A hierarchical memory architecture overcomes context limits in long-horizon multi-agent computational modeling
Large language models (LLMs) demonstrate remarkable reasoning capabilities, yet their stateless architecture fundamentally limits deployment in long-horizon research workflows requiring multi-session continuity and quantitative rigor. Here we present Ensemble QSP, a multi-agent framework featuring a three-layer hierarchical memory architecture that bounds injected context (median 301 tokens, max 4,050) by capping state categories and evicting completed work. This enables continuous autonomous operation without context degradation. The system orchestrates five specialist worker agents under domain-expert principal investigators (PIs), enforcing physical constraints through physics-based checklists and structured domain knowledge. Comprehensive benchmarking demonstrates autonomous pharmacokinetic-pharmacodynamic (PKPD) model selection, improved parameter recovery relative to single-agent baselines, and robust interpretation of linguistically diverse prompts. Replication with open-weight models (DeepSeek-V4-Flash/Pro, Llama 3.1 70B) confirmed these architectural conclusions across PKPD modeling, literature synthesis, and PBPK model implementation, proving the framework is independent of proprietary LLMs. Feature-level ablations show that memory, retrieval, and PI oversight address distinct scientific failure modes, though underlying LLM capability remains consequential for stringent physical-consistency checks. The architecture is structurally agnostic to computational biology; adding a new scientific domain requires only a new PI-agent configuration.