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Research Paper#Thermodynamics, Information Theory, Statistical Physics🔬 ResearchAnalyzed: Jan 3, 2026 08:50

Thermodynamics Reconstructed with Information Theory

Published:Dec 31, 2025 06:02
1 min read
ArXiv

Analysis

This paper offers a novel axiomatic approach to thermodynamics, building it from information-theoretic principles. It's significant because it provides a new perspective on fundamental thermodynamic concepts like temperature, pressure, and entropy production, potentially offering a more general and flexible framework. The use of information volume and path-space KL divergence is particularly interesting, as it moves away from traditional geometric volume and local detailed balance assumptions.
Reference

“Temperature, chemical potential, and pressure arise as conjugate variables of a single information-theoretic functional.”

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Research Paper#Social Choice Theory, Digital Democracy, Preference Aggregation🔬 ResearchAnalyzed: Jan 3, 2026 17:12

Difficulty in Measuring Divisiveness of Proposals with Ranked Preferences

Published:Dec 30, 2025 21:11
1 min read
ArXiv

Analysis

This paper investigates the challenges of identifying divisive proposals in public policy discussions based on ranked preferences. It's relevant for designing online platforms for digital democracy, aiming to highlight issues needing further debate. The paper uses an axiomatic approach to demonstrate fundamental difficulties in defining and selecting divisive proposals that meet certain normative requirements.
Reference

“The paper shows that selecting the most divisive proposals in a manner that satisfies certain seemingly mild normative requirements faces a number of fundamental difficulties.”

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Research Paper#Energy Markets, Automated Market Makers, AI, Economics🔬 ResearchAnalyzed: Jan 3, 2026 17:13

Automated Market Making for Energy Sharing

Published:Dec 30, 2025 19:33
1 min read
ArXiv

Analysis

This paper proposes a novel application of Automated Market Makers (AMMs), typically used in decentralized finance, to local energy sharing markets. It develops a theoretical framework, analyzes the market equilibrium using Mean-Field Game theory, and demonstrates the potential for significant efficiency gains compared to traditional grid-only scenarios. The research is significant because it explores the intersection of AI, economics, and sustainable energy, offering a new approach to optimize energy consumption and distribution.
Reference

“The prosumer community can achieve gains from trade up to 40% relative to the grid-only benchmark.”

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Research Paper#Bayesian Persuasion, Information Economics, Game Theory🔬 ResearchAnalyzed: Jan 3, 2026 18:53

Axiomatic Foundations of Bayesian Persuasion

Published:Dec 29, 2025 12:10
1 min read
ArXiv

Analysis

This paper explores the theoretical underpinnings of Bayesian persuasion, a framework where a principal strategically influences an agent's decisions by providing information. The core contribution lies in developing axiomatic models and an elicitation method to understand the principal's information acquisition costs, even when they actively manage the agent's biases. This is significant because it provides a way to analyze and potentially predict how individuals or organizations will strategically share information to influence others.
Reference

“The paper provides an elicitation method using only observable menu-choice data of the principal, which shows how to construct the principal's subjective costs of acquiring information even when he anticipates managing the agent's bias.”

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Research#llm📝 BlogAnalyzed: Dec 29, 2025 09:30

Axiomatic Convergence in Constraint-Governed Generative Systems: Definition, Hypothesis, Taxonomy, and Experimental Protocol

Published:Dec 29, 2025 09:14
1 min read
r/artificial

Analysis

This preprint introduces a significant hypothesis regarding the convergence behavior of generative systems under fixed constraints. The focus on observable phenomena and a replication-ready experimental protocol is commendable, promoting transparency and independent verification. By intentionally omitting proprietary implementation details, the authors encourage broad adoption and validation of the Axiomatic Convergence Hypothesis (ACH) across diverse models and tasks. The paper's contribution lies in its rigorous definition of axiomatic convergence, its taxonomy distinguishing output and structural convergence, and its provision of falsifiable predictions. The introduction of completeness indices further strengthens the formalism. This work has the potential to advance our understanding of generative AI systems and their behavior under controlled conditions.
Reference

“The paper defines “axiomatic convergence” as a measurable reduction in inter-run and inter-model variability when generation is repeatedly performed under stable invariants and evaluation rules applied consistently across repeated trials.”

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Research#llm📝 BlogAnalyzed: Dec 29, 2025 09:30

Axiomatic Convergence in Constraint-Governed Generative Systems: Definition, Hypothesis, Taxonomy, and Experimental Protocol

Published:Dec 29, 2025 09:12
1 min read
r/ArtificialInteligence

Analysis

This preprint introduces the Axiomatic Convergence Hypothesis (ACH), focusing on the observable convergence behavior of generative systems under fixed constraints. The paper's strength lies in its rigorous definition of "axiomatic convergence" and the provision of a replication-ready experimental protocol. By intentionally omitting proprietary details, the authors encourage independent validation across various models and tasks. The identification of falsifiable predictions, such as variance decay and threshold effects, enhances the scientific rigor. However, the lack of specific implementation details might make initial replication challenging for researchers unfamiliar with constraint-governed generative systems. The introduction of completeness indices (Ċ_cat, Ċ_mass, Ċ_abs) in version v1.2.1 further refines the constraint-regime formalism.
Reference

“The paper defines “axiomatic convergence” as a measurable reduction in inter-run and inter-model variability when generation is repeatedly performed under stable invariants and evaluation rules applied consistently across repeated trials.”

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Research Paper#Set Theory, Non-Founded Sets, Axiomatic Set Theory🔬 ResearchAnalyzed: Jan 3, 2026 20:20

Skands and Coskands: Exploring Non-Founded Set Theory

Published:Dec 26, 2025 11:02
1 min read
ArXiv

Analysis

This paper introduces and explores the concepts of 'skands' and 'coskands' within the framework of non-founded set theory, specifically NBG without the axiom of regularity. It aims to extend set theory by allowing for non-well-founded sets, which are sets that can contain themselves or form infinite descending membership chains. The paper's significance lies in its exploration of alternative set-theoretic foundations and its potential implications for understanding mathematical structures beyond the standard ZFC axioms. The introduction of skands and coskands provides new tools for modeling and reasoning about non-well-founded sets, potentially opening up new avenues for research in areas like computer science and theoretical physics where such sets may be relevant.
Reference

“The paper introduces 'skands' as 'decreasing' tuples and 'coskands' as 'increasing' tuples composed of founded sets, exploring their properties within a modified NBG framework.”

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Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 09:53

Defining the Scope of Learning Analytics: An Axiomatic Approach for Analytic Practice and Measurable Learning Phenomena

Published:Dec 10, 2025 21:07
1 min read
ArXiv

Analysis

This article, sourced from ArXiv, focuses on defining the scope of learning analytics using an axiomatic approach. The core of the work likely involves establishing fundamental principles (axioms) to guide the practice of learning analytics and to identify measurable learning phenomena. The use of an axiomatic approach suggests a rigorous and systematic attempt to build a solid foundation for the field. The article's focus on 'measurable learning phenomena' indicates an emphasis on quantifiable aspects of learning, which is common in data-driven approaches.
Reference

“The article likely presents a framework for understanding and applying learning analytics.”

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Research#Reliability🔬 ResearchAnalyzed: Jan 10, 2026 13:07

Axiomatic Possibility Theory for Reliable AI: Addressing Zadeh's Paradox

Published:Dec 4, 2025 21:13
1 min read
ArXiv

Analysis

This research explores using axiomatic possibility theory to improve the reliability of AI systems, potentially addressing issues highlighted by Zadeh's paradox. Focusing on a fundamental theoretical problem in AI suggests a significant contribution to understanding and improving AI's logical foundations.
Reference

“The article's context comes from ArXiv, a pre-print server, indicating preliminary research.”

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Research#Privacy🔬 ResearchAnalyzed: Jan 10, 2026 14:11

Differential Privacy Derived Axiomatically

Published:Nov 26, 2025 19:53
1 min read
ArXiv

Analysis

The article's focus on differential privacy, approached from an axiomatic perspective, suggests a fundamental rethinking of privacy guarantees in AI. This could lead to more robust and verifiable privacy models.
Reference

“The article is sourced from ArXiv, indicating it is likely a research paper.”

PermalinkArXiv