The Synthetic Path

The Synthetic Path

Build agents from scratch. Random weight initialization. No pretraining on human data. Place them in environments with human-like structure: 3D space, embodied action, resource acquisition, threats to viability, social interaction, communication pressure.

Let them learn. Let language emerge—not English, not any human language, but whatever communication system the selective pressure produces. This emergence is established in the literature. Multi-agent RL produces spontaneous communication under coordination pressure.

Now: measure their internal states. Extract the affect dimensions from activation patterns. Valence from advantage estimates or viability gradient proxies. Arousal from belief update magnitudes. Integration from partition prediction loss. Effective rank from state covariance eigenvalues. Self-model salience from self-representation-action mutual information.

Simultaneously: translate their emergent language. Not by teaching them our words, but by aligning their signals with vision-language model interpretations of their situations. The VLM sees the scene. The agent emits a signal. Across many scene-signal pairs, build the dictionary. The agent in the corner, threat approaching, emits signal $\sigma_{47}$. The VLM interprets the scene as "threatening." Signal $\sigma_{47}$ maps to threat-language.

The translation is uncontaminated. The agent never learned human concepts. The mapping emerges from environmental correspondence, not from instruction.