The frontier of AI shifts weekly. New models ship, new evaluation regimes surface, old assumptions collapse. Researchers writing in the open often get read months later, once the argument has already moved on. GIKSN Research is our response to that. It is a lab where the reasoning behind a result lands in the archive at the same time as the result itself, and where AI is the sector that gets the most cycles.

What lands here

An AI paper on GIKSN is not a compressed conference submission. It is the argument as we thought through it. The failure modes we considered. The prior work we leaned on and the prior work we did not. Original research goes up in Preprint status the moment it is ready to be argued with. Surveys of subfields sit alongside, because a good map is as valuable to us as a new experiment.

What we are working on

Our current focus areas cluster around agent orchestration, evaluation under domain shift and open-model interpretability. We do not try to compete with the largest labs on scale. We try to add clarity where the largest labs choose not to write. That includes benchmarks that nobody wants to run, cost-of-ownership numbers on model deployments and workflows for making multi-model setups debuggable rather than magical.

Reasoning in public

Every paper carries a discussion thread underneath it. Authors are credited, and rejected directions stay in the archive because the reasoning matters more than the verdict. We do not delete papers when we change our mind. We move the status, add a note and keep the trail. This is the record.

Cross-sector work is the point

AI does not live on its own. A frontier model is a compute substrate question first and a model quality question second. It is a systems question when the model has to serve requests. It is a deeptech question when the underlying advance depends on chemistry or biology or energy. That is why the archive has four sectors that share a discussion floor rather than one AI-only feed with everything else tacked on.

What we ship

Rinne is our first shipped tool out of the AI bench. It is a local terminal-first orchestrator that composes the coding-agent CLIs and model APIs you already have into a verifying generator to evaluator loop. It runs on your machine, holds no credentials in plaintext and does not phone home. The argument that led to Rinne, and the design decisions we made along the way, live under its companion paper in this sector.

More products are on the bench. They will ship when they are ready.

Reading and contributing

Anyone can read. Anyone can comment. Contribution is gated because the work is real: applications land through the About page, and accepted contributors get access to the private working channels on Telegram alongside the public read-only channels. If you want to argue with something specific, the discussion thread under any paper is the fastest way in.