Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Safety Systems Engineering in Autonomy
Abstract
Kratki opisStručni usporedni rad analizira funkcionalni položaj PREDECISION™ arhitekture u odnosu na safety filtering, reinforcement-learning shielding i runtime assurance. Naglasak je na razlikovanju determinističke kvalifikacije već generiranog ili odabranog kandidata od mehanizama koji modificiraju, zamjenjuju ili preusmjeravaju akcije. PREDECISION™ prikazan je na razini javnog funkcionalnog ugovora, bez otkrivanja interne implementacije zaštićene jezgre. DatotekaPREDECISION_usporedna_analiza_HR_PUBLIC_SAFE_2026-09-19.pdf SHA-256aff3564e698b65c5cfaccb6fb380e06bb5d19c10e4490711168469de5499cba1 🇬🇧 English version TitlePREDECISION™ Beyond Safety Filtering: Distinguishing Deterministic Candidate Qualification from Safety Filters, Shielding, and Runtime Assurance Architectures DescriptionThis comparative technical paper examines the functional position of PREDECISION™ in relation to safety filtering, reinforcement-learning shielding, and runtime assurance architectures. It focuses on the distinction between deterministic qualification of an already generated or selected candidate and mechanisms that modify, replace, or redirect actions. PREDECISION™ is described strictly at the level of its public functional contract, without disclosing the internal implementation of the protected core. FilePREDECISION_comparative_analysis_EN_PUBLIC_SAFE_2026-09-19.pdf SHA-2561db9aec6bf8068bda4168d91b2568cd713313ac029b01a42754114bb113416df
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
This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach and presents how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework.
Carmine Giardino, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 175 citations· ⚡19
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
It is found that roles of MVPs in startups were not fully aware by entrepreneurs, and entrepreneurs should consider a systematic approach to fully explore the value of MVP, as a multiple facet product (MFP).
Anh Nguyen-Duc, P. Abrahamsson· International Conference on...· 93 citations· ⚡9
It is found that what perceived as biggest challenges by software startups do vary across different life cycle stages, even though its significance decreases when the learning focuses of the startups move from problem to solution and their products mature.
Xiaofeng Wang, Henry Edison, Sohaib Shahid Bajwa et al.· International Conference on...· 62 citations· ⚡6
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
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.
MIT News · Artificial Intelligence· news.mit.eduOct 7, 2026
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026