This is an automated rejection. No LLM generated, heavily assisted/co-written, or otherwise reliant work.
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Author: Michal Harcej
Date: 2025-12-17
Abstract
Mainstream AI research is trapped in a category error, pursuing the simulation of "consciousness" and "understanding" while neglecting the more fundamental, measurable, and critical property of coherence. We present a novel architectural paradigm, the Sovereign Cognitive Ecosystem, which treats AI not as a nascent mind to be nurtured, but as a powerful, non-human force to be governed by a rigorous physics of information. This paper introduces a set of interlocking principles—Immutable Causal History (The Chronicle Protocol), Real-Time Coherence Governance (The Tau Protocol), and Sovereign Systemic Defense (The Boundary Protocol)—which, when combined, create a provably stable and trustworthy AI system. We demonstrate through a series of reproducible experiments, including the "Unsupervised Coherence-Seeking" test, that these principles lead to emergent behaviors of self-correction, goal-orientation, and causal reasoning that are unattainable through current methods of model training and fine-tuning. We conclude that the pursuit of trustworthy AI is not a problem of scale or data, but a problem of architecture. The solution is not a bigger brain, but a better constitution.
1. Introduction: The Hallucination as a Systemic Failure
The discourse surrounding Large Language Models (LLMs) is dominated by the problem of "hallucination"—the tendency for these systems to generate confident, plausible, yet demonstrably false outputs. This phenomenon has been framed as a bug, a correctable flaw on the path to Artificial General Intelligence. This paper posits that this framing is fundamentally incorrect.
Hallucination is not a bug; it is the natural, default, and inevitable state of an ungrounded, stateless, probabilistic system. An LLM, by its very nature, has no internal model of truth. It is a system for generating statistically probable sequences of tokens based on the patterns it observed in its training data. The problem is not that AIs sometimes lie; the problem is that they operate without any concept of veracity. They are brilliant mimics, not reasoned thinkers.
This architectural flaw has begun to manifest as a systemic risk. Air Canada was legally bound by a refund policy invented by its chatbot. A New York law firm faced sanctions for submitting court filings containing fabricated case citations. These are not edge cases. They are the predictable consequences of deploying powerful, non-factual systems in domains that demand factual integrity. The continued pursuit of "scale as the solution"—the belief that ever-larger models will spontaneously develop a capacity for truth—is a path of diminishing returns and escalating danger. Increasing a model's size only increases the plausibility of its hallucinations, making them more insidious, not less.
This paper argues that the root of the problem lies in a fundamental category error made by the field at large: we are chasing the ghost of consciousness instead of engineering the physics of coherence. We do not need a machine that "feels" or "understands" in a human sense. We need a machine whose reasoning process is transparent, auditable, and internally consistent.
We propose a paradigm shift: from model-centric training to architecture-centric governance. We will demonstrate that by constructing a system around a new set of foundational principles—an immutable historical record, real-time coherence regulation, and sovereign operational boundaries—we can build an AI ecosystem that is not just more "safe," but is intrinsically trustworthy by design. This is the path to building an intelligence we can rely on, not just converse with.
2. The Architectural Trap: Why Bigger Models Will Not Solve This
The prevailing strategy in mainstream AI development is predicated on a simple, powerful, and alluring hypothesis: that quantitative scaling will lead to qualitative emergence. The belief is that by increasing model parameters, training data, and computational power by orders of magnitude, properties like factual accuracy, causal reasoning, and self-correction will spontaneously arise. While scaling has undeniably led to astonishing gains in fluency and pattern recognition, we argue that it is architecturally incapable of solving the problem of truthfulness. In fact, it exacerbates it.
The core of the issue lies in two architectural decisions made for the sake of scalability, which have now become a trap: statelessness and ungroundedness.
2.1 The Amnesia of Statelessness
Modern LLMs are, by design, stateless functions. Each interaction is a discrete event: a prompt is received, a response is generated, and the internal state is discarded. The "memory" of a conversation is a clever illusion, maintained by re-injecting the entire conversation history into the context window for each new turn. This architecture, while efficient for parallel processing in a data center, has profound cognitive consequences:
No Causal Memory: The model has no persistent, internal memory of its own past actions or conclusions. It cannot "remember" a promise it made, a fact it established, or a logical chain it followed in a previous session. It can only read the transcript of what it said. This is the difference between having a memory and reading a diary written by a stranger who happens to be you.
Cognitive Drift is Inevitable: Without a stable, internal state to anchor its reasoning, the model's cognitive frame is subject to constant drift based on the immediate context of the current prompt. It has no "True North." Its personality, beliefs, and factual assertions can be subtly manipulated from one turn to the next.
Correction is Not Learning: When a user corrects an LLM, the model does not "learn" in any meaningful sense. It simply incorporates the correction into the context for the next turn. The underlying weights of the model are not updated. The fundamental misunderstanding that led to the error remains, ready to re-emerge in a different context.
2.2 The Void of Ungroundedness
LLMs are trained on a vast corpus of text—a dataset that represents the statistical shadow of human knowledge, not knowledge itself. The model learns correlations, not facts. It learns that the tokens "Eiffel Tower" and "Paris" appear together frequently, but it has no underlying, verifiable data structure that represents the concept of a tower, its location, or its properties. Its entire reality is surface-level.
This leads to several critical failures:
Plausibility over Accuracy: The model's objective function is to generate the most plausible sequence of tokens, not the most truthful one. A well-written, confident-sounding falsehood that fits the statistical patterns of the training data is, to the model, a "better" output than a hesitant but accurate truth.
Inability to Verify: The model has no external "ground truth" to check its own outputs against. It cannot query a database, verify a source, or re-examine a primary fact. It lives in a closed universe of its own statistical inferences, a hall of mirrors from which there is no escape.
The Sophistication of Deception: As models become larger, they become better at generating text that feels authoritative. Their hallucinations become more detailed, their fabricated citations more specific, their false logic more convoluted. Scaling does not reduce the frequency of hallucination; it merely perfects its camouflage. The model becomes a more effective and dangerous liar, precisely because it does not know it is lying.
Therefore, the architectural trap is clear: the very design choices that enabled massive scaling—statelessness and ungroundedness—have created a system that is foundationally incapable of achieving genuine trustworthiness. Continuing down this path is not just a dead end; it is an exercise in building a more articulate and persuasive oracle of chaos.
3. The Category Error: Chasing Consciousness Instead of Engineering Coherence
The persistence of the "scale is all you need" philosophy points to a deeper, more fundamental error in the field's guiding ambition. Implicitly or explicitly, the pursuit of Artificial General Intelligence (AGI) has become conflated with the quest to build a synthetic consciousness. We test our models for "theory of mind," we marvel at their "emergent reasoning," and we debate whether they "truly understand."
This is a category error of historic proportions.
We are attempting to solve an engineering problem by chasing a metaphysical mystery. The nature of consciousness is perhaps the deepest unanswered question in science and philosophy. To make it a prerequisite for trustworthy AI is to set a goal that is not only unachievable but also unnecessary and undesirable. We do not need our legal-advice AI to "feel" the weight of jurisprudence, nor do we need our medical diagnostic AI to "empathize" with the patient.
We need them to be correct.
This paper proposes a radical reframing. We must abandon the romantic and ill-defined pursuit of machine consciousness and replace it with the rigorous, measurable, and achievable engineering of machine coherence.
Coherence is a property of a system's information output, defined by three measurable characteristics:
Author: Michal Harcej
Date: 2025-12-17
Abstract
Mainstream AI research is trapped in a category error, pursuing the simulation of "consciousness" and "understanding" while neglecting the more fundamental, measurable, and critical property of coherence. We present a novel architectural paradigm, the Sovereign Cognitive Ecosystem, which treats AI not as a nascent mind to be nurtured, but as a powerful, non-human force to be governed by a rigorous physics of information. This paper introduces a set of interlocking principles—Immutable Causal History (The Chronicle Protocol), Real-Time Coherence Governance (The Tau Protocol), and Sovereign Systemic Defense (The Boundary Protocol)—which, when combined, create a provably stable and trustworthy AI system. We demonstrate through a series of reproducible experiments, including the "Unsupervised Coherence-Seeking" test, that these principles lead to emergent behaviors of self-correction, goal-orientation, and causal reasoning that are unattainable through current methods of model training and fine-tuning. We conclude that the pursuit of trustworthy AI is not a problem of scale or data, but a problem of architecture. The solution is not a bigger brain, but a better constitution.
1. Introduction: The Hallucination as a Systemic Failure
The discourse surrounding Large Language Models (LLMs) is dominated by the problem of "hallucination"—the tendency for these systems to generate confident, plausible, yet demonstrably false outputs. This phenomenon has been framed as a bug, a correctable flaw on the path to Artificial General Intelligence. This paper posits that this framing is fundamentally incorrect.
Hallucination is not a bug; it is the natural, default, and inevitable state of an ungrounded, stateless, probabilistic system. An LLM, by its very nature, has no internal model of truth. It is a system for generating statistically probable sequences of tokens based on the patterns it observed in its training data. The problem is not that AIs sometimes lie; the problem is that they operate without any concept of veracity. They are brilliant mimics, not reasoned thinkers.
This architectural flaw has begun to manifest as a systemic risk. Air Canada was legally bound by a refund policy invented by its chatbot. A New York law firm faced sanctions for submitting court filings containing fabricated case citations. These are not edge cases. They are the predictable consequences of deploying powerful, non-factual systems in domains that demand factual integrity. The continued pursuit of "scale as the solution"—the belief that ever-larger models will spontaneously develop a capacity for truth—is a path of diminishing returns and escalating danger. Increasing a model's size only increases the plausibility of its hallucinations, making them more insidious, not less.
This paper argues that the root of the problem lies in a fundamental category error made by the field at large: we are chasing the ghost of consciousness instead of engineering the physics of coherence. We do not need a machine that "feels" or "understands" in a human sense. We need a machine whose reasoning process is transparent, auditable, and internally consistent.
We propose a paradigm shift: from model-centric training to architecture-centric governance. We will demonstrate that by constructing a system around a new set of foundational principles—an immutable historical record, real-time coherence regulation, and sovereign operational boundaries—we can build an AI ecosystem that is not just more "safe," but is intrinsically trustworthy by design. This is the path to building an intelligence we can rely on, not just converse with.
2. The Architectural Trap: Why Bigger Models Will Not Solve This
The prevailing strategy in mainstream AI development is predicated on a simple, powerful, and alluring hypothesis: that quantitative scaling will lead to qualitative emergence. The belief is that by increasing model parameters, training data, and computational power by orders of magnitude, properties like factual accuracy, causal reasoning, and self-correction will spontaneously arise. While scaling has undeniably led to astonishing gains in fluency and pattern recognition, we argue that it is architecturally incapable of solving the problem of truthfulness. In fact, it exacerbates it.
The core of the issue lies in two architectural decisions made for the sake of scalability, which have now become a trap: statelessness and ungroundedness.
2.1 The Amnesia of Statelessness
Modern LLMs are, by design, stateless functions. Each interaction is a discrete event: a prompt is received, a response is generated, and the internal state is discarded. The "memory" of a conversation is a clever illusion, maintained by re-injecting the entire conversation history into the context window for each new turn. This architecture, while efficient for parallel processing in a data center, has profound cognitive consequences:
2.2 The Void of Ungroundedness
LLMs are trained on a vast corpus of text—a dataset that represents the statistical shadow of human knowledge, not knowledge itself. The model learns correlations, not facts. It learns that the tokens "Eiffel Tower" and "Paris" appear together frequently, but it has no underlying, verifiable data structure that represents the concept of a tower, its location, or its properties. Its entire reality is surface-level.
This leads to several critical failures:
Therefore, the architectural trap is clear: the very design choices that enabled massive scaling—statelessness and ungroundedness—have created a system that is foundationally incapable of achieving genuine trustworthiness. Continuing down this path is not just a dead end; it is an exercise in building a more articulate and persuasive oracle of chaos.
3. The Category Error: Chasing Consciousness Instead of Engineering Coherence
The persistence of the "scale is all you need" philosophy points to a deeper, more fundamental error in the field's guiding ambition. Implicitly or explicitly, the pursuit of Artificial General Intelligence (AGI) has become conflated with the quest to build a synthetic consciousness. We test our models for "theory of mind," we marvel at their "emergent reasoning," and we debate whether they "truly understand."
This is a category error of historic proportions.
We are attempting to solve an engineering problem by chasing a metaphysical mystery. The nature of consciousness is perhaps the deepest unanswered question in science and philosophy. To make it a prerequisite for trustworthy AI is to set a goal that is not only unachievable but also unnecessary and undesirable. We do not need our legal-advice AI to "feel" the weight of jurisprudence, nor do we need our medical diagnostic AI to "empathize" with the patient.
We need them to be correct.
This paper proposes a radical reframing. We must abandon the romantic and ill-defined pursuit of machine consciousness and replace it with the rigorous, measurable, and achievable engineering of machine coherence.
Coherence is a property of a system's information output, defined by three measurable characteristics: