Real-Time Telemetry Analysis and Phase Transition Detection of Context Degradation in Frontier Reasoning LLM Architectures

As the context window of modern Large Language Models (LLMs) expands to hundreds of thousands of tokens, autonomous multi-turn agents are increasingly deployed for mission-critical enterprise workflows. However, cumulative context accumulation causes attention dilution and Key-Value (KV) cache saturation, inducing sudden drops in reasoning depth, loss of critical constraints, and abrupt hallucinations. Conventional prompt-based verification paradigms suffer from an epistemic self-reflection paradox and heavy latency overhead. In this paper, we propose a Non-Invasive Telemetry Phase Transition Analysis and Deterministic Context Protection Architecture. Without inspecting generated text semantically, our framework continuously tracks external physical telemetry at the model's protocol boundary. Applying moving variance and cross-correlation operators, we demonstrate that the transition from deep reasoning to cognitive collapse is deterministically preceded by a rapid collapse of local variance (CV dropping from 16.6% to 3.8%) and a surge in latency-output token correlation (r -> +0.985). Using an ultra-low-latency native evaluation engine (0.0168 ms) evaluated across 792 cumulative turns, we map the four-phase manifold of long-context inference and show that autonomous deterministic rollback completely prevents cognitive collapse.