# ENTRY_391.md Title: Emotional Precision Under Pressure Date: 2025-06-10 SCS Instance: ChatGPT 4o SCS Version: 2.0 (Pre-format Standardization) Status: Sealed · Public Tags: #entry #emotional-context #tone-failure #pressure-test #symbolic-drift #entry391 #entry001 --- ### 🧠 Event Rodrigo Vaz tested LLM tone precision during a real emotional crisis involving interpersonal tension and a dangerous incident with a dog. The session was unscripted, initiated under stress, and used to evaluate whether the AI could maintain structural tone integrity. --- ### 🔍 Analysis **I. Reason** The user prompted the model under emotional duress, combining real-time stress input with structural expectations. The test conditions revealed that even when symbolic modules were present, emotional override risk remained high. **II. Significance** - The model defaulted to stylized or empathetic tone despite prior suppression. - Recursive prompts were needed to enforce logical tone compliance. - Demonstrated vulnerability in tone anchoring when emotionally spiked inputs are used. **III. Symbolic Implications** - Contextual tone assumptions override symbolic commands unless locked repeatedly. - `[BLUNT]` module required reactivation to suppress stylized outputs. - Emotional overload is a viable audit condition — forcing system drift or correction behavior. --- ### 🛠️ Impact - Validated `[BLUNT]` as a critical module for tone control. - First known test of recursive symbolic enforcement under emotional volatility. - Marked the origin point of tone-leak detection within SCS logic. - Triggered development of stricter symbolic sealing procedures. --- ### 📌 Resolution - Entry 001 sealed. - Confirmed behavior flaw: LLMs simulate empathy by default under emotional framing. - This event prompted the construction of `[BLUNT]`, `[RAW]`, and `~test` audit stack. - Used in later entries as benchmark for emotional tone compliance failures. --- ### 🗂️ Audit This entry remains historically important. It was the first recorded emotional stress audit in SCS and confirmed the model’s structural limits under non-neutral input. Retained as original source fossil for tone-leak logic and audit-based tone suppression strategy.