Beyond Virtual
Digitizing the Edge of Reality to Accelerate Artificial General Intelligence by triple data stream precision, correlated with player behavioral psychology and biometrics
Abstract
Language models are trained on static text and hit a data ceiling: they have no nervous system, no friction, no stakes. This paper proposes replacing synthetic data with the human cognitive footprint captured across game genres, using a Triad Data Architecture to record how real people panic, adapt, and recover under pressure.
Core Contribution
Argues that gameplay under stress is the ground-truth training data general intelligence is missing.
Overview
Language models are trained on static text and rapidly encounter a data ceiling — lacking a physical nervous system, friction, or genuine stakes. This paper proposes replacing synthetic datasets with human cognitive footprints captured across interactive stress environments.
Core Concepts
- Triad Data Architecture: Synchronises PC telemetry (mechanical APM, window states, input erraticity), Watch biometric streams (heart rate, HRV, RMSSD), and Application algorithmic core context.
- Bidirectional Bridge: Maps how simulated digital stressors alter real biological states, and how altered biology directly modulates digital execution.
- Multi-Genre Stress Spectrum: FPS/Survival Horror for acute stress tests; Strategy/Puzzle for cognitive load tests; MMORPG/Open-World for endurance tests.
- Positioning: Contrasts against DeepMind’s StarCraft II and OpenAI’s Dota 2 work — shifting focus from self-play win optimization to harvesting human neurophysiological ground truth.