# IACDM: Interactive Adversarial Convergence Development Methodology -- A Structured Framework for AI-Assisted Software Development

arXiv: https://arxiv.org/abs/2604.16399 · HTML: https://mlsanity.com/paper/2604.16399/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 59 references that are in the map.
- First posted on arXiv 31 Mar 2026.

## Builds on (most cited first, 9 of 9)

- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [Large Language Models Cannot Self-Correct Reasoning Yet](https://mlsanity.com/paper/2310.01798/) (2023)
- [Discovering Language Model Behaviors with Model-Written Evaluations](https://mlsanity.com/paper/2212.09251/) (2022)
- [Faith and Fate: Limits of Transformers on Compositionality](https://mlsanity.com/paper/2305.18654/) (2023)
- [Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity](https://mlsanity.com/paper/2507.09089/) (2025)
- [AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz Testing](https://mlsanity.com/paper/2409.10737/) (2024)
- [Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmap](https://mlsanity.com/paper/2410.06107/) (2024)
- [The Impact of AI-Generated Solutions on Software Architecture and Productivity: Results from a Survey Study](https://mlsanity.com/paper/2506.17833/) (2025)

## Abstract (arXiv)

Adoption of AI-assisted development in 2025 exposed a tool-agnostic failure pattern: experienced developers using frontier models were measurably slower while believing they were faster, and 10.3% of applications in one production showcase leaked data through misconfigured access. These failures share a structural cause, the verification gap: absent external tool use, no language model can determine whether what it generated is correct. The tool is irrelevant; the process is determinative. We present IACDM (Interactive Adversarial Convergence Development Methodology), an 8-phase framework in which verification agents external to the generator operate at discrete gates, and the AI alternates between building artifacts and attacking them through specialized critique lenses. What distinguishes it from the review and red-teaming traditions it borrows from is that the gate is enforced by a state machine outside the model: the agent may request advancement, not grant it. One component has now been tested. A pre-registered experiment over twelve projects under a single frozen instrument asked whether the nineteen lenses are non-redundant, against a prior expectation from replications of Perspective-Based Reading, which found reading perspectives not to differ. Every lens found at least one defect no other lens found, under four independent judgements of what counts as the same defect. Two further results were unplanned: the taxonomy withstands testing while the criterion deciding when to apply it does not, and the method creates commitments nothing ever re-confronts with the built system. The method was otherwise developed at one industrial R&D institute. That evidence establishes applicability, not effectiveness: no project ran without the method, and the proponents are the evaluators. Whether IACDM outperforms direct prompting remains open, and this paper is written to make it refutable.