Current Large Language Models (LLMs) produce erroneous answers on temporally evolving knowledge at rates of 18–23%, confidently recommending deprecated APIs, outdated patterns, and obsolete frameworks years after they have been superseded. This paper presents CGTKMS v2.0 — a hardened, implementation-ready architecture that solves the temporal knowledge problem through architectural separation of concerns rather than weight modification. The system introduces a dedicated Management Neuron — an autonomous external system that organizes knowledge in dated, topic-indexed temporal buckets, computes coverage completeness via a 3-stage semantic equivalence pipeline, and gates the retirement of old knowledge on verified completeness of new knowledge. The LLM is treated as a pure inference engine and never participates in knowledge management decisions. v2.0 resolves seven critical gaps identified in v1.0, most importantly the parametric knowledge override problem — the empirically confirmed failure where LLMs default to training data even when clean external context is provided. The solution integrates a three-layer defense: context framing, CLEAR-style output verification, and RL-based parametric/contextual knowledge balancing. Novel contributions include: (1) coverage-gated archival threshold, (2) semantic knowledge mutation taxonomy (DEPRECATED / REPLACED / NET-NEW / VERSIONED), (3) domain-calibrated adaptive thresholds, (4) cold start bootstrap protocol, (5) diff worker hallucination defense via ensemble voting, (6) cross-bucket dependency chain management, and (7) tiered adversarial ingestion protection. Projected outcome: temporal error rate reduced from ~20% to ~2.8%, with ~99% old-knowledge retention and full backward compatibility. Implementable with current open-source tools: Qdrant, vLLM, Ollama, FastAPI, LoRA adapters. This is the v2.0 final document. v1.0 (original architecture proposal) is linked as a related work.
Jay prakash Bhagat (Sat,) studied this question.