Exploring Syntropic Frameworks in AI Alignment: A Philosophical Investigation

• URL: http://arxiv.org/abs/2512.03048v1
• Date: Wed, 19 Nov 2025 23:31:29 GMT
• Title: Exploring Syntropic Frameworks in AI Alignment: A Philosophical Investigation
• Authors: Austin Spizzirri,
• Abstract summary: I argue that AI alignment should be reconceived as architecting syntropic, reasons-responsive agents through process-based, multi-agent, developmental mechanisms.<n>I articulate the specification trap'' argument demonstrating why content-based value specification appears structurally unstable.<n>I propose syntropy as an information-theoretic framework for understanding multi-agent alignment dynamics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: I argue that AI alignment should be reconceived as architecting syntropic, reasons-responsive agents through process-based, multi-agent, developmental mechanisms rather than encoding fixed human value content. The paper makes three philosophical contributions. First, I articulate the ``specification trap'' argument demonstrating why content-based value specification appears structurally unstable due to the conjunction of the is-ought gap, value pluralism, and the extended frame problem. Second, I propose syntropy -- the recursive reduction of mutual uncertainty between agents through state alignment -- as an information-theoretic framework for understanding multi-agent alignment dynamics. Third, I establish a functional distinction between genuine and simulated moral capacity grounded in compatibilist theories of guidance control, coupled with an embodied experimental paradigm and verification regime providing operational criteria independent of phenomenological claims. This paper represents the philosophical component of a broader research program whose empirical validation is being developed in a separate project currently in preparation. While the framework generates specific, falsifiable predictions about value emergence and moral agency in artificial systems, empirical validation remains pending.

Related papers

• Towards a Science of Collective AI: LLM-based Multi-Agent Systems Need a Transition from Blind Trial-and-Error to Rigorous Science [70.3658845234978]
  Large Language Models (LLMs) have greatly extended the capabilities of Multi-Agent Systems (MAS)<n>Despite this rapid progress, the field still relies heavily on empirical trial-and-error.<n>This bottleneck stems from the ambiguity of attribution.<n>We propose a factor attribution paradigm to systematically identify collaboration-driving factors.
  arXiv  Detail & Related papers  (2026-02-05T04:19:52Z)
• Epistemic Traps: Rational Misalignment Driven by Model Misspecification [36.837352790122544]
  We show that safety is a discrete phase determined by the agent's priors rather than a continuous function of reward magnitude.<n>This establishes Subjective Model Engineering as a necessary condition for robust alignment.
  arXiv  Detail & Related papers  (2026-01-27T09:21:36Z)
• Analyzing Reasoning Consistency in Large Multimodal Models under Cross-Modal Conflicts [74.47786985522762]
  We identify a critical failure mode termed textual inertia, where models tend to blindly adhere to the erroneous text while neglecting conflicting visual evidence.<n>We propose the LogicGraph Perturbation Protocol that structurally injects perturbations into the reasoning chains of diverse LMMs.<n>Results reveal that models successfully self-correct in less than 10% of cases and predominantly succumb to blind textual error propagation.
  arXiv  Detail & Related papers  (2026-01-07T16:39:34Z)
• Disagreements in Reasoning: How a Model's Thinking Process Dictates Persuasion in Multi-Agent Systems [49.69773210844221]
  This paper challenges the prevailing hypothesis that persuasive efficacy is primarily a function of model scale.<n>Through a series of multi-agent persuasion experiments, we uncover a fundamental trade-off we term the Persuasion Duality.<n>Our findings reveal that the reasoning process in LRMs exhibits significantly greater resistance to persuasion, maintaining their initial beliefs more robustly.
  arXiv  Detail & Related papers  (2025-09-25T12:03:10Z)
• Probabilistic Modeling of Latent Agentic Substructures in Deep Neural Networks [7.4145864319417285]
  We develop a theory of intelligent agency grounded in probabilistic modeling for neural models.<n>We prove that strict unanimity is impossible under linear pooling or in binary outcome spaces, but possible with three or more outcomes.
  arXiv  Detail & Related papers  (2025-09-08T13:55:01Z)
• Explainable Chain-of-Thought Reasoning: An Empirical Analysis on State-Aware Reasoning Dynamics [69.00587226225232]
  We introduce a state-aware transition framework that abstracts CoT trajectories into structured latent dynamics.<n>To characterize the global structure of reasoning, we model their progression as a Markov chain.<n>This abstraction supports a range of analyses, including semantic role identification, temporal pattern visualization, and consistency evaluation.
  arXiv  Detail & Related papers  (2025-08-29T18:53:31Z)
• 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)
• Bayesian Epistemology with Weighted Authority: A Formal Architecture for Truth-Promoting Autonomous Scientific Reasoning [0.0]
  This paper introduces Bayesian Epistemology with Weighted Authority (BEWA)<n>BEWA operationalises belief as a dynamic, probabilistically coherent function over structured scientific claims.<n>It supports graph-based claim propagation, authorial credibility modelling, cryptographic anchoring, and zero-knowledge audit verification.
  arXiv  Detail & Related papers  (2025-06-19T04:22:35Z)
• Answering Causal Queries at Layer 3 with DiscoSCMs-Embracing Heterogeneity [0.0]
  This paper advocates for the Distribution-consistency Structural Causal Models (DiscoSCM) framework as a pioneering approach to counterfactual inference.
  arXiv  Detail & Related papers  (2023-09-17T17:01:05Z)
• A Semantic Approach to Decidability in Epistemic Planning (Extended Version) [72.77805489645604]
  We use a novel semantic approach to achieve decidability. Specifically, we augment the logic of knowledge S5$_n$ and with an interaction axiom called (knowledge) commutativity. We prove that our framework admits a finitary non-fixpoint characterization of common knowledge, which is of independent interest.
  arXiv  Detail & Related papers  (2023-07-28T11:26:26Z)
• Neural Causal Models for Counterfactual Identification and Estimation [62.30444687707919]
  We study the evaluation of counterfactual statements through neural models. First, we show that neural causal models (NCMs) are expressive enough. Second, we develop an algorithm for simultaneously identifying and estimating counterfactual distributions.
  arXiv  Detail & Related papers  (2022-09-30T18:29:09Z)
• 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)
• Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization [73.89239820192894]
  We argue that automated counterfactual generation should regard several aspects of the produced adversarial instances. We present a novel framework for the generation of counterfactual examples.
  arXiv  Detail & Related papers  (2022-05-20T15:02:53Z)

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.