Oct 2026· Proceedings of the ACM/IEEE 29th International Conference on Model Driven Engineering Languages and Systems· 0 citations· 11 references
Abstract
AI assistants are increasingly used to support behavioural modelling from natural-language requirements, yet they provide little assurance that the resulting models are logically coherent. We argue that this limitation stems not only from the capabilities of large language models but also from the absence of explicit reasoning structures mediating interactions between human analysts and AI systems. To address this limitation, we introduce the notion of a reasoning scaffold as an explicit intermediate modelling artefact and propose scaffolded behavioural modelling, in which AI agents reason over such scaffolds rather than rely solely on conversational interactions. As a first instantiation of this broader concept, we investigate logical squares as one possible reasoning scaffold that exposes contradictions, alternatives, implications and missing behavioural concepts, and introduce scaffold-aware agents that exploit these structures to support systematic behavioural exploration. Finally, we outline a research agenda for reasoning scaffolds and argue that they provide a foundation for more explainable and verifiable AI-assisted modelling environments, and may facilitate future correct-by-construction approaches.
Human reasoning relies on abstraction and generalization, in order to make decisions flexible under changing conditions while ignoring irrelevant details and focusing on the essence. Developing AI systems with such abilities, while ensuring transparency and explainability on the reasoning behind the made decision, rema...
Z. G. Saribatur· Proceedings of the Thirty-Fi...· 0 citations
This work presents a post-hoc XAI framework that transforms a lengthy agent's execution trace into a structured report and a faithful natural-language explanation explicitly grounded in its observable behavior, outperforming naive LLM-generated explanations.
Vittoria Vineis, Fabiano Veglianti, Lorenzo Antonelli et al.· 0 citations
Reasoning about alternatives is a fundamental component of human cognition and argumentation, yet it remains unclear whether large language models (LLMs) can coherently generate and assess them. This paper introduces Counter-Hypothesis Generation (CHG), a novel task for evaluating how LLMs construct plausible hypothese...
Marzieh Abdolmaleki, Aaron Maladry, Veronique Hoste et al.· International Conference on...· 0 citations
This paper implements a text-based, multi-agent version of the classic board game Clue as an environment to evaluate multi-step, agentic deductive reasoning, and introduces a tool-augmented approach in which a structured possibility matrix converts implicit game state from generated reasoning logs into an explicit repr...
It is argued that emergent reasoning in LLMs is a product of three factors: model size, prompting approach, and evaluation metric, and proposed implications for designing benchmarks and assessing capabilities are proposed.
N. Kuotsu· International Journal of Cre...· 0 citations
Large language models (LLMs) have demonstrated great potential in code reasoning tasks, but their reasoning processes lack reliable verification mechanisms, making it difficult to ensure logical correctness. The Tree of Thoughts (ToT) framework improves reasoning by exploring multiple paths and employing backtracking,...
Hao-Liang Cheng, En-Yi Tang, Shuo-Xiao Zhang et al.· Proceedings of the ACM on So...· 0 citations
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
