Scientific research is not merely a sequence of tasks to be automated, but a knowledge-production process in which claims must remain verifiable, criticizable, revisable, and accountable. Yet most AutoResearch systems remain single scaffolds that collapse generation, execution, interpretation, and review into one loop. This survey diagnoses three resulting epistemic limits of such architectures: collapsed skepticism, where a single scaffold produces not only errors but also the narratives that allow them to survive; unverified translation from discovery to application, where proposal generation outpaces independent verification; and invisible evidence trails, where final manuscripts hide the failed attempts and post-hoc decisions that produced them. To overcome these structural fragilities, we propose a shift from autonomous AI scientists to AI research communities, where specialized agents, shared memory, inspectable artifacts, and governance checkpoints distribute epistemic labor. We synthesize AutoResearch as a transition from metric-driven search loops to AI-scientist workflows, domain-mediated discovery, research infrastructure, and accountable human–AI co-evolution. Across these layers, we argue for a fundamental shift from productivity-centered evaluation to verifiability-centered evaluation.