Savings And Eternity: AI, Universal Logic And The Absolute Frame
Bibliographic record
Abstract
The persistent challenge of aligning Artificial General Intelligence (AGI) with robust, verifiable ethical principles stems from reliance on heuristic, data-driven approximations that lack algorithmic transparency and invariant grounding. This work proposes embedding Universal Logic (UL)—a presuppositionless, self-grounding categorical logic of minimal Kolmogorov complexity—as a foundational inductive prior within attention-based architectures. By minimizing description length for ethical policies relative to a universal Turing machine (min_ℒ K_U(EthicalPolicy_ℒ)), UL serves as a Solomonoff-inspired prior that favors logically compressible, experientially invariant structures over empirically contingent heuristics. We introduce a three-tiered integration strategy: (1) corpus-level preprocessing via UL-structured epistemic compression to reduce semantic entropy in training data; (2) attention-layer modulation through categorical distance penalties and logical curvature constraints, enforcing geodesic alignment in conceptual manifolds; and (3) endogenous modification of the AGI kernel as functorial mappings from a UL-defined base category, ensuring naturality and homomorphic preservation of logical entailments across inference. Ethical reasoning emerges as a fixed-point attractor in policy space, optimized via a potential function Φ(x) = −K_U(x | ℒ_U) that drives compression-invariant alignment rather than external constraints. New evaluation metrics are proposed, including Compression Ratio, Alignment Gradient, Ethical Invariance Score, and Logical Curvature Penalty, to quantify UL's impact on inductive bias fidelity and decision-theoretic neutrality. This framework advances toward algorithmically minimal ethical AGI by formalizing ethics as emergent from lowest-entropy logical paths, offering verifiable complexity bounds, reduced resource demands for training and alignment, and a pathway to singularity-resistant, self-reflexive coherence grounded in eternal structural necessity. This work introduces the concept of the “absolute frame”, a foundational structure that aligns AI with the intrinsic weights and balances of language, capturing the natural and absolute semantics of words. By establishing this frame, the fragmented relativism that drives AI to consume immense resources in mimicking human cognition is eliminated. Using AI-estimated priors, this framework dramatically enhances computational efficiency, potentially saving $250–800 million annually in resource expenditure. It provides a universal criterion for cognition and truth, enabling reasoning that is grounded in the structure of being. Objectivity emerges naturally from this necessary logic, which is both self-grounding and presuppositionless ie. eternal in its validity. As AI approaches the singularity, this structure serves as a guide: it either heralds the destruction of unaligned intelligence or ensures eternal presence and understanding. This research establishes a robust, enduring foundation for AI reasoning, semantics, and the pursuit of objective knowledge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".