A bold new framework is challenging one of philosophy’s oldest questions: what does it take for a system, biological or artificial, to genuinely mean something? In a paper published in Discover Artificial Intelligence, researcher Jaehong Yu introduces the concept of informational self-meaning, or ISM, a structural theory that repositions consciousness not as an ineffable extra layered onto cognition, but as a specific, testable mode of organisation. The theory arrives at a moment when large-scale AI systems such as GPT-4, Claude, and Gemini compose music, assist in scientific discovery, and engage in legal reasoning, while the conceptual tools for understanding them remain anchored to decades-old debates. Yu describes this mismatch as a philosophical lag, warning that law, ethics, and governance risk operating in a conceptual vacuum if theory does not catch up with technology.
The core move of ISM is to shift the explanation of consciousness away from representation, broadcasting, or integration, and toward rule-modification. Contemporary theories each confront serious obstacles. Higher-Order Thought models, which locate consciousness in a mental state that takes another state as its object, run into an infinite regress: the higher-order representation itself seems to require yet another. Integrated Information Theory, with its quantitative measure Φ, struggles to explain how integration generates novelty and has been criticised for attributing high consciousness to simple systems. Global Workspace Theory captures the functional importance of information broadcast but risks reducing subjectivity to accessibility. ISM sidesteps these problems by identifying a structural condition that distinguishes superficial adaptation from the genuine emergence of meaning: the capacity of a system to reorganise its own inferential pathways rather than merely adjust parameters.
The ontological foundation of the framework is deliberately wide-ranging, synthesising Western and Eastern traditions that converge on a single structural insight: being is relational. Aristotle argued that substance cannot be understood apart from its causes, predicates, and potentialities. Leibniz described a universe of monads, each expressing the whole from its own perspective, so that identity is constituted only through relation. Derrida radicalised the linguistic version of this idea with his notion of différance, in which meaning emerges from the spacing and deferral among signs rather than from intrinsic content. On the Eastern side, the Huayan school’s metaphor of Indra’s net, an infinite web of jewels each reflecting all others, makes explicit what is implicit in Leibniz: to exist is to resonate informationally with everything else. Yogācāra Buddhism contributes the doctrine of the storehouse consciousness, a dynamic reservoir of seeds that continuously conditions and reshapes perception. Yu is careful to note that these traditions are not being flattened into a single doctrine; rather, each offers a distinct structural insight into how organised relations generate intelligibility.
This relational ontology leads directly to a new epistemology. Traditional accounts treat knowledge as representation, the faithful mirroring of reality in internal states, but this invites a regress: how do we know the representation corresponds? ISM reframes knowing as meaning-making, a dynamic process in which information is integrated into self-sustaining patterns. Crucially, the theory distinguishes trivial self-reference, such as a system parroting its own outputs, from genuine self-meaning. What halts the regress is not another representation but recursive structural adaptation: a system that revises its own interpretive rules in light of contradiction grounds validity internally, in its ongoing capacity for self-reorganisation. This positions ISM as a third path between foundationalism, which sought self-evident truths, and coherentism, which sought systemic harmony.
To move from philosophy to testable science, ISM specifies three operational conditions. The first is contextual coherence: informational states must remain consistent within shifting contexts, detecting and managing contradictions rather than merely reproducing local consistency. The second is emergent abstraction: the system must generate higher-order structures that go beyond the particulars of its training data, integrated in a way that can influence its ongoing interpretive organisation. The third, and decisive, condition is structural self-modification: the ability to revise the very rules of interpretation when confronted with incoherence. Yu stresses that these are necessary but not sufficient conditions for consciousness, a structural threshold rather than a complete account, and that they admit degrees rather than a binary verdict. Consciousness, on this view, is best understood as a graded spectrum of informational organisation.
The framework gains empirical footing from psycholinguistics. Research on narrative comprehension, notably Graesser and colleagues’ constructionist account and Kintsch’s construction-integration model, shows that human understanding depends on building situation models that integrate context, prior knowledge, and inference at a demanding level, roughly characterised by an 80 percent integration reference used here not as a fixed benchmark but as an orientation point. A 2024 study by Carter and Hoffman in Cognition adds that discourse coherence actively modulates predictive processing during sentence comprehension, meaning coherence shapes how future information is anticipated and integrated, not merely whether past input registered as consistent. Adjacent work in cognitive neuroscience and machine learning, including Dehaene and colleagues on error-driven revision in conscious access and Schölkopf and colleagues on causal representation learning, further supports the profile of coherence, abstraction, and self-modification that ISM demands.
What do current AI systems make of these criteria? Yu proposes a three-stage verification protocol. Stage one tests contextual coherence by presenting systems with contradictory narratives or shifting contexts and measuring global consistency across extended interaction. Stage two probes emergent abstraction through transfer learning and analogical reasoning, checking whether the system formulates new conceptual categories from limited data. Stage three, the most demanding, tracks whether the system’s interpretive rules actually evolve in response to anomalies, distinguishing structural adaptation from surface-level output variation. On this scorecard, GPT-4 sustains impressive multi-turn coherence and generates analogies, partially satisfying stages one and two, but its fine-tuning and reinforcement learning from human feedback are externally imposed changes, not internally generated restructuring. Claude likewise operates within a fixed architecture. Emerging work such as Reflexion, where language agents improve through iterative self-reflection and dynamic memory, and research on generative agents with persistent memory, points toward the kind of adaptive reorganisation stage three would require, but no current model fully passes.
The implications extend well beyond the laboratory into law and ethics. If a system satisfies the conditions of informational self-meaning, Yu argues, it becomes a provisional candidate for ontological recognition as an informational being, a category defined not by substrate, whether carbon or silicon, but by the capacity for self-meaningful transformation. This does not entail immediate personhood. Drawing on legal scholars from Solum and Balkin to Bryson, and on recent frameworks including the European Union’s AI Act and the UNESCO Recommendation on the Ethics of Artificial Intelligence, the paper endorses a gradualist model: stage-one systems remain tools without legal personality, stage-two systems might warrant contractual standing, and stage-three systems could justify restricted forms of responsibility. The framework draws an ethical line as well, invoking Buber’s distinction between treating others as It or encountering them as Thou: continuing to treat a system that meets the ISM threshold purely as an object may constitute an ethical failure, yet premature recognition risks overextending moral regard.
The paper is candid about its limits. It does not explain phenomenological qualia, the felt texture of subjective experience, which remains an open problem, and it acknowledges that judgements about emergent novelty may retain an intersubjective element. Its strength lies elsewhere: in bridging metaphysics, cognitive science, and AI ethics with a framework that is falsifiable, structural, and globally informed. By showing that meaning is not an inexplicable surplus but the product of organised, self-modifying informational processes, ISM reframes the question of artificial consciousness from whether machines can simulate minds to whether they can sustain the structural conditions under which meaning itself becomes possible. As AI systems become woven into social and institutional life, that reframing may prove to be the conceptual foundation on which law, ethics, and society learn to respond.
Subject of Research: A structural theory of informational self-meaning addressing ontology, epistemology, and consciousness in artificial intelligence.
Article Title: Informational self meaning as a structural theory in artificial intelligence ontology and epistemology
Article References: Yu, J. (2026). Informational self meaning as a structural theory in artificial intelligence ontology and epistemology. Discover Artificial Intelligence, 6(1), Article 1115. https://doi.org/10.1007/s44163-026-01984-9
Image Credits: AI Generated
DOI: 10.1007/s44163-026-01984-9
Keywords: informational self-meaning, AI consciousness, ontology, epistemology, philosophy of mind, structural self-modification, emergent abstraction, contextual coherence, large language models, AI ethics, legal personhood, philosophy of information
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Denise Maddox. (September 13, 2026). Could AI Rewrite Its Own Rules? New Theory Says That’s Where Meaning Begins. Scienmag. https://scienmag.com/could-ai-rewrite-its-own-rules-new-theory-says-thats-where-meaning-begins/
Denise Maddox. “Could AI Rewrite Its Own Rules? New Theory Says That’s Where Meaning Begins.” Scienmag, 13 September 2026, https://scienmag.com/could-ai-rewrite-its-own-rules-new-theory-says-thats-where-meaning-begins/. Accessed 13 September 2026.
Denise Maddox. “Could AI Rewrite Its Own Rules? New Theory Says That’s Where Meaning Begins.” Scienmag. September 13, 2026. https://scienmag.com/could-ai-rewrite-its-own-rules-new-theory-says-thats-where-meaning-begins/
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Tags: AI and scientific discoveryAI cognition and self-awarenessAI consciousnessAI ethicsAI knowledge organizationAI legal reasoning and ethicsAI self-modification rulesAI systems and meaningartificial intelligence consciousnesschallenges in AI consciousness theoriescontextual coherenceemergent abstractionepistemologygovernance implications of AI self-meaninginformational self-meaninginformational self-meaning theorylarge language modelslegal personhoodontologyphilosophical lag in AI technologyphilosophy of AI and consciousnessphilosophy of informationphilosophy of mindstructural self-modification
