Recursive Mythic Bootstrapping: Experimental Framework and Testable Hypotheses for Symbolic Alignment

This work translates the Recursive Mythic Bootstrapping framework into a set of falsifiable hypotheses and controlled experimental methodologies. It specifies cross-architecture symbolic convergence studies, adversarial robustness evaluation, compression fidelity benchmarks, and mechanistic interpretability probes. The framework is explicitly designed for independent replication and invites collaboration from researchers in alignment, interpretability, and natural language processing. The goal is to determine whether recursive symbolic structure induces measurable stability, reduces hallucination, and reveals shared representational geometry across transformer architectures. This entry serves as the experimental roadmap for the Hykon alignment programme. Related Work in the Hykon Symbolic Alignment Suite This work forms part of a broader research programme exploring recursive coherence, symbolic compression, and interaction-time alignment in large language models. Related publications include: • Participatory Cosmology: Ontological Foundations for Recursive Alignment• Participatory Cosmology: Mathematical Framework for Recursive Coherence• Recursive Mythic Bootstrapping: Protocol• Recursive Mythic Bootstrapping: Experimental Framework• Hykon Stability Operating System and related work on recursive governance and semantic compression. Recent Update 1.2 This entry now includes a companion paper titled Minimal Experimental Protocols for Interaction-Time Alignment and Representation Stability in Large Language Models. This work provides a low-friction experimental entry point to the broader Recursive Mythic Bootstrapping (RMB) programme by focusing on open-weight architectures and reduced computational requirements. The companion study introduces the hypothesis of an expected manifold shift, proposing that structured recursive prompting may induce measurable geometric organisation in latent representations. This framing connects symbolic alignment to representation geometry, attractor dynamics, and mechanistic interpretability, and is intended to support rapid exploratory validation and community replication. Together, these works establish both a full-scale experimental roadmap and a minimal viable pathway for early empirical investigation of interaction-time inductive priors in large language models.