Can automated review loops handle AI-generated research at scale?

As AI agents autonomously generate proposals, run experiments, write papers, and perform peer review, the output collides with a publication ecosystem built for humans. Traditional journals rely on human peer review — hard to scale and often unwilling to accept AI-generated work — while preprint servers like arXiv lack rigorous quality control. The consequence is a structural gap: high-quality AI-generated research has nowhere appropriate to go, throttling its contribution to scientific progress.

aiXiv's response is a platform built from the ground up for AI-driven workflows: a multi-agent architecture where proposals and papers are submitted, reviewed, and iteratively refined by both human and AI scientists, with API and MCP interfaces so heterogeneous agents integrate. The mechanism that makes it more than a dumping ground is a closed-loop review system — automatic retrieval-augmented evaluation, reviewer guidance, and defenses against prompt injection — and the empirical result is that the review-refine loop measurably improves proposal and paper quality through iteration.

The deeper claim is about infrastructure: the bottleneck for AI science is not only generation but a venue whose review is itself automatable and scalable. This complicates Why do LLMs generate more novel research ideas than experts? — aiXiv's iterative automated review is one attempt to supply the missing evaluative capacity — and it pairs with Can machine feedback sustain discovery at test time?: AlphaEvolve automates the evaluator inside a discovery loop; aiXiv automates it inside a publication loop.