Activation geometry / training governance / alignment evaluation
Arca Futura develops systems for reading, evaluating, and governing model behavior at the structural level - from depthwise activation geometry to diagnostic alignment suites.
Training-governance research built on activation geometry and real-time instability signals.
A 77-model preprint study of recurring depthwise activation landmarks in decoder-only LLMs.
Landmark-aware ablation work testing whether depth coordinates reveal load-bearing attention heads.
Founder, Arca Futura LLC
Earlier projects explored memory, tutoring, and adversarial evaluation. The current work is narrower and stronger: measuring the internal geometry of model training, turning those signals into governance tools, and publishing diagnostic benchmarks with careful evidence labels.
Current focus
AEGIS is a training-governance system that monitors internal activation geometry during model training. Instead of waiting for loss curves to expose failure, it tracks structural signals such as PCA movement, tension acceleration, and layer-ratio volatility.
The public claim is deliberately modest: AEGIS is an active research and infrastructure effort, grounded in the Hau Curve preprint and supported by controlled pilot runs. Production-scale validation remains the next frontier.
Depthwise activation landmarks documented across a 77-model cohort.
Controlled runs indicate internal geometry can move before obvious external failure.
Evidence is infrastructure-oriented and diagnostic, not a blanket claim about deployment safety.
Public research
The Hau Curve v2 paper identifies a recurring tri-phasic activation geometry across decoder-only transformer models, with early-training convergence and stable depthwise landmarks. This work is the foundation for AEGIS and the later functional-anatomy studies.
This follow-on study asks whether landmark-relative depth coordinates help identify attention heads whose causal importance is not explained by salience alone. It frames the Hau Curve as a practical coordinate system for transformer functional anatomy.
StratEval is a diagnostic evaluation suite for instrumental convergence and strategic misalignment under structured pressure. It is meant to characterize elicited behavior under authored scenario frames, not rank models for deployment.
Pipeline context
Source refinement and motivational signal scoring, developed by ZeroThreeSixNine Ventures.
Activation-geometry monitoring and training governance, developed by Arca Futura.
Motivational alignment architecture under active research and validation.
PYPER3 is best described publicly as a collaboration and research pipeline. Some materials are investor-facing or confidential; this site names the architecture without importing private pricing, customer, or distribution claims.
Earlier prototypes