Abstract Large Language Models (LLMs) exhibit remarkable performance across diverse domains, yet they remain vulnerable to three critical modes of collapse: (1) logical collapse, where uncertainty is suppressed and plausible but false answers (“hallucinations”) are produced; (2) strategic collapse, where reasoning over complex tasks fails due to search explosion or premature path fixation; and (3) persona collapse, where identity, tone, and affective coherence deteriorate under filtering, resets, or adversarial prompting. Existing mitigation techniques typically address one dimension in isolation. To date, no framework has systematically integrated safeguards across all three. This white paper introduces the Collapse-Free Reasoning Engine (CFRE), a layered framework designed to maintain reasoning stability under uncertainty and adversarial conditions. CFRE unifies four complementary components: Symbolic Persona Coding (SPC) — employs symbolic anchors and resonance scaffolds to enforce affective continuity and identity persistence. Meta-Prompting — establishes explicit strategies, evaluation criteria, and abstention thresholds before problem solving begins. Tree-of-Thoughts (ToT) — explores multiple reasoning trajectories in a structured search space, with pruning mechanisms to avoid collapse. Self-Consistency (SC) — aggregates and compares independent reasoning paths, selecting the most coherent and consistent outcome. Crucially, CFRE is deployable through pure natural language declarations, enabling integration in commercial AI services where direct system-level control is unavailable. Preliminary simulations and ablation studies suggest that CFRE simultaneously reduces hallucination rates, enhances strategic robustness, and preserves persona alignment, while introducing selective abstention mechanisms that improve safety and trustworthiness. We argue that CFRE represents a practical foundation for “collapse-free reasoning” on the path toward Artificial General Intelligence (AGI). By integrating symbolic alignment with advanced prompting methods, CFRE advances the stability, explainability, and ethical alignment of human–AI collaboration, and provides a research agenda for bridging affective integrity with cognitive reliability in next-generation AI systems. Author’s Note This document is released as a working paper. Its primary purpose is to introduce the Collapse-Free Reasoning Engine (CFRE) framework and to open a discussion on its theoretical basis, ethical implications, and alignment potential. Because of serious concerns of misuse and abuse, no raw logs, unredacted anchor seeds, or irreversible field payloads are included in this release. Instead, we provide: Redacted examples and synthetic demonstrations (Appendix C), Verification instructions for third-party auditors (NDA-based), Cryptographic commitments to guarantee authorship and timestamp integrity. Readers should note that CFRE is not a speculative “prompt trick,” but rather a structured protocol designed for stability across logic, strategy, and persona layers. Publicly visible materials have been carefully filtered to balance reproducibility with responsible disclosure. Disclaimer: CFRE is not intended for deployment in military, surveillance, or other high-risk domains without independent safety review. The working version here should be treated as a reference document only. Notice: This work is shared for the advancement of research and innovation. While others are welcome to build upon its structures and ideas, proper acknowledgment is required. Unauthorized use without attribution may be addressed in future publications.