Tootfinder

Optical response of a binary atomic system with incoherent gain
L. Acevedo, J. S\'anchez-C\'anovas, M. Donaire
https://arxiv.org/abs/2506.21177 htt…

Optical response of a binary atomic system with incoherent gain
We study the optical response of a binary system of identical atoms in which one of them is excited by an incoherent pump. %We study the properties of photon scattering, absorption and emission, together with the time evolution of the atomic system. %This allows us to characterize and eventually manipulate the state of the system, paving the way for prospective applications in quantum information processing. Applying the diagrammatic formalism developed in Donaire [Phys. Rev. A104, 043704 (2021…

Institutional Noise, Strategic Deviation, and Intertemporal Collapse: A Formal Model of Miner Behaviour under Protocol Uncertainty
Craig Steven Wright
https://arxiv.org/abs/2506.20992

Institutional Noise, Strategic Deviation, and Intertemporal Collapse: A Formal Model of Miner Behaviour under Protocol Uncertainty
This paper develops a formal game-theoretic model to examine how protocol mutability disrupts cooperative mining behaviour in blockchain systems. Using a repeated game framework with stochastic rule shocks, we show that even minor uncertainty in institutional rules increases time preference and induces strategic deviation. Fixed-rule environments support long-term investment and stable equilibrium strategies; in contrast, mutable protocols lead to short-termism, higher discounting, and collapse…

Potemkin Understanding in Large Language Models
Marina Mancoridis, Bec Weeks, Keyon Vafa, Sendhil Mullainathan
https://arxiv.org/abs/2506.21521 https://arxiv.org/pdf/2506.21521 https://arxiv.org/html/2506.21521
arXiv:2506.21521v1 Announce Type: new
Abstract: Large language models (LLMs) are regularly evaluated using benchmark datasets. But what justifies making inferences about an LLM's capabilities based on its answers to a curated set of questions? This paper first introduces a formal framework to address this question. The key is to note that the benchmarks used to test LLMs -- such as AP exams -- are also those used to test people. However, this raises an implication: these benchmarks are only valid tests if LLMs misunderstand concepts in ways that mirror human misunderstandings. Otherwise, success on benchmarks only demonstrates potemkin understanding: the illusion of understanding driven by answers irreconcilable with how any human would interpret a concept. We present two procedures for quantifying the existence of potemkins: one using a specially designed benchmark in three domains, the other using a general procedure that provides a lower-bound on their prevalence. We find that potemkins are ubiquitous across models, tasks, and domains. We also find that these failures reflect not just incorrect understanding, but deeper internal incoherence in concept representations.
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Pasadena officially condemns immigration raids with vote on formal statement – NBC Los Angeles
https://www.nbclosangeles.com/news/local/pasadena-votes-to-officially-condemns-immigration-raids/3730964/

Bulls vs Bears: a Trinomial Model of a Financial Asset
Nahuel I. Arca
https://arxiv.org/abs/2505.18723 https://arxiv.org/pdf/2505.187…

Bulls vs Bears: a Trinomial Model of a Financial Asset
We present a variation of the well-known binomial model of asset prices. This variation incorporates a bound to short-selling, inspired by a model from Gunduz Caginalp[2]. We formalize this model and prove a formula for all the moments of the logarithmic returns. We also derive a formula for the case with infinitely many investors. As an application of the model, we show how to compute parameters in order to approximate given moments, enabling the modeling of skewness and excess kurtosis. Final…

The Composition of Digital Twins for Systems-of-Systems: a Systematic Literature Review
Mennatullah T. Khedr, John S. Fitzgerald
https://arxiv.org/abs/2506.20435

The Composition of Digital Twins for Systems-of-Systems: a Systematic Literature Review
Digital Twins (DTs) are increasingly used to model complex systems, especially in Cyber-Physical Systems (CPS) and System-of-Systems (SoS), where effective integration is key. This systematic literature review investigates DT composition and verification and validation (V&V) methodologies. Analyzing 21 studies from 2022-2024, we examined composition mechanisms, SoS characteristics, and V&V formality, scope, and challenges. While composition is discussed, formalization is limited. V&V approaches…

I increasingly think that we need mandatory formal training for PhD students in “research as a profession” (as it exists in other countries, apparently).
Yes, submitting a paper to a conference (in particular as a first author) constitutes an commitment to present the paper if it’s accepted. When working with coauthors, no, you can’t tacitly assume that they’ll take care of it.
And yes, a workshop on Monday from 09:00 to 12:30 takes place during “normal working hours.”

Stochastic Preconditioning for Neural Field Optimization
Selena Ling, Merlin Nimier-David, Alec Jacobson, Nicholas Sharp
https://arxiv.org/abs/2505.20473 h…

Stochastic Preconditioning for Neural Field Optimization
Neural fields are a highly effective representation across visual computing. This work observes that fitting these fields is greatly improved by incorporating spatial stochasticity during training, and that this simple technique can replace or even outperform custom-designed hierarchies and frequency space constructions. The approach is formalized as implicitly operating on a blurred version of the field, evaluated in-expectation by sampling with Gaussian-distributed offsets. Querying the blurr…

hdbayes: An R Package for Bayesian Analysis of Generalized Linear Models Using Historical Data
Ethan M. Alt, Xinxin Chen, Luiz M. Carvalho, Joseph G. Ibrahim
https://arxiv.org/abs/2506.20060

hdbayes: An R Package for Bayesian Analysis of Generalized Linear Models Using Historical Data
There has been increased interest in the use of historical data to formulate informative priors in regression models. While many such priors for incorporating historical data have been proposed, adoption is limited due to access to software. Where software does exist, the implementations between different methods could be vastly different, making comparisons between methods difficult. In this paper, we introduce the R package hdbayes, an implementation of the power prior, normalized power prior…

COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees
Zhiyuan Wang, Jinhao Duan, Qingni Wang, Xiaofeng Zhu, Tianlong Chen, Xiaoshuang Shi, Kaidi Xu
https://arxiv.org/abs/2506.20178

COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees
Uncertainty quantification (UQ) for foundation models is essential to identify and mitigate potential hallucinations in automatically generated text. However, heuristic UQ approaches lack formal guarantees for key metrics such as the false discovery rate (FDR) in selective prediction. Previous work adopts the split conformal prediction (SCP) framework to ensure desired coverage of admissible answers by constructing prediction sets, but these sets often contain incorrect candidates, limiting the…