Institution profile

Offenburg University

Academic institutioneurope · de
Official website
Research library10linked papers
Opportunities0open roles
Selected work

Representative Papers

Implementing the Spec Growth Engine: Preventing Spec-Code Divergence, and Growing the Spec with Agents

Oct 08, 2026

This study addresses the challenges of specification-code inconsistency and limited extensibility in AI-assisted software development by proposing a dual-layer engine architecture. The lower layer integrates graph theory with static analysis to perform deterministic verification, thereby preventing deviations from established specifications. The upper layer leverages a multi-agent system to collaboratively extend specifications. Furthermore, this work introduces a novel three-switch mechanism that enables eighteen distinct operational configurations, supporting a full spectrum of human-AI collaboration paradigms ranging from manual to fully unsupervised modes. By effectively balancing deterministic guarantees with the value of autonomous generation, the proposed framework ensures that code modifications remain traceable while facilitating the continuous evolution of specifications.

0 citationsRead paper

2D Spatial Reasoning with Adaptive Neural Cellular Automata

Oct 06, 2026

This study addresses the challenge that existing models struggle to effectively leverage geometric information and spatial relationships in two-dimensional spatial reasoning. To this end, we propose an adaptive neural cellular automaton architecture that integrates deformable convolutions into the cellular automaton framework. By dynamically adjusting receptive fields, this method enables iterative reasoning over spatial relationships on grid-structured data. The core innovation lies in synergizing the local adaptivity of deformable convolutions with the global iterative capacity of cellular automata to achieve efficient spatial representation learning. Experimental results demonstrate that the proposed model achieves state-of-the-art performance on benchmark tasks, including Sudoku solving and shortest-path maze navigation, while exhibiting superior generalization capabilities.

0 citationsRead paper

The Spec Growth Engine: Spec-Anchored, Code-Coupled, Drift-Enforced Architecture for AI-Assisted Software Development

Jun 25, 2026

This work addresses two structural failure modes in AI coding agents—context explosion and silent specification-code drift—that undermine development efficiency and code reliability. To tackle these challenges, the paper proposes a lightweight framework that uniquely integrates classical software engineering principles, including information hiding, the C4 model, and Architecture Decision Records (ADRs), into a unified, machine-enforceable architecture. The framework employs a machine-readable specification graph, an ownership-path-based Spine context assembler, a vertical-slice growth protocol, and a drift-gating mechanism to jointly ensure consistency between specifications and generated code. By constraining context expansion and mandating drift detection and correction prior to code merge, the approach significantly enhances the reliability and maintainability of AI-generated code.

0 citationsRead paper
Recent publications

Latest Papers

Implementing the Spec Growth Engine: Preventing Spec-Code Divergence, and Growing the Spec with Agents

Oct 08, 2026

This study addresses the challenges of specification-code inconsistency and limited extensibility in AI-assisted software development by proposing a dual-layer engine architecture. The lower layer integrates graph theory with static analysis to perform deterministic verification, thereby preventing deviations from established specifications. The upper layer leverages a multi-agent system to collaboratively extend specifications. Furthermore, this work introduces a novel three-switch mechanism that enables eighteen distinct operational configurations, supporting a full spectrum of human-AI collaboration paradigms ranging from manual to fully unsupervised modes. By effectively balancing deterministic guarantees with the value of autonomous generation, the proposed framework ensures that code modifications remain traceable while facilitating the continuous evolution of specifications.

0 citationsRead paper

2D Spatial Reasoning with Adaptive Neural Cellular Automata

Oct 06, 2026

This study addresses the challenge that existing models struggle to effectively leverage geometric information and spatial relationships in two-dimensional spatial reasoning. To this end, we propose an adaptive neural cellular automaton architecture that integrates deformable convolutions into the cellular automaton framework. By dynamically adjusting receptive fields, this method enables iterative reasoning over spatial relationships on grid-structured data. The core innovation lies in synergizing the local adaptivity of deformable convolutions with the global iterative capacity of cellular automata to achieve efficient spatial representation learning. Experimental results demonstrate that the proposed model achieves state-of-the-art performance on benchmark tasks, including Sudoku solving and shortest-path maze navigation, while exhibiting superior generalization capabilities.

0 citationsRead paper

The Spec Growth Engine: Spec-Anchored, Code-Coupled, Drift-Enforced Architecture for AI-Assisted Software Development

Jun 25, 2026

This work addresses two structural failure modes in AI coding agents—context explosion and silent specification-code drift—that undermine development efficiency and code reliability. To tackle these challenges, the paper proposes a lightweight framework that uniquely integrates classical software engineering principles, including information hiding, the C4 model, and Architecture Decision Records (ADRs), into a unified, machine-enforceable architecture. The framework employs a machine-readable specification graph, an ownership-path-based Spine context assembler, a vertical-slice growth protocol, and a drift-gating mechanism to jointly ensure consistency between specifications and generated code. By constraining context expansion and mandating drift detection and correction prior to code merge, the approach significantly enhances the reliability and maintainability of AI-generated code.

0 citationsRead paper