An autonomous agentic framework for cross-campaign generalization and sensor shift adaptation
Although autonomous, Large Language Model (LLM)-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 (SiLA) 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 strictly guaranteeing zero data contamination. The fusion framework was validated on the challenging task of bacterial classification via Surface-Enhanced Raman Spectroscopy (SERS) 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.