arXiv subjects

Tyler Slater

Publications and source records attributed to Tyler Slater.

2 recordsLinked to original sources

arXiv2025

Quantitative Analysis of Technical Debt and Pattern Violation in Large Language Model Architectures

As Large Language Models (LLMs) transition from code completion tools to autonomous system architects, their impact on long-term software maintainability remains unquantified. While existing research benchmarks functional correctness (pass@k), this study presents the first empirical framework to measure "Architectural Erosion" and the accumulation of Technical Debt in AI-synthesized microservices. We conducted a comparative pilot study of three state-of-the-art models (GPT-5.1, Claude 4.5 Sonnet, and Llama 3 8B) by prompting them to implement a standardized Book Lending Microservice under strict Hexagonal Architecture constraints. Utilizing Abstract Syntax Tree (AST) parsing, we find that while proprietary models achieve high architectural conformance (0% violation rate for GPT-5.1), open-weights models exhibit critical divergence. Specifically, Llama 3 demonstrated an 80% Architectural Violation Rate, frequently bypassing interface adapters to create illegal circular dependencies between Domain and Infrastructure layers. Furthermore, we identified a phenomenon of "Implementation Laziness," where open-weights models generated 60% fewer Logical Lines of Code (LLOC) than their proprietary counterparts, effectively omitting complex business logic to satisfy token constraints. These findings suggest that without automated architectural linting, utilizing smaller open-weights models for system scaffolding accelerates the accumulation of structural technical debt.

cs.SE↗

arXiv2025

A Self-Improving Architecture for Dynamic Safety in Large Language Models

Context: Large Language Models (LLMs) rely on static, pre-deployment safety mechanisms that cannot adapt to adversarial threats discovered after release. Objective: To design a software architecture enabling LLM-based systems to autonomously detect safety failures and synthesize defense policies at runtime, without retraining or manual intervention. Method: We propose the Self-Improving Safety Framework (SISF), grounded in the MAPE-K reference model. The framework couples a target LLM with a feedback loop: an Adjudicator detects breaches, a Policy Synthesis Module generates dual-mechanism defense policies (heuristic and semantic), and a Warden enforces them. We conducted seven experiments (10,061 evaluations) across four model families. Results: Across five reproducibility trials, SISF achieved a mean Attack Success Rate (ASR) of 0.27% (+/-0.15%), autonomously generating 240 policies per trial. Cross-model evaluation confirmed deployment portability. A held-out test showed a 68.5% proactive interception rate on unseen attacks. Stacked behind Llama Guard 4, the combined defense reduced residual ASR from 7.88% to 0.00%. Ablation confirmed both heuristic and semantic policy types are architecturally required. Conclusion: Self-adaptive architecture is a viable approach to LLM safety. SISF achieves sub-1% ASR through synchronous output monitoring, progressively shifting enforcement to fast, local Warden policies via the MAPE-K loop, offering a new pattern for building resilient AI systems.

cs.SE↗