Searcharxiv⌕ Search

arXiv · 2610.14130

Conserved amount, negotiable placement. Prompting perturbs one architecture, an AI reviewer both

Abstract

Tesler's conservation law holds that a problem carries an irreducible amount of complexity. Brooks separates that essential part from the accidental part a solution adds. If the essential part is fixed, architecture is placement rather than quantity, and the question is which levers move placement. An AI agent implements sixty accumulating change requests on one REST endpoint under two architectures, a mutative Spring controller and an additive OfficeFloor pipeline, under five conditions, ten runs per cell, model fixed. Four are external controls, from a bare change request to the disclosed structural cost function. The fifth is different in kind. A second agent reviews each change and reasons about cohesion itself. Amount is steady. Across all ten cells the finished complexity varies by 7% to 20% over four operationalisations, and the complexity the sixty rules added by 21% to 29% within an architecture. Placement is not. Under the four external controls the plasticity ratio between the architectures is near 1 on six amount metrics (five of six intervals contain 1) against 2.7 to 10.0 on eleven placement metrics (ten of eleven above 1). The mutative architecture moves, the additive one does not, and the disclosed formula brings it to the additive arm's untouched placement. The AI reviewer is the one lever that moves the additive arm, on one family only. It consolidates the pipeline, raising change- and mass-concentration on every such metric, interval excluding zero, while leaving coupling, file count and per-node complexity untouched. The price is reported in full. Strict correctness fell in most comparisons though each rule was implemented correctly in over 99% of checkpoints, and all three outcome measures favour the concentrated codebase. The contribution is a measurable property of an architecture and its lever, not a verdict on either.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Daniel Sagenschneider. 2026-10-09. Conserved amount, negotiable placement. Prompting perturbs one architecture, an AI reviewer both. https://arxiv.org/abs/2610.14130

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

daVinci-Env: Open SWE Environment Synthesis at Scale

Training capable software engineering (SWE) agents demands large-scale, executable, and verifiable environments that provide dynamic feedback loops for iterative code editing, test execution, and solution refinement. However, existing open-source datasets remain limited in scale and repository diversity, while industrial solutions are opaque with unreleased infrastructure, creating a prohibitive barrier for most academic research groups. We present OpenSWE, the largest fully transparent framework for SWE agent training in Python, comprising 45,320 executable Docker environments spanning over 12.8k repositories, with all Dockerfiles, evaluation scripts, and infrastructure fully open-sourced for reproducibility. OpenSWE is built through a multi-agent synthesis pipeline deployed across a 64-node distributed cluster, automating repository exploration, Dockerfile construction, evaluation script generation, and iterative test analysis. Beyond scale, we propose a quality-centric filtering pipeline that characterizes the inherent difficulty of each environment, filtering out instances that are either unsolvable or insufficiently challenging and retaining only those that maximize learning efficiency. With $891K spent on environment construction and an additional $576K on trajectory sampling and difficulty-aware curation, the entire project represents a total investment of approximately $1.47 million, yielding about 13,000 curated trajectories from roughly 9,000 quality guaranteed environments. Extensive experiments validate OpenSWE's effectiveness: OpenSWE-32B and OpenSWE-72B achieve 62.4% and 66.0% on SWE-bench Verified, establishing SOTA among Qwen2.5 series. Moreover, SWE-focused training yields substantial out-of-domain improvements, including up to 12 points on mathematical reasoning and 5 points on science benchmarks, without degrading factual recall.

cs.SE↗

From Research to Practice: An Interactive Rapid Review of Autonomous Driving System Testing in Industry

Autonomous driving systems (ADS) are increasingly deployed in real traffic, yet testing remains fundamentally challenging due to open environments, complex scenarios, and the lack of established processes and acceptance criteria. Despite extensive research, a gap persists between academic advances and their applicability in industrial practice. To address this, we conduct an interactive rapid review in collaboration with 21 practitioners from a leading automotive company. Practitioners identified 12 key challenges in ADS testing, and prioritised two as the most critical issues, namely approaches to and completeness of testing for End-to-End (E2E) ADS. We analyzed 17 research studies relevant to these two challenges, most of which focus on generating critical testing scenarios, and subsequently assessed their relevance and applicability in practice. Our study provides the first practitioner-driven review and evaluation of current ADS testing research, reveals practical challenges in ADS testing, offers rapid insights for practitioners, and highlights the need for more context-aware, industry-relevant solutions to bridge the gap between research and practice.

cs.SE↗

Memoir: Learning, Verifying, and Evolving False-Positive Memories for Static Application Security Testing Tools

Static Application Security Testing (SAST) tools have become indispensable in modern secure software devel- opment. However, these tools often generate false-positive (FP) alerts, imposing substantial manual inspection costs and reducing the trust from developers. Existing FP reduction methods still face two primary challenges. First, the large differences among SAST tools and vulnerability categories make it difficult for these methods to learn recurring patterns in historical false positives. Moreover, the knowledge used by these methods are largely static and cannot be updated as newly validated cases accumulate. To address these challenges, we propose Memoir, a memory- driven framework for identifying false positives by transform- ing historical FP alerts into reusable semantic memories. It consists of two key modules. First, historical semantic memory construction converts historical FP alerts into structured semantic memories through LLM-guided annotation, pattern clustering, and memory synthesis to capture reusable behavioral patterns. Moreover, memory-driven identification and evolution retrieves relevant memories and performs semantic verification against taxonomy consistency and security invariants before making the final prediction. It then incorporates verified predictions back into the memory repository, allowing the knowledge base to evolve as new cases accumulate. We evaluate Memoir on CWE- Bench-Java to demonstrate its effectiveness in real-world security analysis. Specifically, Memoir achieves an F1-score of 99.43% with a Recall of 98.88% and perfect Precision, consistently outperforming other baselines. Furthermore, an industrial case study on production software systems from a top IT company shows that the learned memory base generalizes effectively across different SAST tools without retraining.

cs.SE↗