software infrastructure engineers

Abstract: Repository-level automated program repair must reason about more than a localized failing statement. A candidate change can satisfy a narrow test while violating dependency boundaries, modifying an unsafe path, or relying on context that omits relevant repository structure. ArchGuard-APR is an architecture-aware repair prototype that integrates deterministic repository analysis, immutable repository graphs, explicit architecture profiles, graph-guided context retrieval, role-based repair orchestration, patch-policy enforcement, isolated execution feedback, provider cost controls, and canonical source-free evidence. We evaluate the implemented pipeline through an exploratory paired feasibility study, not a confirmatory effectiveness experiment. Six executions were planned; four completed, and two black-4 conditions ended in pre-provider infrastructure attrition. Across two valid paired tasks, baseline and ArchGuard conditions each produced 0/2 successful repairs, and all four completed outputs were classified as invalid patches. The completed executions consumed 35,431 input tokens and 2,737 output tokens, for a total of 38,168 tokens, at a total cost of USD 0.1296325. No effectiveness advantage was observed, and the sample provides no useful inferential basis. The study instead establishes the engineering feasibility of composing architecture constraints with a reproducible repair pipeline and identifies patch validity and infrastructure robustness as the principal targets for future work.

Abstract: Recursive self-improvement (RSI) of agent harnesses has emerged as an effective approach to enhancing the capabilities of large language model (LLM) agents. By searching the harness space for a specific LLM on a given dataset, RSI can discover a harness tailored to the capabilities and behavioral characteristics of that model. However, such optimization often creates a strong coupling between the learned harness and its target LLM, limiting generalization across models and requiring substantial computational resources to adapt to new LLMs. Moreover, existing RSI methods typically optimize a harness using feedback from a single model and evaluate its generalization to other models only after training. This paradigm treats model differences primarily as a criterion for evaluating generalization rather than as valuable evidence for optimization. In this paper, we introduce a new perspective that treats models as data and places the harness at the center of optimization. Building on this perspective, we propose \textsc{MaD-RSI}, a framework that directly optimizes a shared harness across multiple heterogeneous LLMs by incorporating their diverse failure trajectories into the training process. Rather than assessing model differences only after optimization, \textsc{MaD-RSI} leverages these differences as training evidence to guide the self-improvement of a shared harness. Furthermore, we introduce a new evaluation framework with two complementary metrics designed to assess the effectiveness of harness optimization across models. Experiments across multiple datasets and LLMs demonstrate that \textsc{MaD-RSI} improves generalization over existing approaches while reducing training costs.