ContinuumCellAgent: A Framework-Guided Agent for Long-Horizon Scientific Research

AI-scientist systems are beginning to automate parts of scientific research. We present CO_SCPLOWONTINUUMC_SCPLOWCO_SCPLOWELLC_SCPLOWAO_SCPLOWGENTC_SCPLOW, an autonomous agent that executes literature review, hypothesis formation, computational experimentation, manuscript drafting, and adversarial peer review as a single unattended run. Existing AI scientist systems remain difficult to diagnose because they lack modularity, systematic prompt grounding, and observability into long-running behavior. CO_SCPLOWONTINUUMC_SCPLOWCO_SCPLOWELLC_SCPLOWAO_SCPLOWGENTC_SCPLOW addresses these gaps with a modular supernode architecture for stage-wise backend swapping, protocols grounded in curated research-method checklists that also define reviewer rubrics, and a diagnostics layer that records file-based artifacts, message traces, and state transitions. We evaluate the system on open-domain QA benchmarks and biomedical/longevity case studies, showing that it can produce checkable research artifacts while exposing pipeline dynamics for rigorous AI co-scientist research.