Nathan Langley

Research

My research work — papers, active projects, and the code behind them. Everything is open source and reproducible; each entry links to its full materials.

Phase diagram from the singleton-attractor model

Singleton Attractors in Recursive Self-Improvement Dynamics

Nathan Langley

Preprint — 22-page paper with proofs and 11 reproducible simulations

A coupled-ODE model of when competitive recursive self-improvement collapses into single-agent dominance. The theorems are conditional, and calibration against Epoch AI compute and benchmark data does not yet support the critical divergence assumption — the paper says so.

View PDF Code
Subtext reading-phase visualization

Subtext: real-time activation-lens interpretability

Nathan Langley

Open-source instrument — 183 GitHub stars

Applies a Jacobian activation lens at nine depths inside a small LLM during live conversation, decoding hidden states through the vocabulary — with per-token inspection and session replay that runs in a browser without a GPU.

Live demo Code
Collective escape simulation from swarmsim

Emergent behavior in multi-agent systems

Nathan Langley

Computational study · undergraduate research, UNC Greensboro

Simulations of emergent flocking, order–disorder phase transitions, predator–prey dynamics, and self-organized criticality in sandpile models — self-organization in systems of many simple agents.

Code