Created: 2026-05-29
Status: internal public-safe draft. Not a claim of new physics.
This is not a proposed theory of everything, not a claim that consciousness causes quantum measurement, not a UAP/new-physics claim, and not an announcement of a finished result. The first goal is to learn where the intuitions break under known physics.
One-sentence thesis
We are building an AI-assisted research program that translates intuitions about observerhood, agency, information, memory, feedback, and attractors into existing physics languages, then tests them with small formal models and reproducible public-data analyses.
Working translation
| Intuitive term | First operational translation |
|---|---|
| Observer | Measurement channel plus state estimator. |
| Agent | Policy mapping observations or estimates to bounded actions. |
| Information | Mutual information, entropy, predictive information, likelihood, or uncertainty. |
| Memory | Explicit state variable carrying past measurement information, with reset or finite capacity when relevant. |
| Attractor | Basin, metastable state, stationary distribution, limit cycle, phase-locked regime, or recovery dynamic. |
| Control | Bounded intervention with explicit cost, delay, noise, and failure cases. |
First two models
1. Feedback and memory reset
Question: when does measurement plus feedback improve an objective after the cost of memory reset is included?
Minimal model: finite-state Markov system, noisy measurement, controller memory, feedback rule, reset cost proxy, and passive/random/no-information baselines.
Why it matters: it forces "information helps action" to pay a thermodynamic accounting cost instead of remaining a slogan.
2. Bounded observer-control of a noisy attractor
Question: when does a noisy or delayed observer-controller improve basin stability, phase locking, or prediction/control tradeoff under matched action cost?
Minimal model: stochastic double-well or driven oscillator, noisy observations, bounded feedback, delay/noise sweeps, and matched-cost baselines.
Why it matters: it turns "agency reshapes attractors" into measurable stability, switching, and cost curves.
First public-data discipline
Default: NASA Exoplanet Archive selection-effect analysis.
Question: how does the observing pipeline shape which worlds become visible in a public scientific catalog?
Why this matters: it gives a concrete, low-risk way to study observer-dependent knowledge without jumping to anomalies or speculative claims.
Guardrails
- Respect Lorentz invariance/covariance where relevant.
- Respect unitarity for closed quantum dynamics.
- Respect no-signaling.
- Account for thermodynamic costs in measurement, memory, feedback, reset, and reservoirs.
- Check known experimental bounds before any new-physics language.
- Treat UAP/anomaly material only as future sensor-chain discipline, not evidence in the first phase.
- Verify citations and equations from primary/review sources before any public use.
What we would ask a physicist
- What existing field is the closest serious home for this framing?
- Which terms are category mistakes?
- Which equations or known results should constrain the first two toy models?
- What would immediately kill this as a research direction?
- What is the smallest legitimate model or data analysis that would make the question worth continuing?
Current next step
Build a small notebook pack: a claims/constraints reading log, a Maxwell-demon-lite feedback-memory notebook, a double-well observer-control notebook, and an observer-pipeline public-data notebook. Success means reproducible toy models with failure cases, not a persuasive essay.