ARMSB CORE · INDEPENDENT PROPOSAL · REV 2026.07 · STATUS: ACTIVE
In June 2026, the world's most capable AI models were switched off for every user on Earth by a single government directive. ARMSB Core is an independent research proposal with one argument: India must fund sovereign AI research beyond the transformer race, before the next switch flips.
01 · The Signal
In June 2026, a U.S. export-control directive ordered Anthropic to suspend access to its most capable models, Mythos 5 and Fable 5, for foreign nationals in every role, its own employees included. Nationality cannot be verified at the point of use, so within hours both models went dark for every customer on Earth, American citizens included.
The lesson is not about one company or one directive. When a nation's researchers, startups and public systems depend on a single foreign model, that dependency can be switched off overnight, by a decision made elsewhere, for reasons never given, with no recourse and no warning. Zoho's Sridhar Vembu called it a wake-up call. ARMSB Core starts from that same premise: the exposure does not disappear just because the country holding the switch happens to be a partner today.
02 · The Trap
Frontier AI today is a race to scale transformers, and the entry ticket keeps inflating: capital on the order of a hundred billion dollars, GPU clusters behind export walls, data moats already fenced off. India can rent the products of that race. It cannot rent sovereignty over them. Depending on American or Chinese cognitive engines leaves our economy and security standing one directive away from disruption.
03 · The Proposal
A dedicated, well-funded national research mission that refuses to bet everything on a single paradigm, and keeps every resulting breakthrough as Indian intellectual property.
Track A
Keep funding Indian teams fine-tuning transformer models for Indian languages and context, the work Sarvam, Krutrim and others are already doing. It keeps India fluent in the dominant paradigm and delivers working capability while the longer bets mature.
Track B
Back high-risk, high-reward research into fundamentally different architectures by independent Indian researchers and labs. One breakthrough here does not just catch India up. It resets the cost curve of intelligence itself.
↓ both tracks feed into sovereign IP, kept in Bharat
04 · Beyond the Transformer
Transformers won the current era by scaling, and scaling now demands budgets and hardware that sit behind other nations' export walls. The next paradigm may not. Whoever finds it does not merely join the race. They move the starting line. These are the kinds of directions a sovereign research mission should be funding Indian teams to explore.
Linear-time sequence modelling
Sequence architectures such as the Mamba family read context in linear time, sidestepping the quadratic attention cost that makes transformer context windows so expensive to scale.
Long-document, long-conversation capability on a fraction of the compute. A cost-curve reset rewards clever architecture over giant clusters, which is exactly the race India can win.
Parallel generation, not token-by-token
Text generators that work the way image diffusion models paint: refining whole passages in parallel through denoising steps instead of emitting one token at a time.
Parallel decoding promises dramatically faster, cheaper inference on the same silicon. When serving costs collapse, sovereign deployment at India's scale stops being a luxury.
Learning how the world behaves
Joint-embedding predictive architectures learn models of how the world evolves in abstract representation space, rather than memorising the surface statistics of internet text.
A bet on data-efficiency. If intelligence can be learned from less data, the advantage of having scraped the entire English internet shrinks, and India's own data becomes enough.
Answers that must survive a checker
Generator-plus-verifier systems pair a model with a formal or learned checker, from proof assistants to trained critics, so that answers must be validated before they ship.
Reliability becomes a property you can certify rather than merely benchmark. For law, governance, medicine and engineering, a sovereign stack must be provably dependable, not just fluent.
Thinking longer at test time
AlphaZero-style systems spend compute searching over possibilities when a hard problem actually arrives, instead of relying purely on reflexes frozen in during pre-training.
It shifts spending from one colossal training run to flexible per-problem compute. That economics favours focused national missions over trillion-dollar balance sheets.
Models that never stop learning
Architectures designed to keep learning after deployment, updating knowledge in place instead of freezing the world at a training cutoff and retraining from scratch each cycle.
It escapes the retrain-from-scratch treadmill that only the largest balance sheets can afford. A model that grows with Bharat's languages, data and needs is sovereignty by design.
Directions shown are illustrative of active research areas being explored worldwide, not endorsements of any single lab, paper or claim.
05 · The Thesis
"If we continue to rely on American or Chinese cognitive engines, we face a future of digital colonization, where our economy and security are entirely at the mercy of foreign state directives, issued without warning and without recourse. This is India's once-in-a-generation chance to own the cognitive front-end of its own digital future, on its own terms."
About
Ananya Raj Mohan Singh Bhadauriya
An independent AI researcher working on India's strategic autonomy in frontier technology. ARMSB Core is a personal research and policy initiative: researched, written, designed and maintained independently.