Immutability Does Not Guarantee Trust: A Formal and Logical Refutation
- URL: http://arxiv.org/abs/2507.08844v1
- Date: Tue, 08 Jul 2025 09:35:52 GMT
- Title: Immutability Does Not Guarantee Trust: A Formal and Logical Refutation
- Authors: Craig S Wright,
- Abstract summary: We define immutability as the cryptographic persistence of historical states in an append-only data structure.<n>We demonstrate that immutability neither entails nor implies correctness, fairness, or credibility.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: It is frequently claimed in blockchain discourse that immutability guarantees trust. This paper rigorously refutes that assertion. We define immutability as the cryptographic persistence of historical states in an append-only data structure and contrast it with trust, understood as a rational epistemic expectation under uncertainty. Employing predicate logic, automata-theoretic models, and epistemic game-theoretic analysis, we demonstrate that immutability neither entails nor implies correctness, fairness, or credibility. Through formal constructions and counterexamples--including predictive fraud schemes and the phenomenon of garbage permanence--we show that the belief conflates structural and epistemic domains. Immutability preserves all data equally, regardless of veracity. Therefore, the assertion that immutability guarantees trust collapses under the weight of formal scrutiny.
Related papers
- Toward a Graph-Theoretic Model of Belief: Confidence, Credibility, and Structural Coherence [0.0]
This paper introduces a minimal formalism for belief systems as directed, weighted graphs.<n>Unlike logical and argumentation-based frameworks, it supports fine-grained structural representation without committing to binary justification status or deductive closure.<n>Its aim is to provide a foundational substrate for analyzing the internal organization of belief systems.
arXiv Detail & Related papers (2025-08-05T14:03:23Z) - Bayesian Evolutionary Swarm Architecture: A Formal Epistemic System Grounded in Truth-Based Competition [0.0]
We introduce a mathematically rigorous framework for an artificial intelligence system composed of probabilistic agents evolving through structured competition and belief revision.<n>The system establishes truth as an evolutionary attractor, demonstrating that verifiable knowledge arises from adversarial pressure within a computable, self-regulating swarm.
arXiv Detail & Related papers (2025-06-23T23:27:44Z) - On Immutable Memory Systems for Artificial Agents: A Blockchain-Indexed Automata-Theoretic Framework Using ECDH-Keyed Merkle Chains [0.0]
We introduce the concept of the Merkle Automaton, a cryptographically anchored, deterministic computational framework.<n>Each agent transition, memory fragment, and reasoning step is committed within a Merkle structure rooted on-chain.<n>This architecture reframes memory not as a cache but as a ledger - one whose contents are enforced by protocol, bound by cryptography, and constrained by formal logic.
arXiv Detail & Related papers (2025-06-16T08:43:56Z) - All You Need for Counterfactual Explainability Is Principled and Reliable Estimate of Aleatoric and Epistemic Uncertainty [27.344785490275864]
We argue that transparency research overlooks many foundational concepts of artificial intelligence.<n>Inherently transparent models can benefit from human-centred explanatory insights.<n>At a higher level, integrating artificial intelligence fundamentals into transparency research promises to yield more reliable, robust and understandable predictive models.
arXiv Detail & Related papers (2025-02-24T09:38:31Z) - Prototype-based Aleatoric Uncertainty Quantification for Cross-modal
Retrieval [139.21955930418815]
Cross-modal Retrieval methods build similarity relations between vision and language modalities by jointly learning a common representation space.
However, the predictions are often unreliable due to the Aleatoric uncertainty, which is induced by low-quality data, e.g., corrupt images, fast-paced videos, and non-detailed texts.
We propose a novel Prototype-based Aleatoric Uncertainty Quantification (PAU) framework to provide trustworthy predictions by quantifying the uncertainty arisen from the inherent data ambiguity.
arXiv Detail & Related papers (2023-09-29T09:41:19Z) - Zero-shot Faithful Factual Error Correction [53.121642212060536]
Faithfully correcting factual errors is critical for maintaining the integrity of textual knowledge bases and preventing hallucinations in sequence-to-sequence models.
We present a zero-shot framework that formulates questions about input claims, looks for correct answers in the given evidence, and assesses the faithfulness of each correction based on its consistency with the evidence.
arXiv Detail & Related papers (2023-05-13T18:55:20Z) - Logical Satisfiability of Counterfactuals for Faithful Explanations in
NLI [60.142926537264714]
We introduce the methodology of Faithfulness-through-Counterfactuals.
It generates a counterfactual hypothesis based on the logical predicates expressed in the explanation.
It then evaluates if the model's prediction on the counterfactual is consistent with that expressed logic.
arXiv Detail & Related papers (2022-05-25T03:40:59Z) - AmbiFC: Fact-Checking Ambiguous Claims with Evidence [57.7091560922174]
We present AmbiFC, a fact-checking dataset with 10k claims derived from real-world information needs.
We analyze disagreements arising from ambiguity when comparing claims against evidence in AmbiFC.
We develop models for predicting veracity handling this ambiguity via soft labels.
arXiv Detail & Related papers (2021-04-01T17:40:08Z) - Don't Just Blame Over-parametrization for Over-confidence: Theoretical
Analysis of Calibration in Binary Classification [58.03725169462616]
We show theoretically that over-parametrization is not the only reason for over-confidence.
We prove that logistic regression is inherently over-confident, in the realizable, under-parametrized setting.
Perhaps surprisingly, we also show that over-confidence is not always the case.
arXiv Detail & Related papers (2021-02-15T21:38:09Z) - A Weaker Faithfulness Assumption based on Triple Interactions [89.59955143854556]
We propose a weaker assumption that we call $2$-adjacency faithfulness.
We propose a sound orientation rule for causal discovery that applies under weaker assumptions.
arXiv Detail & Related papers (2020-10-27T13:04:08Z) - Reliable Post hoc Explanations: Modeling Uncertainty in Explainability [44.9824285459365]
Black box explanations are increasingly being employed to establish model credibility in high-stakes settings.
prior work demonstrates that explanations generated by state-of-the-art techniques are inconsistent, unstable, and provide very little insight into their correctness and reliability.
We develop a novel Bayesian framework for generating local explanations along with their associated uncertainty.
arXiv Detail & Related papers (2020-08-11T22:52:21Z) - Bypassing the Kochen-Specker theorem: an explicit non-contextual
statistical model for the qutrit [0.0]
We describe an explicitly non-contextual statistical model of hidden variables for the qutrit.
We observe that the existence of such an absolute frame of reference is not required by fundamental physical principles.
arXiv Detail & Related papers (2018-05-13T19:37:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.