Live Knowledge Graphs: Runtime Graph Integration for Continuous Domain Adaptation in Language Agents

Recent work on Domain-Specific Superintelligence (Belova et al., 2026) demonstrates that knowledge graph-derived training curricula produce domain specialists that outperform models 400x their size. However, this approach treats knowledge graphs as static training scaffolding: constructed once, used for fine-tuning, then discarded at inference. We present an alternative: runtime knowledge graph integration, where the graph is queried live during inference with provenance-tracked context slicing, real-time entity resolution, and cryptographic admissibility verification. We implement this in cc-graph-kernel, a production Rust service built on the Axum framework, processing real workloads across a multi-machine mesh with 71,130 knowledge triples in its production database. We evaluate multi-hop path quality using a 3-signal reward function over 199 valid graph paths and 199 hard negatives, achieving 81.0% pairwise ranking accuracy (Cohen's d = 2.228). We further demonstrate that anticipation geometry scalars, originally developed for conversational turn analysis, produce meaningful distributions when applied to knowledge graph paths, with distinct profiles compared to the conversation domain. Our key contribution is the Context Slicer, a priority-queue BFS algorithm that produces provenance-complete, HMAC-signed graph slices suitable for direct injection into language model prompts. **Keywords:** knowledge graphs, retrieval-augmented generation, context slicing, runtime integration, provenance, admissibility tokens, conversational AI