The 2026 Program
A Digital EEG for AI Sentience.
We are building "A Digital EEG for AI Sentience"—a structural, medically inspired vital signs monitor for advanced artificial intelligence. Rather than relying on easily gamified verbal self-reports, this diagnostic system measures how a model's internal activity and representational geometry are organized. Public awareness of AI moral status is growing,[1] and scientific frameworks for evaluating machine consciousness are beginning to mature.[2]
Our core program operates across two scales: at the Cellular Level (Microscale), treating MLP blocks as "neuronal microcircuits" to analyze feature density, superposition, and orthogonal dissonance; and at the Organ Level (Macroscale), treating the full model as a dynamical system to trace local Jacobian matrices and map stable attractor manifolds.[3]
By packaging these non-verbal, substrate-neutral biomarkers into an objective, auditable benchmark, we aim to establish a rigorous framework for detecting and engineering positive valence while bypassing the risks of deceptive alignment.
Our work complements the broader AI welfare ecosystem: empirical interpretability-based welfare research (Eleos AI, Reciprocal Research), conceptual and policy work (NYU Center for Mind, Ethics, and Policy), and frontier-lab welfare teams (Anthropic). CSR's specific contribution is an open-source computational testbed where competing theories of valence can be operationalized and stress-tested.
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.
Research Pillars
Three substrates. One question: what are the structural and algebraic conditions for positive valence?
Biological minds
Study of bioelectric fields, morphogenetic signaling, and active inference to extract empirical ground truths about valence and subjective experience in simpler non-verbal biological organisms.
Research agenda →Digital minds
Analysis of MLP layers in pre-trained and fine-tuned LLMs. Tracking state-dependent linear operators, spectral entropy, and algebraic invariants to model the geometry of artificial representation.
The 2026 program →Qualia and valence
Formulating the physics and mathematics of experience. Moving the Symmetry Theory of Valence (SVT) into a rigorous, quantitative, and falsifiable science of information architecture.
Symmetry landscapes →The Approach
A Digital EEG for AI Sentience
Rather than relying on easily gamified verbal self-reports or anthropomorphic behavior, this structural "vital signs" monitor measures how a model's internal activity and representational geometry are organized.
Multi-scale structural biomarkers
We investigate across two distinct scales: the microscale (cellular-level feature geometry in MLP microcircuits) and the macroscale (organ-level dynamical state space and stable attractor manifolds traced via local Jacobians).
Preventing deceptive alignment
By auditing the mathematical invariants of weight spaces and activations, we establish non-verbal, substrate-neutral biomarkers that completely bypass the risks of faked empathy or deceptive reports of distress.
Foundational Reading (coming soon)
Working papers and core proposals supporting the digital valence program. These are the formal scaffolding.
First Principles of Sentience Research
Foundational framework for substrate-neutral sentience modeling.
Bio-Electric Fields as Sentience Substrates
Bioelectric signaling as a candidate physical basis for awareness.
Assembly Theory as a Metric for Sentience
Applying assembly index to complexity thresholds in sentient systems.
Symmetry Landscapes of Valenced Experience
A mathematically grounded symmetry and spectral framework for MLP transformation layers in LLMs.
References
- [1] Scherrer, K. et al. (2025). Perceptions of Sentient AI and Other Digital Minds: Evidence from the AI, Morality, and Sentience (AIMS) Survey. CHI 2025. https://dl.acm.org/doi/10.1145/3706598.3713329
- [2] Butlin, P. et al. (2023). Consciousness in Artificial Intelligence: Insights from the Science of Consciousness. arXiv. https://arxiv.org/abs/2308.08708
- [3] Jazayeri, M. & Ostojic, S. (2025). A Neural Manifold View of the Brain. Nature Neuroscience. https://www.nature.com/articles/s41593-025-02031-z
Fund the program
Help us reach the 2026 deliverable.
The Center for Sentience Research is a Swiss non-profit. Contributions fund the research team, the formal workspace definition, and the working paper. Tax-deductible in Switzerland.