Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
As a continuation of our previous work, we publish the paper resolving the open Automata Exchange Problem 24.04(A) by showing that the problem is decidable. Unlike Problem 24.04(B), Problem 24.04(A) was not stated precisely enough to make clear whether epsilon-moves were included in the model. For this reason, we solve both interpretations and prove decidability both with and without epsilon-moves. We prove that language equivalence between a deterministic zero-testing one-counter automaton and a nondeterministic one-counter net is decidable. The work develops an exact shortest-counterexample framework based on forward reachability envelopes and backward completion requirements, deterministic zero-cut decompositions, effective compactness for positive first-return corridors, well-quasi-ordered zero-state trees, and terminal-drain reductions, yielding a computable small-witness bound for the difficult inclusion from the deterministic zero-testing machine into the nondeterministic net. The reverse inclusion is reduced independently to exact reachability in a vector addition system with one zero test. We further show that the decidability result survives a substantially stronger setting with silent transitions: deterministic epsilon-moves on the zero-testing side are handled through stable/reset normalization, while arbitrary epsilon-moves of the nondeterministic net are compiled into an exact finite threshold-affine weak-macro calculus with pumping thresholds, omega-valued envelopes, and a guarded-macro compactness transfer theorem. Based on our literature search through September 2026, we are not aware of a prior published decidability result for this exact asymmetric language-equivalence problem; the paper therefore resolves the standard real-time formulation of Problem 24.04(A) and additionally establishes the epsilon-move extension.
Supporting data, adapters, predictions and code for the article *Low-Cost LoRA Fine-Tuning of Small Language Models for Multi-Step Arithmetic Reasoning* by Jake O'Grady, Asena Isik Gürhan, Chee Fong Ting and Effirul Ramlan (University of Galway). We generated 20,000 GSM8K-derived arithmetic problems with step-by-step s...
O'Grady, Jake, Gürhan, Asena Isik, Chee, Fong Ting et al.· Zenodo (CERN European Organi...· 465 citations
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
Related blog posts
Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.