Tootfinder

Compliance of Implementations with Specification
https://github.com/open-telemetry/opentelemetry-specification/blob/main/spec-compliance-matrix.md

Delsarte duality on subspaces and applications to rank-metric codes and q-matroids
Martino Borello, Olga Polverino, Ferdinando Zullo
https://arxiv.org/abs/2509.24409 https://

Delsarte duality on subspaces and applications to rank-metric codes and q-matroids
We study the interplay between the lattice of F_{q^m}-subspaces and the lattice of F_{q^m}-subspaces of an F_{q^m}-vector space. Introducing notions of weight and defect relative to an F_q-subspace, we analyze the sequence of maximum non-zero defects. We establish a correspondence between subspaces of positive defect and their Delsarte duals, enabling explicit characterizations of the associated sequences of maximum non-zero defects. Our framework unifies several classes of subspaces studied in…

Agentic Specification Generator for Move Programs
Yu-Fu Fu, Meng Xu, Taesoo Kim
https://arxiv.org/abs/2509.24515 https://arxiv.org/pdf/2509.24515

Agentic Specification Generator for Move Programs
While LLM-based specification generation is gaining traction, existing tools primarily focus on mainstream programming languages like C, Java, and even Solidity, leaving emerging and yet verification-oriented languages like Move underexplored. In this paper, we introduce MSG, an automated specification generation tool designed for Move smart contracts. MSG aims to highlight key insights that uniquely present when applying LLM-based specification generation to a new ecosystem. Specifically, MSG …

My first attempt at desoldering a BGA chip and it worked really well. Chip cleaned up nicely as well.
Disclaimer: I don't plan on using this chip or board again. So, I don't need it to work at the end.
#retrocomputing
#desoldering

Global-Local Dirichlet Processes for Identifying Pan-Cancer Subpopulations Using Both Shared and Cancer-Specific Data
Arhit Chakrabarti, Yang Ni, Debdeep Pati, Bani K. Mallick
https://arxiv.org/abs/2509.22884

Global-Local Dirichlet Processes for Identifying Pan-Cancer Subpopulations Using Both Shared and Cancer-Specific Data
We consider the problem of clustering grouped data for which the observations may include group-specific variables in addition to the variables that are shared across groups. This type of data is common in cancer genomics where the molecular information is usually accompanied by cancer-specific clinical information. Existing grouped clustering methods only consider the shared variables, thereby ignoring valuable information from the cancer-specific variables. To allow for these cancer-specific …

Parametrized complexity of relations between multidimensional subshifts
Nicanor Carrasco-Vargas, Benjamin Hellouin de Menibus, R\'emi Pallen
https://arxiv.org/abs/2509.24343

Parametrized complexity of relations between multidimensional subshifts
We study the parametrized complexity of fundamental relations between multidimensional subshifts, such as equality, conjugacy, inclusion, and embedding, for subshifts of finite type (SFTs) and effective subshifts. We build on previous work of E. Jeandel and P. Vanier on the complexity of these relations as two-input problems, by fixing one subshift as parameter and taking the other subshift as input. We study the impact of various dynamical properties related to periodicity, minimality, finite …

"regardless of the underlying technology, the pursuit of artificial general intelligence is not necessarily the most efficient route to useful applications. Artificial specific intelligence (AI approaches focused on a specific domain, such as the Nobel prize-winning, protein-folding algorithm, AlphaFold2) gives more reliable and transparent results by combining the subtle pattern detection at which GenAI excels with explicitly encoded, domain-specific knowledge."

Hierarchical Error Correction for Large Language Models: A Systematic Framework for Domain-Specific AI Quality Enhancement
Zhilong Zhao, Yindi Liu
https://arxiv.org/abs/2509.24841

Hierarchical Error Correction for Large Language Models: A Systematic Framework for Domain-Specific AI Quality Enhancement
Large Language Models face significant performance challenges in specialized domains, with state-of-the-art models achieving only 45.9% accuracy on medical coding tasks. This study proposes a Hierarchical Error Correction (HEC) framework that addresses domain-specific AI limitations through systematic error analysis and targeted intervention strategies. We analyze error patterns across four specialized domains and find that AI errors follow consistent hierarchical structures: Knowledge-layer …