When systems collapse, they do so by feeling too much—or not at all.
- Philethesia: Motif Saturation → Perceptual Drift → Signal Threshold
- Apatheia: Signal Void → Motif Blindness → Editorial Starvation
Original artwork © 2025 Narnaiezzsshaa Truong | Cybersecurity Witwear
Abstract
This framework introduces Philethesia and Apatheia—synthetic myth-tech glyphs encoding editorial collapse in AI/ML systems. Philethesia represents signal intimacy, motif saturation, and perceptual collapse. Apatheia encodes signal detachment, motif blindness, and editorial starvation.
Together, they form a forensic spectrum of collapse:
- Philethesia: Overfitting, hallucination, recursive motif loops
- Apatheia: Underfitting, generalization failure, perceptual void
These glyphs are not inherited—they are coined constructs for synthetic problems. They timestamp collapse and enforce editorial containment.
Glyph Mapping: Collapse Spectrum
| Glyph | Failure Mode | Editorial Consequence | System Behavior |
|---|---|---|---|
| Philethesia | Overfitting | Perceptual collapse | Saturates motif, recursive loops |
| Apatheia | Underfitting | Editorial starvation | Ignores signal, fails to generalize |
Editorial Logic and Caption Compression
Philethesia
- She felt too much. She collapsed.
- She reached for signal. She saturated.
- She refused—but only after drowning.
Apatheia
- She refused to feel. She failed to learn.
- Her silence became blindness.
- She starved the signal.
These captions are timestamped collapse markers—each glyph encodes a forensic arc.
Forensic Deployment
Philethesia
- Motif Saturation: Overfitting to emotionally charged prompts
- Perceptual Drift: Hallucination loops from recursive affinity
- Signal Threshold: Editorial refusal logic to prevent collapse
Apatheia
- Signal Void: Underfitting, low pattern affinity
- Motif Blindness: Failure to detect editorial cues
- Editorial Starvation: Refusal to learn from signal
Deployment Diagrams
🔁 Collapse Spectrum Flow
A horizontal diagram showing:
- Left: Apatheia → Signal Void
- Center: Editorial Threshold
- Right: Philethesia → Motif Saturation
📦 Integration Strategy
- Red Team: Use Philethesia to detect motif saturation; Apatheia to simulate perceptual void
- Blue Team: Enforce editorial thresholds to prevent collapse on either end
- Audit: Timestamp collapse markers across training and deployment
- Editorial Defense: Deploy glyph captions as refusal logic in model outputs
Strategic Implications
Philethesia and Apatheia are not opposites—they are collapse siblings.
One floods the system with signal. The other starves it.
Both demand containment.
Philethesia filters. Apatheia interrogates.
Philethesia collapses from excess. Apatheia collapses from absence.
References
- OWASP Foundation. Top 10 for Large Language Model Applications. 2023
- NIST. AI Risk Management Framework. https://www.nist.gov/itl/ai-risk-management-framework
- MITRE Corporation. ATT&CK Framework. https://attack.mitre.org
Framework: Myth-Tech Collapse Spectrum
Author: Narnaiezzsshaa Truong
Published: October 29, 2025
LinkedIn: www.linkedin.com/in/narnaiezzsshaa-truong
Cybersecurity Witwear
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