Artificial intelligence (AI) is being applied to an expanding range of scientific activities, including literature analysis, hypothesis generation, software development and automated experimentation. Yet these capabilities rely on a more fundamental condition: scientific information must remain identifiable and traceable across systems and over time. In practice, different versions of a work and their associated records and files are often scattered across services and local directories. This fragmentation forces researchers to identify and contextualize the same work repeatedly, disrupting continuity from discovery to interpretation and reuse. Here we present Gewu Insight, a research information infrastructure that addresses this discontinuity by organizing scientific works as persistent, stateful research objects. It unifies venue-, topic- and author-based discovery records through a shared paper identity. Within a local library, metadata, versioned resources and research context can then accumulate around the same work. These capabilities are integrated into an iterative workflow for scientist-led, AI-assisted research, allowing earlier interpretations to be revisited and refined. A bounded evaluation showed high candidate precision across the three discovery modes, with paper identity, resource distinctions and research state preserved in controlled workflows. By making continuity an explicit property of research infrastructure, Gewu Insight can serve as an inspectable scientific memory layer shared by researchers and AI assistants, supporting human judgment and providing a foundation for future, more epistemically grounded scientific AI.

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