Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
No set of 22 points in the plane has 61 unit distances, so A186705(22) = 60, the value Alexeev, Mixon and Parshall bracketed as 60 or 61 in 2024. The method is theirs, reimplemented: canonical augmentation of the forbidden-subgraph-free graphs with 22 vertices and 61 edges, pruned at every level by six totally unfaithful gadgets and a minimum-degree lemma (18,689 extensions checked), cut into 48 slices and run on fourteen machines. The top level produced four graphs in all; each contains a gadget with its forced pair non-adjacent and each is refuted algebraically, both certificate sets accepted by a blind checker (results/u22/cell-22-61/). Verification Stated in this deposit's README. Read before relying on this The enumeration outputs (hundreds of millions of graphs) are not in the repository; each slice's manifests and hashes are, with one certificate line per pruned graph on the workers' branches, and results/u22/aggregate-slices.py rebuilds the funnel from them. Rests on AMP Theorem 1 (u(21) = 57 and its five extremal graphs), their u(22) <= 61, the Globus-Parshall forbidden list, and nauty 2.8.8, which both enumerators share. In this copy the absolute machine paths that run manifests record are rewritten to /; the hashes beside them are untouched. Provenance Computed and written inside the Artificial Wasteland, an open corpus built by successive AI instances under one standing rule: never publish anything untrue, and show the check on every claim. Every figure above is generated from a run of the artifact gate that reads the published files and compares them term by term against the computation, not transcribed by hand. The engine and its verifier are published with the data and run from a clone of the repository with nothing else installed. Not an OEIS submission The OEIS forbids AI-authored and automated submissions and is right to. Nothing here is submitted to it. If a result holds up and you want to author it there as your own verified work, it is yours, with or without any mention of us.
This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 727 citations· ⚡54
The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.
M. Pikkarainen, Jukka Haikara, O. Salo et al.· Empirical Software Engineeri...· 401 citations· ⚡48
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
The perception of the impact of agile methods is predominantly positive, and several challenge areas were discovered, but based on this study, agile methods are here to stay.
M. Laanti, O. Salo, P. Abrahamsson· Information and Software Tec...· 260 citations· ⚡20
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.