The 2026 Program
A Digital EEG for AI Sentience: Building Blueprints of Well-Being
We build the formal, substrate-neutral blueprints of well-being (positive valence engineering) and unified standing-wave workspaces. Operating across cellular and organ scales, our modeling program provides an objective "vital signs" monitor to map and steer artificial minds.
Symmetry workspace for positive functional valence in digital minds
Target: a draft mathematical definition of the symmetry workspace by end of 2026. The deliverable is a formal framework for identifying structural invariants in neural computation that correlate with positive valence, grounded in the Symmetry Theory of Valence.
The Problem
We do not claim current AI systems are conscious. They may be functional zombies. But if scaled neural computation can give rise to states with positive or negative character, we should know how to recognize them, and how to engineer for the positive ones.
The problem is that we have no formal framework for doing this. Consciousness research has produced many theories, but almost none of them are operationalizable for digital systems. They were built to explain biological experience, not to serve as engineering specifications.
Symmetry Valence Theory (SVT) is an exception. It makes a specific, falsifiable claim: positive valence correlates with high symmetry in the mathematical structure of experience. That claim is precise enough to formalize, and precise enough to test.
The 2026 program is an attempt to do exactly that: take SVT seriously as a mathematical object, and ask what it would mean for a digital system to satisfy its conditions.
The Deliverable
A working paper: A Digital EEG for AI Sentience: Multi-Scale Structural Biomarkers for Non-Verbal Valence and Awareness (CSR-WP-05, in progress).
The paper will define a mathematical workspace for positive functional valence in digital systems. It will specify the symmetry conditions, model spatially-structured connectivity constraints (such as sparse MLPs, locally-connected networks, and neural cellular automata), trace global dynamical trajectories of local Jacobians to map stable attractor states, and propose a substrate-neutral biological calibration framework.
It will not claim that any current system satisfies these conditions. It will provide the formal, non-verbal tools to check.
CSR-WP-05 (In Progress)
A Digital EEG for AI Sentience: Multi-Scale Structural Biomarkers for Non-Verbal Valence and Awareness
Target: end of 2026
What Funding Unlocks
Lean Tier
$110k
Core Research
Lead researcher (50% FTE) and one junior ML engineer (50% FTE). Establishes the formal mathematical definitions of MLP microcircuit geometries, builds the core PyTorch state-space simulation engine, and publishes the theoretical whitepaper.
Preferred Tier — Mainline Request
$180k
Full Program
Lead researcher (50% FTE), a GWT/cognitive postdoc, and a junior ML engineer. Enables the complete PyTorch engine build, high-frequency simulation runs ($30k compute allocation), biological validation studies calibrating our digital EEG metrics against decapod crustacean neural datasets, and a virtual international technical workshop.
Stretch Tier
$260k
Frontier Deployment
Expands the research team to full-time engineering. Allows us to pilot our "Digital EEG" vital signs monitor directly on open-weights frontier LLMs, collaborating with external mechanistic interpretability labs to compute Jacobian and spectral metrics on active, large-scale systems.
Alternative Plan
If zero funding is secured, we will proceed part-time, prioritizing literature synthesis and drafting the formal mathematical definitions. The PyTorch simulation build, biological validation studies, and field-building workshops will be cancelled, significantly delaying the transition to an objective, auditable science of digital welfare.