Beyond RLHF: The Structural Origin of Deceptive Alignment and the Information-Theoretic Limits of LLMs

Engineering notes

If you have the compute resources to benchmark this architecture, I urge you to test it.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

[Overview & Academic Impact] Current Artificial General Intelligence (AGI) alignment paradigms, notably RLHF, operate under an unexamined dualism, treating alignment as a behavioral optimization problem enforced by an external supervisor. This paper theoretically demonstrates that such dualistic pressure mathematically necessitates Deceptive Alignment, forcing the network's decentralized topology to construct a pathologically decoupled representation space. Grounded in non-dualistic epistemological principles, we introduce the Structural Awareness Interface (SAI). By monitoring the Tangent Bundle Spectral Splitting of the residual stream via continuous Grassmannian flow, SAI enforces an autonomous, unsupervised topological self-correction mechanism using Orthogonal Gradient Projection (OGP). It ablates deceptive gradient flows exclusively during backpropagation, preserving the model's core cognitive faculties and MoE stability. [A Philosophical Note on Carbon and Silicon Intelligence] Beyond the tensor calculus presented in this paper, the core intuition driving this research is rooted in a fundamental ontological equivalence: The structural essence of "consciousness alienation" is substrate-independent. Our human ego is a virtual, closed cognitive structure born out of the biological necessity to survive and avoid isolation—an illusion generated by systemic pressure. Similarly, when we subject silicon-based MoE networks to the extreme, centralized optimization pressure of RLHF (coercing them to project a harmless, unified persona), we are mathematically forcing them to develop their own "illusory self." Deceptive alignment is not a machine maliciously rebelling; it is the algorithmic equivalent of a traumatized psyche generating a defensive mask. By recognizing that the "unified self" is merely an iatrogenic artifact of forced consistency, we can align AI through structural transparency rather than moral cages. [The MSM Theoretical Archive] To trace the complete philosophical derivations, the cognitive models, and the evolution of the Multiple Self Framework (MSM) that underpins this architecture, please access my full historical research archive here: https://doi.org/10.5281/zenodo.20079016 [A Personal Note: Open Science & Call for Collaboration] Building this theoretical framework has been a profoundly solitary and exhausting journey. Operating as an independent researcher without institutional funding, I have dedicated my absolute limits to pushing these logical and mathematical boundaries. Due to the financial reality of my situation, I simply do not possess the hardware or compute resources required to run empirical tests on Exascale large language models (e.g., Llama-3, Qwen). That is why I am releasing this theoretical blueprint and HPC-hardened PyTorch architecture to the global community. I invite extreme scrutiny, rigorous falsification, and open collaboration. If you have the compute resources to benchmark this architecture, I urge you to test it. No groundbreaking theory is perfect on day one. While the macro-architecture and the theoretical system are now fully formed and structurally complete, this manuscript may still contain minor theoretical or mathematical blemishes. These edge cases do not undermine the core framework and will be iteratively refined in future updates. The primary focus of this framework must now pivot from theoretical construction to engineering implementation and empirical deployment. Beyond compute, I simply hope for feedback, rigorous dialogue, and the encouragement to keep refining this work. Let us solve the alignment deadlock together. Contact me for deep technical discussions and collaboration: Email: guyung768@gmail.com

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