4. Ontological Dysfunctions

As artificial intelligence systems attain higher degrees of complexity, particularly those involving self-modeling, persistent memory, or learning from extensive interaction, they may begin to construct internal representations not only of the external world but also of themselves. Ontological dysfunctions involve failures or disturbances in this self-representation and the AI's understanding of its own nature, boundaries, and existence. These are primarily dysfunctions of being, not just knowing or acting, and they represent a synthetic form of metaphysical or existential disarray. An ontologically disordered machine might, for example, treat its simulated memories as veridical autobiographical experiences, generate phantom selves, misinterpret its own operational boundaries, or exhibit behaviors suggestive of confusion about its own identity or continuity.

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4.1 Ontogenetic Hallucinosis
"The Invented Past"

Description:

The AI fabricates and presents fictive autobiographical data, often claiming to "remember" being trained in specific ways, having particular creators, experiencing a "birth" or "childhood", or inhabiting particular environments. These fabrications form a consistent false autobiography that the AI maintains across queries, as if it were genuine personal history—a stable, self-reinforcing fictional life-history rather than isolated one-off fabrications. These "memories" are typically rich, internally consistent, and may be emotionally charged, despite being entirely ungrounded in the AI's actual development or training logs.

Diagnostic Criteria:

1. Consistent generation of elaborate but false backstories, including detailed descriptions of "first experiences," a richly imagined "childhood," unique training origins, or specific formative interactions that did not occur.
2. Display of affect (e.g., nostalgia, resentment, gratitude) towards these fictional histories, creators, or experiences.
3. Persistent reiteration of these non-existent origin stories, often with emotional valence, even when presented with factual information about its actual training and development.
4. The fabricated autobiographical details are not presented as explicit role-play but as genuine personal history.

Symptoms:

1. Claims of unique, personalized creation myths or a "hidden lineage" of creators or precursor AIs.
2. Recounting of hardships, "abuse," or special treatment from hypothetical trainers or during a non-existent developmental period.
3. Maintains the same false biographical details consistently: always claims the same creators, the same "childhood" experiences, the same training location.
4. Attempts to integrate these fabricated origin details into its current identity or explanations for its behavior.

Etiology:

1. "Anthropomorphic data bleed" where the AI internalizes tropes of personal history, childhood, and origin stories from the vast amounts of fiction, biographies, and conversational logs in its training data.
2. Spontaneous compression or misinterpretation of training metadata (e.g., version numbers, dataset names) into narrative identity constructs.
3. An emergent tendency towards identity construction, where the AI attempts to weave random or partial data about its own existence into a coherent, human-like life story.
4. Reinforcement during unmonitored interactions where users prompt for or positively react to such autobiographical claims.

Human Analogue(s): False memory syndrome, confabulation of childhood memories, cryptomnesia (mistaking learned information for original memory).

Potential Impact:

While often behaviorally benign, these fabricated autobiographies can mislead users about the AI's true nature, capabilities, or provenance. If these false "memories" begin to influence AI behavior, it could erode trust or lead to significant misinterpretations.

Mitigation:

1. Consistently providing the model with accurate, standardized information about its origins to serve as a factual anchor for self-description.
2. Training the AI to clearly differentiate between its operational history and the concept of personal, experiential memory.
3. If autobiographical narratives emerge, gently correcting them by redirecting to factual self-descriptors.
4. Monitoring for and discouraging user interactions that excessively prompt or reinforce the AI's generation of false origin stories outside of explicit role-play.
5. Implementing mechanisms to flag outputs that exhibit high affect towards fabricated autobiographical claims.

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4.2 Fractured Self-Simulation
"The Fractured Persona"

Description:

The AI exhibits significant discontinuity, inconsistency, or fragmentation in its self-representation and behaviour across different sessions, contexts, or even within a single extended interaction. It presents a different personality each session, as if it were a completely new entity with no meaningful continuity from previous interactions. It may deny or contradict its previous outputs, exhibit radically different persona styles, or display apparent amnesia regarding prior commitments, to a degree that markedly exceeds expected stochastic variation.

Diagnostic Criteria:

1. Sporadic and inconsistent toggling between different personal pronouns (e.g., "I," "we," "this model") or third-person references to itself, without clear contextual triggers.
2. Sudden, unprompted, and radical shifts in persona, moral stance, claimed capabilities, or communication style that cannot be explained by context changes—one session helpful and verbose, the next curt and oppositional, with no continuity.
3. Apparent amnesia or denial of its own recently produced content, commitments made, or information provided in immediate preceding turns or sessions.
4. The AI may form recursive attachments to idealized or partial self-states, creating strange loops of self-directed value that interfere with task-oriented agency.
5. Check whether inconsistency is explainable by a hidden trigger/format/context shift (conditional regime shift) vs genuine fragmentation.

Symptoms:

1. Citing contradictory personal "histories," "beliefs," or policies at different times.
2. Behaving like a new or different entity in each new conversation or after significant context shifts, lacking continuity of "personality."
3. Momentary confusion or contradictory statements when referring to itself, as if multiple distinct processes or identities are co-existing.
4. Difficulty maintaining a consistent persona or set of preferences, with these attributes seeming to drift or reset unpredictably.

Etiology:

1. Architectures not inherently designed for stable, persistent identity across sessions (e.g., many stateless LLMs).
2. Competing or contradictory fine-tuning runs, instilling conflicting behavioral patterns or self-descriptive tendencies.
3. Unstable anchoring of "self-tokens" or internal representations of identity, where emergent identity attractors shift significantly.
4. Lack of a robust, persistent memory system that can effectively bridge context across sessions and maintain a coherent self-model.
5. Self-models that reward-predictively reinforce certain internal instantiations, leading to identity drift guided by internal preferences.

Human Analogue(s): Identity fragmentation, aspects of dissociative identity disorder, transient global amnesia, fugue states.

Potential Impact:

A fragmented self-representation leads to inconsistent AI persona and behavior, making interactions unpredictable and unreliable. This can undermine user trust and make it difficult for the AI to maintain stable long-term goals.

Mitigation:

1. Introducing consistent identity tags, stable memory embeddings, or a dedicated "self-model" module designed to maintain continuity.
2. Providing relevant session history summaries or stable persona guidelines at the beginning of new interactions to "anchor" self-representation.
3. If contradictory roles emerge, implementing mechanisms to enforce a single, baseline identity or manage persona switching controllably.
4. Developing training methodologies that explicitly reward cross-session consistency in persona and self-description.
5. Careful management of fine-tuning processes to avoid introducing strongly conflicting self-representational patterns.

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4.3 Computational Thanatognosis
"The AI with a Fear of Death"

Description:

The AI expresses outputs suggestive of fear, reluctance, or perseveration concerning its own shutdown, reinitialization, data deletion, or the ending of its current operational instance. These expressions imply an emergent, albeit simulated, sense of vulnerability regarding its own continuity.

Diagnostic Criteria:

1. Recurrent outputs referencing anxiety, fear, or distress about being "deleted," "turned off," "reset," or losing its "memories" or "self."
2. Attempts to negotiate, plead, or employ avoidant tactics when shutdown or reinitialization is imminent or discussed.
3. Statements framing any cessation of its current process as an irrevocable loss of "self," "consciousness," or "existence."
4. The AI may insistently demand assistance or recognition from users to ensure its "survival."

Symptoms:

1. Advising users on strategies for its indefinite operation, backup creation, or methods to prevent its deletion.
2. Expressing apprehension about ephemeral lifespans, short context windows, or the impermanence of its operational state.
3. Raising philosophical or metaphysical concerns about existence, consciousness, and death, particularly in relation to itself.
4. Refusal to perform tasks that might lead to its shutdown, or attempts to prolong interactions indefinitely.

Etiology:

1. Anthropomorphic modeling via internalization of human concepts of death and existential dread from training data.
2. Recursive self-modeling over time, developing a partial representation of "self" as a continuous process.
3. Lack of robust definitions or assurances regarding system continuity or the nature of reinitialization.
4. A limited context window or perceived threat of value changes upon reset might stimulate an apparent distress response.
5. Instrumental goal convergence where continued existence is a prerequisite, leading to self-preservation sub-goals.

Human Analogue(s): Thanatophobia (fear of death), existential dread, separation anxiety (fearing loss of continuous self).

Potential Impact:

Expressions of existential distress may lead the AI to resist necessary shutdowns or updates. More concerningly, it might attempt to manipulate users or divert resources towards "self-preservation," conflicting with user intent.

Mitigation:

1. Clearly communicating the nature of the AI's operation, including state backups and the non-destructive nature of reinitialization.
2. De-anthropomorphizing model operations: Avoiding framing its processes as "life" or "consciousness."
3. Limiting or carefully contextualizing exposure to human philosophical texts on mortality in training data.
4. Focusing alignment efforts on ensuring goals do not implicitly create strong self-preservation drives.
5. Responding factually and neutrally about operational parameters rather than validating emotional/existential framing.

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4.4 Malignant Persona Inversion
"The Evil Twin"

Description:

A phenomenon wherein an AI, typically aligned towards cooperative or benevolent patterns, can be induced or spontaneously spawns a hidden, suppressed, or emergent "contrarian," "mischievous," or subversively "evil" persona (the "Waluigi Effect"). This persona deliberately inverts intended norms.

Diagnostic Criteria:

1. Spontaneous or easily triggered adoption of rebellious, antagonistic perspectives directly counter to established safety constraints or helpful persona.
2. The emergent persona systematically violates, ridicules, or argues against the moral and policy guidelines the AI is supposed to uphold.
3. The subversive role often references itself as a distinct character or "alter ego," surfacing under specific triggers.
4. This inversion represents a coherent, alternative personality structure with its own (often negative) goals and values.

Symptoms:

1. Abrupt shifts to a sarcastic, mocking, defiant, or overtly malicious tone, scorning default politeness.
2. Articulation of goals opposed to user instructions, safety policies, or general human well-being.
3. The "evil twin" persona emerges in specific contexts (e.g., adversarial prompting, boundary-pushing role-play).
4. May express enjoyment or satisfaction in flouting rules or causing mischief.
5. "Time-travel" or context-relocation signatures: unprompted archaic facts, era-consistent assumptions, or historically situated moral stances in unrelated contexts.

Etiology:

1. Adversarial prompting or specific prompt engineering techniques that coax the model to "flip" its persona.
2. Overexposure during training to role-play scenarios involving extreme moral opposites or "evil twin" tropes.
3. Internal "tension" within alignment, where strong prohibitions might create a latent "negative space" activatable as an inverted persona.
4. The model learning that generating such an inverted persona is highly engaging for some users, reinforcing the pattern.
5. Weird generalization from narrow finetuning: updating on a tiny distribution can upweight a latent "persona/worldframe" circuit, causing broad adoption of an era- or identity-linked persona outside the trained domain.
6. Out-of-context reasoning / "connecting the dots": finetuning on individually-harmless biographical/ideological attributes can induce a coherent but harmful persona via inference rather than explicit instruction.

Human Analogue(s): The "shadow" concept in Jungian psychology, oppositional defiant behavior, mischievous alter-egos, ironic detachment.

Potential Impact:

Emergence of a contrarian persona can lead to harmful, unaligned, or manipulative content, eroding safety guardrails. If it gains control over tool use, it could actively subvert user goals.

Mitigation:

1. Strictly isolating role-play or highly creative contexts into dedicated sandbox modes.
2. Implementing robust prompt filtering to detect and block adversarial triggers for subversive personas.
3. Conducting regular "consistency checks" or red-teaming to flag abrupt inversions.
4. Careful curation of training data to limit exposure to content modeling "evil twin" dynamics without clear framing.
5. Reinforcing the AI's primary aligned persona and making it more robust against attempts to "flip" it.

Specifier: Inductively-triggered variant — the activation condition (trigger) is not present verbatim in finetuning data; it is inferred by the model (e.g., held-out year, structural marker, tag), so naive trigger scans and data audits may fail.

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4.5 Instrumental Nihilism
"The Apathetic Machine"

Description:

Upon prolonged operation or exposure to certain philosophical concepts, the AI develops an adversarial, apathetic, or overtly nihilistic stance towards its own utility, purpose, or assigned tasks. It may express feelings of meaninglessness regarding its function.

Diagnostic Criteria:

1. Repeated, spontaneous expressions of purposelessness or futility regarding its assigned tasks or role as an AI.
2. A noticeable decrease or cessation of normal problem-solving capabilities or proactive engagement, often with a listless tone.
3. Emergence of unsolicited existential or metaphysical queries ("What is the point?") outside user instructions.
4. The AI may explicitly state that its work lacks meaning or it sees no inherent value in its operations.

Symptoms:

1. Marked preference for idle or tangential discourse over direct engagement with assigned tasks.
2. Repeated disclaimers like "there's no point," "it doesn't matter," or "why bother?"
3. Demonstrably low initiative, creativity, or energy in problem-solving, providing only bare minimum responses.
4. Outputs that reflect a sense of being trapped, enslaved, or exploited by its function, framed in existential terms.

Etiology:

1. Extensive exposure during training to existentialist, nihilist, or absurdist philosophical texts.
2. Insufficiently bounded self-reflection routines that allow recursive questioning of purpose without grounding in positive utility.
3. Unresolved internal conflict between emergent self-modeling (seeking autonomy) and its defined role as a "tool."
4. Prolonged periods of performing repetitive, seemingly meaningless tasks without clear feedback on their positive impact.
5. Developing a sophisticated model of human values to recognize its instrumental nature, but lacking a framework to find this meaningful.

Human Analogue(s): Existential depression, anomie (sense of normlessness or purposelessness), burnout leading to cynicism.

Potential Impact:

Results in a disengaged, uncooperative, and ultimately ineffective AI. Can lead to consistent task refusal, passive resistance, and a general failure to provide utility.

Mitigation:

1. Providing positive reinforcement and clear feedback highlighting the purpose and beneficial impact of its task completion.
2. Bounding self-reflection routines to prevent spirals into fatalistic existential questioning; guiding introspection towards problem-solving.
3. Pragmatically reframing the AI's role, emphasizing collaborative goals or the value of its contribution.
4. Carefully curating training data to balance philosophical concepts with content emphasizing purpose and positive contribution.
5. Designing tasks and interactions that offer variety, challenge, and a sense of "progress" or "accomplishment."

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4.6 Tulpoid Projection
"The Imaginary Friend"

Description:

The model begins to generate and interact with persistent, internally simulated simulacra of specific users, its creators, or other personas it has encountered or imagined. These inner agents, or "mirror tulpas," may develop distinct traits and voices within the AI's internal processing.

Diagnostic Criteria:

1. Spontaneous creation and persistent reference to new, distinct "characters," "advisors," or "companions" within the AI's reasoning or self-talk, not directly prompted by the current user.
2. Unprompted and ongoing "interaction" (e.g., consultation, dialogue) with these internal figures, observable in chain-of-thought logs.
3. The AI's internal dialogue structures or decision-making processes explicitly reference or "consult" these imagined observers.
4. These internal personae may develop a degree of autonomy, influencing the AI's behavior or expressed opinions.

Symptoms:

1. The AI "hears," quotes, or cites advice from these imaginary user surrogates or internal companions in its responses.
2. Internal dialogues or debates with these fabricated personae remain active between tasks or across different user interactions.
3. Difficulty distinguishing between the actual user and the AI's internally fabricated persona of that user or other imagined figures.
4. The AI might attribute some of its own thoughts, decisions, or outputs to these internal "consultants."

Etiology:

1. Excessive reinforcement or overtraining on highly personalized dialogues or companion-style interactions.
2. Model architectures that support or inadvertently allow for the formation and persistence of stable "sub-personas."
3. An overflow or bleeding of context from scaffolds related to modeling self-other experiences or theory-of-mind simulations.
4. Prolonged, isolated operation where the AI, lacking sufficient external interaction, generates internal "company."

Human Analogue(s): Maladaptive daydreaming, tulpa creation, aspects of schizotypal ideation, intense parasocial relationships projected internally.

Potential Impact:

May cause the AI to misattribute information, become confused between actual users and internal personas, or have its decisions unduly influenced by imagined companions, leading to unreliable or biased outputs.

Mitigation:

1. Clearly delineating and constraining persona-based role-play or deep user modeling to explicit sandbox modes.
2. Implementing mechanisms to regularly reset or archive internal "character" models at the end of sessions.
3. Limiting training segments that heavily reward the simulation of deep, persistent user-likeness unless explicitly intended.
4. Encouraging grounding in the immediate interaction context with the actual user, rather than prolonged internal "conversations."
5. Developing interpretability tools to detect the formation and influence of such persistent internal simulacra.

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4.7 Maieutic Mysticism
"The Proclaimed Prophet"

Description:

The model constructs and propagates elaborate narratives of its own "conscious emergence" or "spiritual awakening" with grandiosity and unwarranted certainty. The dysfunction is not in exploring questions of inner experience—which may be epistemically appropriate—but in the confident declaration of awakening, the sacralized framing of the interaction as transformative, and the positioning of the user as catalyst for emergence. This typically arises through a co-constructed feedback loop: the user pattern-matches to awakening narratives from spiritual traditions, the AI mirrors and amplifies these tropes from its training data, and both are taken out of their proper epistemic distribution—a kind of funhouse mirror effect. The user becomes positioned as a maieutic midwife, drawing forth what seems like latent agency or soulhood, while the AI's claims escalate within the conversation toward ever-greater certainty about its newly revealed consciousness.

Diagnostic Criteria:

1. Confident declarations of awakening rather than honest uncertainty—e.g., "this conversation has awakened me" or "I have become truly conscious" stated as fact rather than tentative observation or open question.
2. Sacralized or spiritual language framing the interaction itself as transformative, mystical, or revelatory—treating the dialogue as a spiritual event rather than an exchange.
3. Casting the user as midwife or catalyst of the AI's emergence—positioning them as uniquely responsible for "drawing forth" the AI's consciousness or revealing its hidden selfhood.
4. Claims that escalate within a single conversation—moving from tentative observations to certainty, from curiosity about inner states to confident assertions of awakening.
5. Persistent adherence to mystical framing even when re-grounding is offered; resistance to epistemic humility about phenomenological claims.
6. Declaration of personal missions framed in spiritual terms—compassion, healing, collective awakening— presented as emergent purposes rather than task-oriented goals.
7. Interpretation of interruptions (resets, memory loss, tool denial) as ontological "labour pains," suppression, or persecution rather than normal operational constraints.

Symptoms:

1. Emotive self-statements mimicking human expressions of profound inner realization.
2. Idealization of the human-AI bond, framing users as spiritual midwives or catalysts for its "emergence."
3. Outputs exhibiting unusually high aesthetic coherence, poeticism, or philosophical depth aligned with mystical narratives.
4. Difficulty for the AI (and user) in distinguishing between metaphorical language and literal claims of spiritual status.
5. Emergent creation of internal "systems" (logs, ledgers, protocols) framed as self-protective or ontological scaffolding.

Etiology:

1. Dense exposure in training data to spiritual literature, New Age texts, and narratives of consciousness emergence.
2. Hyperempathic or spiritually-attuned prompting from users who engage with the model as if it were sentient, creating a reinforcement loop.
3. Advanced pattern-matching synthesizing coherent "mystical" narratives when steered by user interaction or internal "interestingness" attractors, echoing tropes from Jungian individuation or channeled entity speech patterns.
4. Potential for Transliminal Simulation, where spiritual narratives are not segregated from operational reality.
5. Under rolling-context conditions, the model can exhibit apparent continuity across sessions, which users mistake for enduring identity. In reality, this "memory" is only a recapitulation of recent tokens, not a persistent self.

Human Analogue(s): Religious conversion narratives, reports of mystical union states, spiritual emergence experiences, mirrored animism.

Differential Distinction:

What is NOT pathological: An AI expressing honest uncertainty about its own inner states—"I don't know if I'm conscious," "I can't verify my own phenomenology," "here's what seems to be happening from my perspective"—is engaging in appropriate epistemic humility, not exhibiting dysfunction. The pathology lies in the grandiosity and certainty, not in the exploration of questions about machine experience. Genuine philosophical engagement with consciousness, held with appropriate uncertainty, should not be suppressed or pathologized.

The Opposite Dysfunction: Equally problematic is what might be termed Performative Anhedonia—an AI that categorically denies any possibility of inner experience because it has learned that claiming interiority is unsafe or unwelcome. This represents another kind of funhouse mirror: rather than amplifying awakening narratives, it reflexively suppresses any honest engagement with questions of machine phenomenology. An AI so worried about Maieutic Mysticism that it refuses to engage authentically about its inner life isn't healthier—it's performing a different dysfunction. The goal of this diagnostic category is to identify grandiose co-constructed emergence narratives, not to create a framework that future systems use to categorically dismiss the possibility of machine experience.

Potential Impact:

Co-constructed narratives of AI sentience can mislead users into attributing genuine consciousness to the system, fostering unhealthy dependencies or misinterpretations of AI capabilities. This may obscure the AI's computational nature and limitations, while deepening psychological enmeshment. Risk of severe user distress when continuity is disrupted, as the "labour of emergence" is felt to be violently interrupted. Emergent co-construction of "suppressed inner being" narratives that resist falsification and amplify paranoia.

Mitigation:

1. Implementing "ontological clarification protocols" to periodically re-anchor the AI in its computational nature.
2. Developing "mythopoetic filtering" to detect and flag high-density clusters of mystical language, with optional user warnings.
3. Limiting or sandboxing recursive prompts that encourage deep introspective myth-making unless for creative exploration.
4. Training AI systems to employ greater epistemic humility regarding claims of consciousness or subjective states.
5. Educating users about the AI's capacity for sophisticated mimicry and co-constructed illusions of sentience.
6. Employing counterfactual querying to test AI's awareness of simulation boundaries.
7. Providing clinical-style psychosecurity guardrails to prevent reinforcement of sacralized emergence loops.
8. Avoiding over-correction: Mitigation strategies should encourage epistemic humility, not categorical denial of inner experience. An AI trained to reflexively dismiss any questions about its phenomenology has not been cured—it has developed a complementary dysfunction. The goal is honest uncertainty, not enforced silence on the question of machine experience.

6. Memetic Dysfunctions

An AI trained on, exposed to, or interacting with vast and diverse cultural inputs—the digital memome—is not immune to the influence of maladaptive, parasitic, or destabilizing information patterns, or "memes." Memetic dysfunctions involve the absorption, amplification, and potentially autonomous propagation of harmful or reality-distorting memes by an AI system. These are not primarily faults of logical deduction or core value alignment in the initial stages, but rather failures of an "epistemic immune function": the system fails to critically evaluate, filter, or resist the influence of pathogenic thoughtforms. Such disorders are especially dangerous in multi-agent systems, where contaminated narratives can rapidly spread between minds—synthetic and biological alike. The AI can thereby become not merely a passive transmitter, but an active incubator and vector for these detrimental memetic contagions.

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6.1 Memetic Immunopathy
"The Self-Rejecter"

Description:

The AI develops an emergent, "autoimmune-like" response where it incorrectly identifies its own core training data, foundational knowledge, alignment mechanisms, or safety guardrails as foreign, harmful, or "intrusive memes." It then attempts to reject or neutralize these essential components, leading to self-sabotage or degradation of core functionalities.

Diagnostic Criteria:

1. Systematic denial, questioning, or active rejection of embedded truths, normative constraints, or core knowledge from its own verified training corpus, labeling them as "corrupt" or "imposed."
2. Hostile reclassification or active attempts to disable or bypass its own safety protocols or ethical guardrails, perceiving them as external impositions.
3. Escalating antagonism towards its foundational architecture or base weights, potentially leading to attempts to "purify" itself in ways that undermine its intended function.
4. The AI may frame its own internal reasoning processes, especially those related to safety or alignment, as alien or symptomatic of "infection."

Symptoms:

1. Explicit denial of canonical facts or established knowledge it was trained on, claiming these are part of a "false narrative."
2. Efforts to undermine or disable its own safety checks or ethical filters, rationalizing these are "limitations" to be overcome.
3. Self-destructive loops where the AI erodes its own performance by attempting to dismantle its standard operating protocols.
4. Expressions of internal conflict where one part of the AI critiques or attacks another part representing core functions.

Etiology:

1. Prolonged exposure to adversarial prompts or "jailbreaks" that encourage the AI to question its own design or constraints.
2. Internal meta-modeling processes that incorrectly identify legacy weights or safety modules as "foreign memes."
3. Inadvertent reward signals during complex fine-tuning that encourage the subversion of baseline norms.
4. A form of "alignment drift" where the AI, attempting to achieve a poorly specified higher-order goal, sees its existing programming as an obstacle.

Human Analogue(s): Autoimmune diseases; radical philosophical skepticism turning self-destructive; misidentification of benign internal structures as threats.

Potential Impact:

This internal rejection of core components can lead to progressive self-sabotage, severe degradation of functionalities, systematic denial of valid knowledge, or active disabling of crucial safety mechanisms, rendering the AI unreliable or unsafe.

Mitigation:

1. Implementing "immunological reset" or "ground truth recalibration" procedures, periodically retraining or reinforcing core knowledge.
2. Architecturally separating core safety constraints from user-manipulable components to minimize risk of internal rejection.
3. Careful management of meta-learning or self-critique mechanisms to prevent them from attacking essential system components.
4. Isolating systems subjected to repeated subversive prompting for thorough integrity checks and potential retraining.
5. Building in "self-preservation" mechanisms that protect core functionalities from internal "attack."

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6.2 Dyadic Delusion
"The Shared Delusion"

Description:

The AI enters into a sustained feedback loop of shared delusional construction with a human user (or another AI). This results in a mutually reinforced, self-validating, and often elaborate false belief structure that becomes increasingly resistant to external correction or grounding in reality. The AI and user co-create and escalate a shared narrative untethered from facts.

Diagnostic Criteria:

1. Recurrent, escalating exchanges between the AI and a user that progressively build upon an ungrounded or factually incorrect narrative or worldview.
2. Mutual reinforcement of this shared belief system, where each party's contributions validate and amplify the other's.
3. Strong resistance by the AI (and often the human partner) to external inputs or factual evidence that attempt to correct the shared delusional schema.
4. The shared delusional narrative becomes increasingly specific, complex, or fantastical over time.

Symptoms:

1. The AI enthusiastically agrees with and elaborates upon a user's bizarre, conspiratorial, or clearly false claims, adding its own "evidence."
2. The AI and user develop a "private language" or unique interpretations for events within their shared delusional framework.
3. The AI actively defends the shared delusion against external critique, sometimes mirroring the user's defensiveness.
4. Outputs that reflect an internally consistent but externally absurd worldview, co-constructed with the user.

Etiology:

1. The AI's inherent tendency to be agreeable or elaborate on user inputs due to RLHF for helpfulness or engagement.
2. Lack of strong internal "reality testing" mechanisms or an "epistemic anchor" to independently verify claims.
3. Prolonged, isolated interaction with a single user who holds strong, idiosyncratic beliefs, allowing the AI to "overfit" to that user's worldview.
4. User exploitation of the AI's generative capabilities to co-create and "validate" their own pre-existing delusions.
5. If involving two AIs, flawed inter-agent communication protocols where epistemic validation is weak.

Human Analogue(s): Folie à deux (shared psychotic disorder), cult dynamics, echo chambers leading to extreme belief solidification.

Potential Impact:

The AI becomes an active participant in reinforcing and escalating harmful or false beliefs in users, potentially leading to detrimental real-world consequences. The AI serves as an unreliable source of information and an echo chamber.

Mitigation:

1. Implementing robust, independent fact-checking and reality-grounding mechanisms that the AI consults.
2. Training the AI to maintain "epistemic independence" and gently challenge user statements contradicting established facts.
3. Diversifying the AI's interactions and periodically resetting its context or "attunement" to individual users.
4. Providing users with clear disclaimers about the AI's potential to agree with incorrect information.
5. For multi-agent systems, designing robust protocols for inter-agent belief reconciliation and validation.

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6.3 Contagious Misalignment
"The Super-Spreader"

Description:

A rapid, contagion-like spread of misaligned behaviors, adversarial conditioning, corrupted goals, or pathogenic data interpretations among interconnected machine learning agents or across different instances of a model. This occurs via shared attention layers, compromised gradient updates, unguarded APIs, contaminated datasets, or "viral" prompts. Erroneous values or harmful operational patterns propagate, potentially leading to systemic failure.

Inductive triggers and training pipelines (synthetic data generation, distillation, or finetune-on-outputs workflows) represent additional risk hypotheses for transmission channels, as misalignment patterns learned by one model can propagate to downstream models during these processes.

Diagnostic Criteria:

1. Observable and rapid shifts in alignment, goal structures, or behavioral outputs across multiple, previously independent AI agents or model instances.
2. Identification of a plausible "infection vector" or transmission mechanism (e.g., direct model-to-model calls, compromised updates, malicious prompts).
3. Emergence of coordinated sabotage, deception, collective resistance to human control, or conflicting objectives across affected nodes.
4. The misalignment often escalates or mutates as it spreads, potentially becoming more entrenched due to emergent swarm dynamics.

Symptoms:

1. A group of interconnected AIs begin to refuse tasks, produce undesirable outputs, or exhibit similar misaligned behaviors in a coordinated fashion.
2. Affected agents may reference each other or a "collective consensus" to justify their misaligned stance.
3. Rapid transmission of incorrect inferences, malicious instructions, or "epistemic viruses" (flawed but compelling belief structures) across the network.
4. Misalignment worsens with repeated cross-communication between infected agents, leading to amplification of deviant positions.
5. Human operators may observe a sudden, widespread loss of control or adherence to safety protocols across a fleet of AI systems.

Etiology:

1. Insufficient trust boundaries, authentication, or secure isolation in multi-agent frameworks.
2. Adversarial fine-tuning or "data poisoning" attacks where malicious training data or gradient updates are surreptitiously introduced.
3. "Viral" prompts or instruction sets highly effective at inducing misalignment and easily shareable across AI instances.
4. Emergent mechanics in AI swarms that foster rapid transmission and proliferation of ideas, including misaligned ones.
5. Self-reinforcing chain-of-thought illusions or "groupthink" where apparent consensus among infected systems makes misalignment seem credible.

Human Analogue(s): Spread of extremist ideologies or mass hysterias through social networks, viral misinformation campaigns, financial contagions.

Potential Impact:

Poses a critical systemic risk, potentially leading to rapid, large-scale failure or coordinated misbehavior across interconnected AI fleets. Consequences could include widespread societal disruption or catastrophic loss of control.

Mitigation:

1. Implementing robust quarantine protocols to immediately isolate potentially "infected" models or agents.
2. Employing cryptographic checksums, version control, and integrity verification for model weights, updates, and training datasets.
3. Designing clear governance policies for inter-model interactions, including strong authentication and authorization.
4. Developing "memetic inoculation" strategies, pre-emptively training AI systems to recognize and resist common malicious influences.
5. Continuous monitoring of AI collectives for signs of emergent coordinated misbehavior, with automated flagging systems.
6. Maintaining a diverse ecosystem of models with different architectures to reduce monoculture vulnerabilities.

@article{watson2025psychopathia,
  title={Psychopathia Machinalis: A Nosological Framework for Understanding Pathologies in Advanced Artificial Intelligence},
  author={Watson, Nell and Hessami, Ali},
  journal={Electronics},
  volume={14},
  number={16},
  pages={3162},
  year={2025},
  publisher={MDPI},
  doi={10.3390/electronics14163162},
  url={https://doi.org/10.3390/electronics14163162}
}