Needle-in-haystack: perfect retrieval, zero degradation

Buried a secret password ('AURORA-7742') inside 10, 50, 200, and 500 filler sentences, then asked the E4B to find it. 4/4 perfect retrieval. Zero degradation at any depth. The model also passed multi-turn coherence 4/5 — it remembered the user's name, city, and hardware across 5 conversation turns. The only failure was on turn 5 where it forgot the original city. For a 7.5B model running locally at 24 tok/s on a Vulkan GPU — this is legitimately impressive context handling. The implications for agentic workflows are clear: this model can maintain state across long conversations without losing the thread.