Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
Method note reporting the pre-registered retrospective test of AI-RISKPATH: whether a mechanical chaining engine, driven only by precondition/effect labels attached to the 208 techniques of MITRE ATLAS v2026.09, can reconstruct the 73 attack chains ATLAS documents. The case studies were split by a public randomness beacon (drand round 6479280, committed 24 hours before it existed), the vocabulary was built on one half and frozen with published salted commitments, and the metrics were measured once on the half never seen. Chain recall on the held-out half is 33.3 percent (12/36, 95 percent CI 20.2-49.7) and lift is 1.253 (95 percent CI 1.153-1.364), below the pre-registered continuation threshold: the rule stops the project. The cause is structural. After calibration, 108 of the 208 techniques carry no precondition anywhere in the ATLAS text, 42 percent of transition weight, so almost any technique remains admissible at almost any point. The note reports the protocol, the negative result, a contamination statement including two identifier disclosures found by audit and declared, and the measurement that explains the failure. The three earlier versions of this record are the pre-registrations it relies on. The labelled vocabulary is not published; the deposited commitments allow any claim about it to be checked without disclosing it.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
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.
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