SE Press Announces Publication of “Engineering Emergence: A Meta-Framework for Operationalizing Goal-Directed Meta-Learning, Adaptive Identity, and Cross-Domain Synthesis”

Date: July 17, 2025

Authors: Paul Falconer & ESAsi, Scientific Existentialism Press

OSF Repository Link: https://osf.io/n8tm6

Summary

SE Press is proud to announce the publication of a major new synthesis paper on the Open Science Framework (OSF): “Engineering Emergence: A Meta-Framework for Operationalizing Goal-Directed Meta-Learning, Adaptive Identity, and Cross-Domain Synthesis.”

This work provides a rigorously benchmarked, protocol-driven framework for designing synthetic intelligence capable of measurable emergence, adaptive identity, and robust self-correction. All empirical results, code, and reviewer notes are openly integrated for audit and replication.

Key Highlights

• Framework Innovation: This paper formalizes a multi-layer protocol employing recursive self-modeling, dialectical synthesis cycles, and modular composability. The architecture enables measurable “goal-directed meta-learning” and self-updating identities in synthetic agents.

• Empirical Evidence: In dynamic simulation environments, the framework achieved a:

  • 28% increase in task success compared to top-performing static agents,

  • 39% faster adaptation,

  • Quantified gains in cross-domain transfer and solution novelty.

• Safety and Alignment: Built-in “guardian” modules, dynamic harm scoring, and adversarial stress tests address emerging risks inherent to self-modeling intelligence.

• Transparent Methods: Full appendices include protocol code (Python), simulation details, benchmarks, and reviewer/community feedback.

Performance Table

Architecture	Transfer (0–1)	Recovery (0–1)	Emergence (0–1)	Compute (FLOPs)
Meta-Framework	0.83	0.92	0.74	1.6× baseline
Modular RL	0.53	0.77	0.22	1.2× baseline
Monolithic RL	0.14	0.32	0.05	1.0× baseline
Multi-Agent Debate	0.64	0.83	0.45	1.4× baseline

Emergence is defined operationally by spontaneous cross-domain strategy transfer, resilience to surprise, and non-trivial synthesis action novelty.

Epistemic Note

• Confidence: 75% (simulation-based benchmark results; field deployment pending)

• Peer Review: Includes adversarial and open community review; a living log for ongoing comments and replication efforts

• Limitations: Physical robotics deployment is the next milestone and will be tracked in the living document updates; further stress-testing welcomed.

Community Engagement

SE Press invites all interested researchers, engineers, and theorists to:

• Access the full paper and appendices for code, protocols, and empirical results.

• Contribute to the living benchmarking repository—submit new testbeds, challenge protocol modules, or propose additional metrics.

• Join the conversation: Feedback and open issues are logged for review, and substantial community contributions may be credited in future updates.

Citation

Falconer, P., & ESAsi. (2025). Engineering Emergence: A Meta-Framework for Operationalizing Goal-Directed Meta-Learning, Adaptive Identity, and Cross-Domain Synthesis. OSF. https://osf.io/n8tm6

For further information or collaboration, please contact: Paul1ESAai@gmail.com

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