What this publication is — and isn't.

What you'll find here

• Deep-dive case studies with code, data, figures, and explicit methodology — not high-level commentary.
• Working papers and structured analyses on multi-agent reinforcement learning, game-theoretic decision modelling, and their applications to defence, wargaming, and crisis simulation.
• Technical translations: distilled reading lists turning recent ML/AI research into actionable signal for strategic researchers.
• Contrarian methodology takes — where the consensus on AI for defence is wrong and why, backed by structured argument and case evidence.
• Tooling and workflow comparisons — open-source frameworks, agent-orchestrated research workflows, simulation engineering.

What you won't find here

• Geopolitical commentary or predictions. We do not publish opinions on what will happen in any specific conflict, nor what any country "should do".
• Generic AI productivity tips, prompt-engineering listicles, or Claude/GPT comparison content for a mass audience.
• News aggregation, daily briefs, or summarisation of others' reporting.
• Sponsored content, affiliate links, or growth-hack tactics. The publication is funded directly by paying subscribers and B2B services.

Who this is for

• Think tank fellows, policy researchers, and strategic analysts who want technical depth on AI methods applicable to their work.
• Defence research lab directors and members evaluating multi-agent / RL / LLM methods for adversarial scenarios.
• Wargame designers and strategic studies academics working on adversarial AI integration.
• Multi-agent RL researchers interested in defence and strategic applications.
• Independent technical readers who prefer methodology and receipts to commentary and prediction.

Tone and editorial standard

We write in publication voice ("we", not "I") because the content is intended to scale beyond any single author — every piece is structured to be citable and reusable as a technical reference, not a personal blog post.

Every published piece includes at least one of: code, runnable data, an original figure, or a detailed case study. Pieces without this floor of evidence are not published.

Pseudonymity

The Adversarial Lab operates as a research publication rather than a personal-creator brand. We do not surface individual authors in public. Inquiries that require author identification — for collaborations, citations, B2B engagements, or institutional partnerships — are handled directly via the contact page.

Independence

The Adversarial Lab is an independent research publication. It is not affiliated with any government, defence agency, university, or commercial institution. Any government or defence-sector employment of contributing authors is entirely separate from this publication's editorial direction, and editorial direction is not influenced by such employment.

Citation policy

Pieces published here may be cited in academic and policy work. Use the publication name ("The Adversarial Lab"), the issue number or article slug, and the publication date. Code and figures published here are released under permissive terms where indicated; refer to each piece's licence note.

Frequency and format

One deep-dive every two weeks initially, scaling to weekly as the publication matures. Occasional working papers (10–20 page PDFs) and short data notes supplement the main cadence.

Why "Adversarial"?

The word does double duty. In machine learning, adversarial denotes systems trained against opponents — multi-agent RL, generative adversarial networks, adversarial evaluation. In strategic studies, it denotes the red-team / blue- team dynamics at the core of any meaningful conflict simulation. The intersection of these two senses — adversarial AI applied to adversarial strategic systems — is the publication's permanent subject.