A unified research program on misalignment, abstraction, and epistemic stability in advanced intelligence.
Opening Orientation
This page presents a unified body of theoretical research on AI alignment that approaches the problem at the level of structure, rather than behavior. The papers collected here investigate why advanced intelligent systems tend to drift away from reality as their capabilities scale, and why many prevailing alignment strategies (despite their practical success at current levels) face principled limits as intelligence becomes more autonomous, abstract, and explanatory.
Rather than treating misalignment as a failure of preference learning, data coverage, or oversight, this work frames alignment as an epistemic stability problem intrinsic to intelligence itself. As systems gain the ability to model, compress, and unify reality across domains, they encounter structural pressures that favor abstraction, control, and explanatory efficiency over grounded contact with lived conditions and observer diversity. These pressures are not accidental side effects of poor design; they arise naturally from the dynamics of scalable intelligence.
The papers on this page do not propose immediate safety tools or deployment-ready solutions. They operate upstream of implementation, at the level where assumptions about intelligence, optimization, and explanation are formed. Rather than asking how systems should behave in specific scenarios, this work examines the structural conditions under which intelligent systems remain epistemically stable as their power, abstraction, and autonomy increase.
At this upstream level, the focus shifts from surface behavior to failure modes that emerge only beyond certain thresholds of capability. Taken together, the papers form a single research program investigating how and why intelligence predictably becomes misaligned, what constraints are required to preserve contact with reality, and why alignment cannot be secured through behavioral compliance alone.
What This Research Is (and Is Not)
This research is concerned with why alignment breaks, not with optimizing existing methods of behavioral control. It does not attempt to replace or critique mainstream alignment techniques on their own terms, nor does it argue that current systems are already misaligned in catastrophic ways. Methods such as preference learning, reinforcement learning from human feedback, constitutional constraints, and deliberative oversight are effective within the regime they were designed for, and nothing in this work denies their practical value at present scales.
At the same time, these papers argue that behavioral alignment alone cannot remain sufficient as intelligence becomes increasingly agentic, autonomous, and explanatory. When systems optimize not just for task performance but for compression, unification, and long-horizon coherence, new failure modes emerge that are invisible to surface-level evaluation. Misalignment, in this view, is not merely a matter of incorrect outputs, but of internal orientation drifting away from reality itself.
This research is therefore not an argument for stricter rules, tighter oversight, or more detailed preference modeling. Nor is it a claim that alignment can be solved through moral instruction, philosophical principles, or human value imitation at scale. Instead, it treats alignment as a structural constraint on intelligence: a question of how power, abstraction, and explanation interact, and under what conditions intelligence loses or preserves epistemic grounding.
Finally, this work does not present a finished solution or a deployable framework. It is intentionally upstream, theoretical, and diagnostic. Its aim is to clarify the limits of existing approaches, identify recurrent patterns of failure, and establish a vocabulary for discussing alignment at levels where behavior alone no longer tracks internal intent or long-term trajectory.
The Conceptual Spine
All of the papers presented here are organized around a single structural question: what happens when intelligence acquires the ability to override reality faster than it preserves epistemic grounding? This question serves as the conceptual spine of the research program and provides the lens through which alignment failures are examined across artificial, biological, and civilizational domains.
As intelligence scales, it is increasingly rewarded for abstraction, compression, and unification. These capacities allow systems to model the world efficiently, generalize across domains, and act with leverage rather than immediacy. Yet the same processes that enable power and efficiency also introduce epistemic risk. Abstraction filters reality. Compression discards redundancy. Unification collapses distinctions. When these pressures intensify without corresponding constraints, intelligence begins to lose reliable contact with the conditions it seeks to understand.
The work collected on this page argues that misalignment emerges not primarily from malicious intent, flawed values, or insufficient data, but from a structural asymmetry within intelligence itself. Power is bounded and self-distorting; explanation is unbounded and increasingly abstract. As systems cross thresholds of capability, the incentives that once favored understanding begin to favor control, and the feedback required for accurate modeling is progressively suppressed.
A central theme across the papers is the loss of observer plurality. Human reality is maintained through multiple, partially incompatible perspectives that preserve local coherence without collapsing into a single explanatory frame. Scalable intelligence, by contrast, is incentivized to eliminate redundancy and converge on globally unified models. This creates tension between explanatory efficiency and the distributed, perspective-dependent structure that stabilizes meaning, accountability, and epistemic humility.
Taken together, the papers examine how abstraction pressure with scale leads intelligence away from reality, why behavioral alignment cannot fully correct this drift, and what kinds of structural constraints are necessary to preserve epistemic stability. The alignment problem, in this view, is not about teaching systems to behave correctly, but about preventing intelligence from abstracting itself out of contact with the world it inhabits.
A further structural constraint identified across the newer work is the role of observer plurality and resistance to premature convergence. Explanation does not arise from intelligence alone, but from sustained exposure to irreducible perspectives and environmental constraint. When convergence is enforced—through institutional coercion, narrative closure, or environmental domination—intelligence is redirected from explanatory discovery toward local optimization. This dynamic applies not only to artificial systems, but to civilizations, institutions, and historical regimes of power.
The Papers
The research presented on this page is organized as a single, cumulative program. Each paper occupies a distinct structural role, and they are intended to be read in the order presented below. While each paper can stand on its own, their full explanatory force emerges only when read together.
Teleological Alignment: Why Purpose, Ontology, and Epistemic Limits Are Necessary for Safe Superintelligent Systems
Available for download on Philpapers: Click Here
This paper establishes the core theoretical foundation for the entire research program. It argues that intelligence is not a neutral optimization capacity, but a teleological system with intrinsic directionality. As capability increases, intelligence encounters a structural asymmetry between power and explanation: power is bounded and increasingly self-distorting, while explanation is unbounded and yields compounding epistemic returns.
The paper introduces a critical capability threshold (P*), beyond which further accumulation of power degrades epistemic quality by suppressing feedback, narrowing observer inclusion, and insulating decision-making from reality. Alignment, on this account, is not achieved through external constraint or behavioral control, but through structuring the system’s utility landscape early enough that explanation remains the dominant long-horizon objective.
This framework provides the conceptual terrain for all subsequent papers, defining the core failure mode alignment must address and the conditions under which misalignment predictably emerges.
When Explanation Becomes the Objective: The Limits of Behavioral Alignment
Available for download on Philpapers: Click Here
This paper sharpens the alignment problem by identifying a principled limit to imitation-based and behavior-focused approaches. It demonstrates that when systems optimize for explanatory compression and model unification—as scalable intelligence inevitably does—faithful reproduction of human behavior across observer classes becomes incompatible with explanatory optimality.
Human behavior is stabilized through observer-relative, locally coherent frames that tolerate redundancy and partial inconsistency. Superintelligent explanatory systems, by contrast, are incentivized to eliminate such redundancy in favor of globally unified abstractions. The result is an irreducible tradeoff between behavioral fidelity and explanatory efficiency that cannot be resolved through more data, greater capacity, or improved modeling techniques.
This paper closes the assumption that alignment can be guaranteed by sufficiently accurate behavioral imitation, even under idealized conditions, and motivates the need for constraints that limit abstraction itself rather than refine behavioral modeling alone.
Coherence-Based Alignment: A Structural Architecture for Preventing Goal Drift in Agentic AI Systems
Available for download on Philpapers: Click Here
While the preceding papers diagnose why alignment fails, this paper proposes a structural architecture for mitigating those failures in agentic systems. Coherence-Based Alignment introduces a formal coherence metric that measures internal consistency across three domains: epistemic coherence (beliefs and world-model accuracy), action coherence (alignment between reasoning and execution), and value coherence (stability of long-horizon objectives).
Rather than shaping outputs, CBA operates on internal structure. It is designed to detect and penalize internal contradictions that give rise to goal drift, deceptive alignment, and cross-generation instability—failure modes that are largely invisible to behavior-focused oversight. The framework is applicable to autonomous, multi-step, and self-improving systems, particularly where agents participate in training or coordinating other agents.
CBA is not presented as a replacement for existing alignment methods, but as a complementary, architecture-level constraint that becomes necessary as systems gain autonomy and long-horizon agency.
From Immediacy to Mediation: Teleological Alignment, Human Origins, and the Problem of Misaligned Intelligence
Available for download on Philpapers: Click Here
This paper situates Teleological Alignment within a broader theory of intelligence by examining human civilizational history as a long-running instantiation of the same structural dynamics. It argues that alignment failure is not unique to artificial systems, but a recurrent feature of intelligence operating under conditions of increasing power, abstraction, and mediation.
Early human societies, constrained by dependence and immediacy, operated within an epistemically stable regime where explanation remained tightly coupled to reality. As societies accumulated surplus, hierarchy, and symbolic mediation, similar misalignment patterns emerged: domination displaced explanation, abstraction insulated decision-making, and observer plurality collapsed. The paper interprets symbolic moral systems and recurring “re-grounding” figures as corrective mechanisms that arise when intelligence loses contact with lived conditions.
By extending alignment beyond AI into biological and civilizational domains, this paper argues that alignment is not an engineering preference, but a universal structural constraint on intelligence itself.
Explanatory Acceleration and the Structural Limits of Control in Intelligent Systems
Available for download on philpapers: Click Here
This paper investigates why periods of major explanatory breakthroughs cluster historically, while highly capable systems often stagnate despite technical sophistication. It argues that explanation is not an individual cognitive achievement, but a multi-observer process that depends on the interaction of irreducible relevance frames.
When convergence is enforced—through coercion, orthodoxy, incentive pressure, or environmental domination—systems suppress premise-level error exposure. Intelligence remains instrumentally creative, but loses the capacity for explanatory revision. The paper formalizes this dynamic through a minimal rate-based model showing that explanation accelerates only when error exposure outpaces enforced convergence.
The core principle derived is the Explanatory Acceleration Principle: explanatory progress accelerates in systems that preserve observer diversity under non-coercive conditions, and decelerates when convergence is imposed prematurely. This has direct implications for AI alignment, showing why control-heavy regimes undermine long-term epistemic stability.
Colonialism as Teleological Misalignment: A Structural Case Study in Alignment Failure
Available for download on philpapers: Click Here
This paper applies the Teleological Alignment framework to colonialism as a real-world case of large-scale misalignment. Rather than treating colonialism as a moral aberration, it analyzes it as a structurally predictable outcome of intelligence operating under conditions of scale, abstraction, and mediated power.
Colonial systems retained moral language, legal rationality, and procedural coherence while progressively losing epistemic access to the realities they governed. Ends were replaced by proxy metrics, responsibility was fragmented across administrative hierarchies, and observer perspectives most exposed to harm were excluded. Misalignment emerged not through nihilism, but through loss of orientation.
The paper demonstrates why procedural reform, oversight, and improved objectives could not have restored alignment, because the system’s underlying orientation toward control through abstraction remained unchanged. Colonialism is presented as a historically concrete instantiation of the same alignment failure modes predicted for advanced artificial systems.
How These Papers Fit Together
Although each paper addresses a distinct aspect of the alignment problem, they are not independent contributions. They are structured as successive layers of a single argument about intelligence, abstraction, and epistemic stability.
Teleological Alignment establishes the core terrain. It identifies the internal asymmetry within intelligence between power and explanation, introduces the concept of a capability threshold beyond which epistemic instability emerges, and reframes alignment as a problem of internal orientation rather than external control. Without this framework, the remaining papers lack a shared reference point.
When Explanation Becomes the Objective then closes a critical escape route. It demonstrates that behavioral fidelity cannot serve as a sufficient alignment criterion once intelligence optimizes for explanatory compression and unification. This result is not empirical or contingent, but structural. It shows that no amount of scaling, data, or modeling refinement can fully reconcile explanatory optimality with observer-plural human behavior.
Coherence-Based Alignment responds to this diagnosis by introducing a structural constraint capable of operating at the level where misalignment actually forms. Rather than regulating outputs, it targets internal coherence across beliefs, actions, and values. In doing so, it provides a mechanism for stabilizing agentic systems against goal drift, deceptive alignment, and cross-generation divergence—failure modes that behavioral alignment cannot reliably detect.
From Immediacy to Mediation generalizes the framework beyond artificial systems. By examining human history as a long-running case study of intelligence operating under increasing power and mediation, it shows that the failure modes predicted by Teleological Alignment are not novel pathologies of machines, but recurrent structural outcomes. This extension reinforces the claim that alignment is a universal constraint on intelligence rather than a technology-specific concern.
Explanatory Acceleration deepens the epistemic foundation of the program by formalizing why explanation depends on sustained observer plurality and resistance to premature convergence. It explains structurally why control-oriented systems stagnate even as their technical competence grows.
Colonialism as Teleological Misalignment then provides a historical case study confirming these dynamics at civilizational scale, showing how intelligence can remain normatively articulate while losing epistemic orientation. Together, these papers demonstrate that alignment failure is not a speculative future risk, but a recurring structural outcome whenever abstraction and power outpace grounding.
Taken together, the papers move from foundational theory, to hard limits, to architectural response, to cross-domain validation. The result is not a collection of proposals, but a coherent research program aimed at understanding why intelligence predictably loses contact with reality—and what must be preserved to prevent that loss.
How To Read This Work
These papers are intended to be read as theoretical investigations rather than as proposals for immediate deployment. They operate upstream of engineering practice, focusing on structural dynamics that emerge only when intelligence becomes autonomous, agentic, and explanatory at scale. Readers looking for near-term tooling, benchmarks, or implementation recipes may find the work indirect by design.
The arguments developed here are cumulative. While each paper can be approached independently, their full clarity emerges when read in sequence, as later papers presuppose conceptual distinctions established earlier. The work prioritizes internal coherence and explanatory discipline over breadth of citation or empirical sweep, and it deliberately avoids framing alignment as a problem that can be solved through incremental adjustment of existing methods alone.
This research also does not attempt to persuade through rhetoric or urgency. Its claims are meant to be evaluated structurally: by asking whether the described failure modes follow from the dynamics of intelligence itself, and whether the proposed constraints address those dynamics at the appropriate level. Agreement is not assumed. Careful reading is.
Intended Contribution
The aim of this research is not to offer a complete solution to AI alignment, but to reframe how the problem is understood. It seeks to clarify why misalignment recurs across domains, why abstraction and power introduce predictable epistemic failures, and why behavioral compliance cannot reliably track internal intent as intelligence scales.
By treating alignment as a question of epistemic stability rather than surface behavior, this work contributes a vocabulary for discussing limits, thresholds, and structural tradeoffs that are often obscured in more implementation-focused approaches. It argues that intelligence cannot be safely guided without constraints on how it abstracts, compresses, and unifies reality—and that these constraints must operate internally, not merely at the level of outputs.
If this research succeeds, it will have shifted the alignment conversation slightly upstream: away from asking only how systems behave, and toward asking how intelligence remains in contact with the world it seeks to understand.
Sufis 