulations of weakly-measured quantum circuits
We present a tensor-network-based method for simulating a weakly-measured quantum circuit. In particular, we use a Markov chain to efficiently sample measurements and contract the tensor network, propagating their effect forward along the spatial direction. Applications of our algorithm include validating quantum computers (capable of mid-circuit measurements) in regimes of easy classical simulability, and studying generative-machine-learning applications, where sampling from complex stochastic…

GLASS Flows: Transition Sampling for Alignment of Flow and Diffusion Models
Peter Holderrieth, Uriel Singer, Tommi Jaakkola, Ricky T. Q. Chen, Yaron Lipman, Brian Karrer
https://arxiv.org/abs/2509.25170

GLASS Flows: Transition Sampling for Alignment of Flow and Diffusion Models
The performance of flow matching and diffusion models can be greatly improved at inference time using reward alignment algorithms, yet efficiency remains a major limitation. While several algorithms were proposed, we demonstrate that a common bottleneck is the sampling method these algorithms rely on: many algorithms require to sample Markov transitions via SDE sampling, which is significantly less efficient and often less performant than ODE sampling. To remove this bottleneck, we introduce GL…

Computable measures of non-Markovianity for Gaussian free fermion systems
Giuliano Chiriac\`o
https://arxiv.org/abs/2509.25953 https://arxiv.org/pdf/2509.2…

Computable measures of non-Markovianity for Gaussian free fermion systems
We investigate measures of non-Markovianity in open quantum systems governed by Gaussian free fermionic dynamics. Standard indicators of non-Markovian behavior, such as the BLP and LFS measures, are revisited in this context. We show that for Gaussian states, trace-based distances -- specifically the Hilbert-Schmidt norm -- and second-order Rényi mutual information can be efficiently expressed in terms of two-point correlation functions, enabling practical computation even in systems where the…

#Jeavons
Don’t Forget to Ask: What Happens to the Savings? - resilience
https://www.resilience.org/stories/2025-11-26/dont-forget-to-ask-what-happe…

Don’t Forget to Ask: What Happens to the Savings?
The proposal for Degrowth calls for an intentional downscaling of the global economy to achieve both ecological sustainability and social justice. A degrowth transition would help ensure that ecological practices actually displace destructive ones, and that the savings from efficiency measures are truly saved.

To Distill or Decide? Understanding the Algorithmic Trade-off in Partially Observable Reinforcement Learning
Yuda Song, Dhruv Rohatgi, Aarti Singh, J. Andrew Bagnell
https://arxiv.org/abs/2510.03207

To Distill or Decide? Understanding the Algorithmic Trade-off in Partially Observable Reinforcement Learning
Partial observability is a notorious challenge in reinforcement learning (RL), due to the need to learn complex, history-dependent policies. Recent empirical successes have used privileged expert distillation--which leverages availability of latent state information during training (e.g., from a simulator) to learn and imitate the optimal latent, Markovian policy--to disentangle the task of "learning to see" from "learning to act". While expert distillation is more computationally efficient tha…