Contract-Grounded Cognitive Composition: Deterministic Topology, Interface Contracts, and Local Validation in LLM Multi-Agent Systems

Abstract

This release presents Contract-Grounded Cognitive Composition (CGCC), an engineering framework for improving the reliability of LLM multi-agent systems through deterministic workflow topology, shared interface contracts, and local validation. The study reports three exploratory experiments conducted with a local qwen3.5:9b model. The experiments examine sequential reasoning with deterministic validation, control-flow ablations across evidence retrieval, planning, and code generation, and frontend-backend integration under free communication, natural-language API documentation, and shared JSON Schema conditions. The results suggest that LLM cognition can be composed, but reliable composition depends on three distinct conditions: correct workflow progression, compatible interfaces, and validated local execution. Fixed topology reduces premature termination and routing errors, while shared API contracts reduce cases in which independently generated modules are locally plausible but fail during integration. This archive includes the English working paper, complete experimental code, raw model prompts and responses, routing and validation traces, aggregate results, supporting experiment notes, a data dictionary, and reproducibility materials. The experiments use one local 9B model and small synthetic task sets. The results should therefore be interpreted as an exploratory mechanism study rather than a general performance benchmark.