An autonomous agentic framework for cross-campaign generalization and sensor shift adaptation

Abstract

Although autonomous, large language model-driven systems show immense potential for orchestrating complex scientific experiments, their efficacy is constrained by two fundamental bottlenecks: context dilution, where strategic reasoning degrades as experimental history accumulates, and inter-campaign amnesia, which forces systems into computationally expensive tabula rasa explorations upon encountering novel domains. To overcome these limitations, we introduce the Multi-Objective State-Action Network (MOSAN), an autonomous cognitive information fusion framework designed for robust cross-campaign generalization and sensor shift adaptation. MOSAN achieves effective information fusion by coupling a self-evolving semantic memory with an Organic Strategic Graph Memory (OSGM), strictly orchestrated through a novel Single Ledger Architecture that isolates cognitive phases and prevents context saturation. A core mechanism of the OSGM is Cross-Domain Strategic Seeding (‘Ghost Node’ injection), a transient cold-start fusion strategy. Upon encountering novel datasets, the OSGM temporarily injects topological priors from similar past domains; these ephemeral nodes guide initial architectural deductions and are subsequently purged, allowing the system to disseminate structural knowledge across disciplines while decreasing the risk of cross-contamination. The fusion framework was validated on the challenging task of bacterial classification via Surface-Enhanced Raman Spectroscopy subject to sensor aging shifts, autonomously discovering highly accurate multi-topological architectures. Crucially, when subjected to an out-of-distribution 1D sequential modality (the ECG5000 cardiovascular dataset), the agent performed zero-shot architectural meta-learning, bypassing brute-force search to rapidly achieve 0.9930 accuracy. Finally, the framework was successfully deployed using an open-weight model (Mistral 24B) at zero API inference cost. By continuously fusing multi-epoch empirical evidence, bridging representational topologies, and actively overcoming sensor drift, MOSAN establishes a scalable and rigorous paradigm for autonomous biophysical discovery.