Abstract This paper characterizes recurrence and distinguishable-history capacity in finite declared identity regimes. For the class-indexed observable, the central quantity is the logarithm of the spectral radius of the reachable recurrent subgraph. A recurrence converse bounds sound retention codes by this capacity, with matching attainability under the declared conventions, and separates extinction, bare recurrence and branching recurrence. For the continuing verdict-only channel, finite determinization gives an exact computable capacity. Every finite regime lies strictly below one bit per step, while an explicit family approaches that ceiling. A separate stopped-graph construction characterizes the terminating channel: operational words are label-images of walks, at most two words occur at each positive length, and the asymptotic rate is zero or negative infinity according to a reachable-cycle test. For transitions with nonnegative costs and budgets enforced at every prefix, the paper establishes a sublinear-budget characterization, a fixed-tilt spectral converse, and a restricted attainability result for a single recurrent block in the large-buffer limit. Countermodels show why feasible entry, expenditure order and the counted language cannot be omitted. The results are scoped to declared finite models; they do not establish physical resource costs or general autonomous self-improvement. Broader composition and capacity questions remain explicitly open.
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
The ongoing work building a Raspberry Pi cluster consisting of 300 nodes is presented, with potential use cases being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.
P. Abrahamsson, S. Helmer, Nattakarn Phaphoom et al.· IEEE International Conferenc...· 110 citations· ⚡7
The results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains, and 219 factors representing causes of unhappiness while developing software are identified.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· International Conference on...· 84 citations· ⚡6
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
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.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026