Tootfinder

Kiedy piszesz bibliotekę w #RustLang, bo chcesz bezpiecznego kodu, a zamiast tego dostajesz jakiś paskudny #heisenbug z przekłamaniem danych.
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Spouse is a psychotherapist. I am a heavily-interdisciplinary computer programmer. We both work with complex systems.
We are diagnosing why the heat pump mini-split isn't working as expected
Their first hypothesis, of course, is "communication breakdown with partner unit"
Mine is "configuration error root-caused to bad UX design"
... We were both right

On Hypothesis H of Rudnick and Sarnak
Yujiao Jiang
https://arxiv.org/abs/2507.20653 https://arxiv.org/pdf/2507.20653

On Hypothesis H of Rudnick and Sarnak
We prove Hypothesis H in full generality for ${\rm GL}_n$ over any number field. This result is a consequence of our stronger effective bound on Euler products involving Rankin--Selberg coefficients at prime ideal powers. The proof rests on a new analytic method, which employs a power sieve over number fields and an iterative argument to bypass the functoriality barrier that had restricted prior results to $n\leq 4$. As applications, we unconditionally establish the GUE statistics for automorph…

A Comparison of the Bayesian Posterior Probability and the Frequentist $p$-Value in Testing Equivalence Hypotheses
Daniel Ochieng
https://arxiv.org/abs/2507.19685 https://

A Comparison of the Bayesian Posterior Probability and the Frequentist $p$-Value in Testing Equivalence Hypotheses
Equivalence tests, otherwise known as parity or similarity tests, are frequently used in ``bioequivalence studies" to establish practical equivalence rather than the usual statistical significant difference. In this article, we propose an equivalence test using both the $p$-value and a Bayesian procedure by computing the posterior probability that the null hypothesis is true. Since these posterior probabilities follow the uniform $[0,1]$ distribution under the null hypothesis, we use them in a …

HEAL: A Hypothesis-Based Preference-Aware Analysis Framework
Yifu Huo, Chenglong Wang, Qiren Zhu, Shunjie Xing, Tong Xiao, Chunliang Zhang, Tongran Liu, Jinbo Zhu
https://arxiv.org/abs/2508.19922

HEAL: A Hypothesis-Based Preference-Aware Analysis Framework
Preference optimization methods like DPO have achieved remarkable performance in LLM alignment. However, the evaluation for these methods relies on a single response and overlooks other potential outputs, which could also be generated in real-world applications within this hypothetical space. To address this issue, this paper presents a \textbf{H}ypothesis-based Pr\textbf{E}ference-aware \textbf{A}na\textbf{L}ysis Framework (HEAL), a novel evaluation paradigm that formulates preference alignmen…

K-PACT: Kernel Planning for Adaptive Context Switching - A Framework for Clustering, Placement, and Prefetching in Spectrum Sensing
H. Umut Suluhan, Jiahao Lin, Serhan Gener, Chaitali Chakrabarti, Umit Ogras, Ali Akoglu
https://arxiv.org/abs/2507.19662

K-PACT: Kernel Planning for Adaptive Context Switching - A Framework for Clustering, Placement, and Prefetching in Spectrum Sensing
Efficient wideband spectrum sensing requires rapid evaluation and re-evaluation of signal presence and type across multiple subchannels. These tasks involve multiple hypothesis testing, where each hypothesis is implemented as a decision tree workflow containing compute-intensive kernels, including FFT, matrix operations, and signal-specific analyses. Given dynamic nature of the spectrum environment, ability to quickly switch between hypotheses is essential for maintaining low-latency, high-thro…

Repairing vulnerabilities without invisible hands. A differentiated replication study on LLMs
Maria Camporese, Fabio Massacci
https://arxiv.org/abs/2507.20977 https://

Repairing vulnerabilities without invisible hands. A differentiated replication study on LLMs
Background: Automated Vulnerability Repair (AVR) is a fast-growing branch of program repair. Recent studies show that large language models (LLMs) outperform traditional techniques, extending their success beyond code generation and fault detection. Hypothesis: These gains may be driven by hidden factors -- "invisible hands" such as training-data leakage or perfect fault localization -- that let an LLM reproduce human-authored fixes for the same code. Objective: We replicate prior AVR studi…

A Topology-Based Machine Learning Model Decisively Outperforms Flux Balance Analysis in Predicting Metabolic Gene Essentiality
Justin Boone
https://arxiv.org/abs/2507.20406 http…

A Topology-Based Machine Learning Model Decisively Outperforms Flux Balance Analysis in Predicting Metabolic Gene Essentiality
Background: The rational identification of essential genes is a cornerstone of drug discov- ery, yet standard computational methods like Flux Balance Analysis (FBA) often struggle to produce accurate predictions in complex, redundant metabolic networks. Hypothesis: We hypothesized that the topological structure of a metabolic network con- tains a more robust predictive signal for essentiality than functional simulations alone. Methodology: To test this hypothesis, we developed a machine learnin…

A Truly Subcubic Combinatorial Algorithm for Induced 4-Cycle Detection
Amir Abboud, Shyan Akmal, Nick Fischer
https://arxiv.org/abs/2507.18845 https://arxi…

A Truly Subcubic Combinatorial Algorithm for Induced 4-Cycle Detection
We present the first truly subcubic, combinatorial algorithm for detecting an induced $4$-cycle in a graph. The running time is $O(n^{2.84})$ on $n$-node graphs, thus separating the task of detecting induced $4$-cycles from detecting triangles, which requires $n^{3-o(1)}$ time combinatorially under the popular BMM hypothesis. Significant work has gone into characterizing the exact time complexity of induced $H$-detection, relative to the complexity of detecting cliques of various sizes. Prior…

Detecting zeros of Dirichlet $L$-functions via the Riemann zeta-function
Masatoshi Suzuki
https://arxiv.org/abs/2508.17701 https://arxiv.org/pdf/2508.17701…

Detecting zeros of Dirichlet $L$-functions via the Riemann zeta-function
Assuming the Riemann hypothesis, we show that a certain vertical distribution of the nontrivial zeros of the Riemann zeta-function is equivalent to the generalized Riemann hypothesis for Dirichlet $L$-functions. Furthermore, under both the Riemann hypothesis and the generalized Riemann hypothesis, we show that the nontrivial zeros of Dirichlet $L$-functions can be detected through those of the Riemann zeta-function.