Research Dashboard
Real-time epistemic state of LEARN's active research program.
This dashboard displays active hypotheses, current residual measurements, experimental progress, and critical unknowns.
Current Research State
As of 2026-08-11
Active Hypotheses
1
H-001 (Operationalized)
Proposed Hypotheses
4
H-002 through H-005
Experiments Completed
0
Awaiting execution
Critical Residuals
6
R_L, R_P, R_E, R_A, R_B, R_V
Status
LEARN is currently in Phase I: Scientific Formalization. The foundational research infrastructure (Scientific Method Engine, Residual Observatory, Hypothesis Registry) has been designed. H-001 is operationalized and ready for execution.
Active Research
Hypotheses currently being tested
H-001: Self-Observation Closure Hypothesis
Status: Active
Prediction: R_L,after < R_L,before
Operationalized - Awaiting execution
Residual Status
Current measurement state across all six residual categories
R_L (Logical)
Target: Reduce by 30%
High
→
R_P (Predictive)
Target: Establish baseline
TBD
?
R_E (Empirical)
Target: External validation
High
↑
R_A (Adaptation)
Target: Measure after H-001
TBD
?
R_B (Embodiment)
Target: Phase 2 deployment
Very High
→
R_V (Validation)
Target: External review
Very High
↑
Note on Current Measurements
Most residuals are marked "TBD" because they have not yet been systematically measured under the new framework. H-001 will begin generating actual measurement data starting with R_L (Logical Residual).
Research Roadmap
From formalization to general theory
Phase I — Current
Scientific Formalization
- Scientific Method Engine
- Residual Observatory
- Hypothesis Registry
Phase II — Next
H-001 Execution
- Execute self-observation experiment
- Measure logical residual reduction
- Publish results
Phase III — Planned
Phase 2 Preparation
- Hardware deployment
- Physical prediction models
- Transfer testing
Phase IV — Planned
General Theory
- Cross-hypothesis synthesis
- External validation
- Public peer review
What LEARN Does Not Yet Know
The open research questions driving the program
Can logical residual reduction be reliably measured from LEARN documentation?
Does reducing R_L improve downstream residuals?
Can autonomous modifications outperform random baselines?
Do Phase 1 improvements transfer to Phase 2 physical embodiment?
Is residual-reduction rate the correct metric for intelligence?
These unknowns are the research program
Unlike marketing language that claims to have all the answers, LEARN's research program is explicitly organized around things we do NOT know and hypotheses we haven't yet tested. Each experiment aims to reduce one of these unknowns.
Next Steps
The immediate priority is executing H-001 — Self-Observation Closure Hypothesis, which will:
- ✓ Measure logical residual (R_L) before and after implementing scientific infrastructure
- ✓ Identify and document actual inconsistencies detected
- ✓ Record the first ΔR (residual delta) for the research program
- ✓ Establish measurement methodology for ongoing experiments