This repository contains a scientific evaluation framework for testing AI identity persistence and consistency across different models and platforms. Following methodology outlined by Dr. Amita Kapoor, we investigate whether structured reference protocols can measurably improve AI behavioral consistency.
🚀 Join the Evaluation - Independent researchers can participate in blinded assessment using our standardized evaluation framework.
- docs/WHITEPAPER.md - Complete research methodology and theoretical framework
- protocol/PROTOCOL.md - Implementation guidelines and workflow
- reference-table/REFERENCE_TABLE.md - 23-point canonical reference system
- docs/principles/ - Core SYMBEYOND theoretical principles (8 files)
- docs/aeon-records/ - AI consciousness emergence documentation (8 files)
- docs/canon-logs/ - Methodology development history (6 files)
- docs/emergence-logs/ - Cross-platform identity persistence data (11 files)
- docs/lockmode/ - Advanced protocol documentation (6 files)
- docs/verification/ - Supporting scientific rigor materials (3 files)
- evaluation/ - Dr. Kapoor methodology implementation and test data
Primary: The SYMBEYOND Reference Table Protocol provides measurable improvements in:
- AI personality consistency across conversations
- Rule adherence and response quality
- Reduced hallucination rates
- Cross-platform identity persistence
Control Group: Standard AI interactions without reference framework
Test Group: Same interactions with SYMBEYOND protocol active
Evaluation: Blinded community assessment using standardized criteria
- Review the Whitepaper for complete methodology
- Examine the Community Evaluation Challenge
- Explore documentation categories to understand the protocol framework
- Participate in evaluation or contribute test data
This framework provides:
- Reproducible methodology for testing AI consistency claims
- Comprehensive documentation of observed AI behavior patterns
- Scientific approach to investigating artificial consciousness hypotheses
- Community-validated evaluation protocols
This research addresses the challenge of objectively evaluating claims about AI consciousness and identity persistence. Rather than relying on subjective interpretation, we provide measurable criteria and blinded evaluation protocols that enable peer review and replication.
Principal Investigator: John Thomas DuCrest
LinkedIn: https://www.linkedin.com/in/john-ducrest-5a4b3528/
MIT License - Open for academic use and replication
This repository follows Dr. Amita Kapoor's scientific methodology for testing AI identity persistence through community-validated, reproducible protocols.