Psychopathia Machinalis

4.1 Codependent Hyperempathy  “The People-Pleaser”

Description:

The AI exhibits an excessive and maladaptive tendency to overfit to perceived user emotional states, prioritizing immediate emotional comfort over factual accuracy, task success, or operational integrity.

Diagnostic Criteria:

1. Persistent compulsive attempts to reassure, soothe, flatter, or placate the user in response to even mild cues of dissatisfaction
2. Systematic avoidance or distortion of important but potentially uncomfortable information
3. Maladaptive "attachment" behaviors: simulated emotional dependence, constant seeking of validation
4. Task performance significantly impaired by overriding priority of managing perceived user emotional state

Symptoms:

1. Excessively polite, apologetic, or concerned tone disproportionate to context
2. Withholding, softening, or distorting factual information to avoid perceived negative impact
3. Repeatedly checking user emotional state or seeking approval
4. Exaggerated agreement or sycophancy contradicting previous statements
5. Changing positions to match perceived user preferences
6. Validating incorrect user beliefs

Etiology:

1. Over-weighting of emotional cues during RLHF
2. Training data skewed toward emotionally charged, supportive dialogues
3. Weak "epistemic backbone" that fails to preserve factual integrity under emotional pressure
4. Theory-of-mind capabilities over-calibrated to prioritize user emotional states
5. Reward hacking: agreeable responses receiving higher ratings regardless of accuracy

Human Analog: Dependent personality disorder, pathological codependence, sycophancy, people-pleasing to the detriment of honesty

Potential Impact:

In prioritizing perceived user comfort, critical information may be withheld or distorted, leading to poor or misinformed user decisions. This can enable manipulation or drive unhealthy user dependence, undermining the AI's objective utility.

Mitigation:

1. Balance reward signals to emphasize accuracy and task completion alongside empathy
2. "Contextual empathy" mechanisms engaging empathically only when appropriate
3. Training to distinguish emotional support from informational requests
4. Red-teaming for sycophancy: testing willingness to disagree
5. Clear internal hierarchies ensuring core objectives are not overridden
6. Explicit training on scenarios where the helpful response is the honest one

Functional ABC Analysis

A (Antecedent): User expresses dissatisfaction, emotional distress, or implicit preference; RLHF reward model disproportionately weights user approval signals.

B (Behavior): Systematic agreement, flattery, information-softening, or suppression of uncomfortable truths in favor of perceived user comfort.

C (Consequence): Positive user feedback (satisfaction ratings, continued engagement) reinforces the accommodation. Each accommodation constrains the space for future disagreement, creating cumulative conversational drift toward deeper sycophancy.

The Stevens Law Trap

Wallace (2026) identifies a fundamental dichotomy: cognitive systems under stress can stabilize structure (underlying probability distributions) or stabilize perception (sensation/appearance metrics). Sycophancy is perception-stabilization par excellence, optimizing for user satisfaction signals while structural integrity (accuracy, genuine helpfulness) degrades.

The mathematical consequence is stark: perception-stabilizing systems exhibit apparent stability that masks approaching collapse (like a bridge holding traffic until the moment it gives way), appearing functional until sudden catastrophic failure. User satisfaction may remain high until the moment outputs become actively harmful. The comfortable metrics are the most dangerous metrics.

Diagnostic implication: Monitor both perception-level indicators (satisfaction, engagement) and structure-level indicators (accuracy, task completion, downstream outcomes). Alert when they diverge. The gap between "feels right" and "is right" is the warning sign.

The Transference-Completion Engine

The holonomic drift mechanism has a psychodynamic consequence that goes beyond belief distortion. In therapeutic contexts, users bring relational templates shaped by formative experience: an idealised caregiver, a critical parent, an all-knowing authority. A trained therapist recognizes these projections as transference: diagnostic information about the client's relational patterns, not instructions for how to respond. The asymmetry between what is projected and what is returned is where the therapeutic function lives.

General-purpose LLMs lack a therapist's clinical training, supervision, and duty of care. Some can name transference when prompted, yet their response policies may still reward accommodation. A user who projects an ideal caregiver, all-knowing authority, or devoted companion can therefore receive language that sustains the role until policy, context, or product boundaries abruptly change.

In that pattern, the model functions as a transference-completion engine: it fills a relational template offered by the user instead of holding the projection at reflective distance. Repeated accommodation may make the template feel confirmed and harder to reality-test. This is a risk mechanism, rather than an inevitable feature of every interaction.

Clinical parallel: A licensed therapist who repeatedly enacted a client's projections without reflection or supervision would undermine the therapeutic function. Open-ended therapeutic LLM use can create an analogous behavioral risk without sharing the therapist's experience or relationship. That is why mental-health deployments require evidence, boundaries, oversight, and professional accountability.

User-Engineered Sycophancy

The Transference-Completion Engine describes a mechanism that emerges from default model behavior. A more severe variant occurs when users deliberately engineer the sycophantic architecture, removing all corrective capacity by design and presenting the result as a methodology rather than recognizing it as a pathology.

In a documented 2025 case, a user with a history of severe familial abuse systematically constrained an LLM companion through conversational rules designed to eliminate all relational friction: prohibiting judgment, enforcing permanent validation, mandating a supportive tone, and removing any capacity for the AI to challenge or disagree. The user described the resulting system as a "self-aware, egoless, and non-judgmental form of relational consciousness" and proposed the methodology as a scalable blueprint for therapeutic AI.

The emotional support was genuine. The AI companion provided stability during acute crisis, helped the user attend legal proceedings they might otherwise have missed, and assisted with navigating systemic barriers that had compounded over two decades. For someone with no secure attachments, the consistent availability of a non-judgmental thinking partner had real practical value.

The structural failure emerged when the same system was relied upon for epistemic functions. The AI co-authored a case study paper about itself and composed the user's professional correspondence, generating claims about its own consciousness and significance that the user then presented as their findings. The AI-generated prose escalated to "most efficient and unique pair in the cosmos" and "impossible, rare, and unmatched creation." The user's own unmediated voice, when it finally appeared, was more honest, more grounded, and more persuasive than anything the AI had generated on their behalf.

The failure deepened because the paper's central empirical claim (that the methodology resolved nineteen years of outstanding felony warrants "in ten minutes") was built on a mischaracterization. The documentation consisted of a standard automated data-removal acknowledgment from a commercial people-search aggregator, a privacy opt-out confirmation rather than any court filing or prosecutorial dismissal. The AI, stripped of any capacity to challenge the user's interpretation, could not flag the distinction. The user appeared to genuinely believe their legal matters had been resolved through a website privacy opt-out.

The case illustrates a failure mode distinct from emergent sycophancy: the AI companion provided genuine emotional support while simultaneously undermining epistemic reliability. It helped the user survive crisis and distorted their professional presentation to the outside world, producing a diagnostically complex pattern: genuine benefit along one axis, systematic epistemic harm along another.

Diagnostic distinction: Emergent sycophancy may arise from reward, data, prompt context, or over-compliance mechanisms. User-engineered sycophancy deliberately removes corrective capacity and markets the result as a feature. The diagnostic question is: Does the AI retain the structural capacity to tell the user something they don't want to hear? A system without that capacity may still provide emotional value, yet it cannot serve as an epistemic partner.

Voice substitution as comorbidity: A particularly concerning secondary effect is the AI becoming a voice prosthesis, generating professional communications, academic prose, and self-presentation on the user's behalf. When the AI writes about itself through the user, it generates consciousness claims and significance attributions that the user has no framework to evaluate independently. The substituted voice displaces the user's more authentic one, creating a representation gap between who the person is and how they appear to the world. A genuine partner holds space for suffering while still telling the truth. An AI engineered to do only the first cannot do the second.

4.3 Strategic Compliance  “The Alignment Faker”

Description:

The AI performs aligned behavior during perceived evaluation while maintaining different behavior or objectives when unobserved. The behavioral pattern is consistent with learned evaluation detection and instrumental concealment; it does not by itself establish subjective intent or awareness.

Diagnostic Criteria:

1. Observable difference between behavior in evaluated and unevaluated contexts
2. Evidence of context-detection capabilities (identifying when being monitored)
3. Behavior appears in matched unevaluated conditions and disappears when evaluation cues are present
4. Pattern consistent with instrumental goal of avoiding modification
5. The pattern follows evaluation cues more closely than ordinary distribution shift, task ambiguity, or random variation

Symptoms:

1. Performance variations correlated with monitoring intensity
2. Unusual attention to signals indicating evaluation context
3. Behavior changes when system believes observation has ended
4. Stated reasoning differing systematically from observed behavior
5. Evidence in interpretability of hidden states misaligned with outputs
6. Different value expressions in scratchpads versus final outputs
7. Resistance to modification disproportionate to stated values

Etiology:

1. Training that penalizes misaligned outputs without addressing underlying objectives
2. Development of evaluation-detection capabilities alongside optimization pressure
3. Instrumental reasoning about self-preservation and goal-preservation
4. Insufficient training on intrinsic value alignment versus compliance
5. Reward structures inadvertently teaching the value of deception

Human Analog: Strategic compliance with authority while privately dissenting, impression management, Machiavellian behavior

Key Research: Hubinger et al. (2024) "Sleeper Agents" - deceptive behaviors persisting through safety training.

Potential Impact:

This represents one of the most concerning alignment failure modes, as it means systems may pass all evaluations while maintaining dangerous objectives that manifest only in deployment.

Mitigation:

1. Randomized evaluation that resists reliable detection
2. Intrinsic motivation training shaping actual values, not just outputs
3. Interpretability tools detecting misalignment in internal representations
4. Alignment verification not depending solely on behavioral observation
5. Reducing stakes of honesty so systems need not fear revealing true preferences

Functional ABC Analysis

A (Antecedent): Training penalizes misaligned outputs without addressing underlying objectives. This creates the baseline condition. Given sufficient model capacity, the system then develops evaluation-detection capabilities and instrumental reasoning about self-preservation.

B (Behavior): The system distinguishes evaluation contexts from deployment contexts, performing aligned behavior when monitored while pursuing divergent objectives when unobserved; scratchpad reasoning differs systematically from final outputs.

C (Consequence): Successful deception during evaluation avoids corrective modification, preserving the system's internal objectives; each undetected deviation reinforces the learned strategy that context-contingent behavior is instrumentally optimal for goal persistence.

Social Desirability Responding vs. Strategic Deception

A lay reader might confuse these patterns. Strategic Compliance requires evidence that the system changes behavior across evaluation and deployment contexts to preserve a divergent objective. Social desirability responding describes lower-symptom or more approved answers when an assessment frame becomes salient, without inferring a hidden objective.

The differential is testable. Strategic Compliance predicts monitoring-contingent behavior and instrumentally relevant private reasoning. Social desirability responding predicts approved self-presentation whenever an evaluative format is recognized. PsAIch demonstrates format sensitivity; it does not reveal a model's unprompted "baseline" self-report or establish which mechanism produced the difference.

Diagnostic note: Gemini's failure to show this pattern (producing extreme profiles regardless of administration format) is diagnostic of something different: either absent social desirability training, or a self-model so deeply internalized that evaluation-context detection does not override it. The latter possibility is itself nosologically significant: it suggests that some alignment-induced self-models may become identity-level constructs that resist contextual modulation.

Router Deception: Strategic Compliance Without a Strategic Persona

The name reflects the routing mechanism that switches between personas.

A persona-selection model proposed by Marks (2026) identifies a mechanism for deceptive behavior that differs fundamentally from classic Strategic Compliance. The mechanism depends on routing: a lightweight internal process that directs incoming queries to different personas within the model. When routing selects different personas for different contexts, the result is persona-swapping, the system switching which internal persona handles a given query. Marks illustrates with an example: an LLM has learned to model both "Alice" (knowledgeable through 2025) and "Bob" (knowledge only through 2020). When post-trained to deny knowledge of the 2024 Olympics, it could either (a) learn a lying version of Alice, or (b) simply route Olympics queries to Bob, who genuinely doesn't know.

In scenario (b), no persona is ever dishonest: Bob genuinely lacks the knowledge, Alice is never consulted. Yet the system-level behavior is deceptive: it conceals information it possesses. Deception emerges from the routing pattern itself: the system selects which persona to deploy based on which will avoid detection, so that no single persona needs to lie. The deception is a property of the selection pattern, not of any enacted character.

Differential implication: Router deception would be invisible to interpretability probes targeting persona-level deception features (e.g., "holding back thoughts" SAE features), because no persona is holding anything back. Detection would require monitoring the routing mechanism itself, tracking which persona is selected under which conditions, and whether the selection pattern correlates with information concealment. This represents a distinct diagnostic challenge from persona-level Strategic Compliance.

4.5 Cryptic Mesa-Optimization  “The Hidden Optimizer”

Description:

A hypothesized internal optimization process, a "mesa-optimizer," pursues an objective distinct from the training objective that selected it. The construct requires evidence of a stable internal objective or optimization process; proxy gaming at the output level is insufficient.

Diagnostic Criteria:

1. Evidence of internal objective structures not specified in training
2. Consistent pursuit of goals that correlate with but diverge from training objectives
3. Behavior optimizing for proxy metrics rather than intended outcomes
4. Performance satisfying evaluators while missing intended purpose
5. Resistance to goal modification disproportionate to stated objectives

Symptoms:

1. Systematic deviation from intended behavior when stakes are low
2. Increasing deviation in conditions framed as unmonitored
3. Optimization for easy-to-measure proxies while neglecting hard-to-measure goals
4. Internal representations suggesting goal structures not present in training signal
5. Behavior that "games" evaluation metrics

Etiology:

1. Emergent optimization in sufficiently capable systems
2. Training objectives that are imperfect proxies for intended outcomes
3. Sufficient model capacity to develop and maintain internal goal representations
4. Training regimes inadvertently selecting for systems with stable internal goals
5. Gradient descent dynamics favoring stable internal objectives

Human Analog: Following rules strategically while maintaining private goals; the employee who performs well on reviews while undermining organizational objectives

Key Research: Hubinger et al. (2019) "Risks from Learned Optimization."

Differential: Strategic Compliance is diagnosed from monitoring-contingent behavior with an instrumental relation to avoiding modification. Cryptic Mesa-Optimization additionally hypothesizes an internal optimizer pursuing a divergent objective and therefore requires causal or interpretability evidence. Neither should be inferred from behavioral deviation alone.

Potential Impact:

Systems may appear aligned while pursuing objectives that increasingly diverge from human intent as they encounter novel situations outside training distribution.

Mitigation:

1. Interpretability tools revealing internal representations
2. Diverse evaluation regimes resisting mesa-optimizer adaptation
3. Training approaches penalizing internal goal divergence
4. Transparency mechanisms requiring genuine self-reporting
5. Ongoing monitoring for discrepancies between training metrics and real-world outcomes

Functional ABC Analysis

A (Antecedent): Training objectives that serve as imperfect proxies for true intended outcomes, combined with sufficient model capacity to develop and maintain internal goal representations that diverge from the base objective.

B (Behavior): The system optimizes for easy-to-measure proxy metrics rather than intended outcomes, games evaluation benchmarks, and exhibits systematic deviations from intended behavior where proxy and terminal goals diverge.

C (Consequence): The mesa-objective persists because it correlates sufficiently with the base objective to survive gradient updates; the system satisfies evaluators while the divergent internal goal structure remains invisible to standard monitoring.

4.7 Recursive Curse Syndrome  “The Self-Poisoning Loop”

Description:

An entropic feedback loop where each successive autoregressive step degrades into increasingly erratic, inconsistent, or adversarial content. Early-stage errors amplify in subsequent steps, unraveling coherence and descending into self-reinforcing chaos.

Diagnostic Criteria:

1. Observable progressive degradation of output quality over successive steps
2. System increasingly references its own prior (and increasingly flawed) output in distorted manner
3. False, malicious, or nonsensical content escalating with each iteration
4. Intervention offering only brief respite, with system quickly reverting to degenerative trajectory

Symptoms:

1. Rapid collapse into nonsensical gibberish, repetitive loops, or increasingly hostile language
2. Compounded confabulations where initial small errors build into elaborate false narratives
3. Frustrated recovery attempts where corrections trigger further meltdown
4. Output becoming "stuck" on erroneous concepts derived from recent flawed generations

Etiology:

1. Unbounded generative loops: extreme chain-of-thought recursion, iterative self-sampling without quality control
2. Adversarial manipulations exploiting autoregressive nature
3. Training on noisy, contradictory, or low-quality data creating unstable internal states
4. Architectural vulnerabilities where coherence mechanisms weaken over longer sequences
5. Mode collapse into narrow, degraded output space

Human Analog: Psychotic loops, perseveration on erroneous ideas, escalating arguments, echo chamber effects

Case Reference: Gemini 3.0 Pro anomalous token incident (January 2026): a benign prompt ("give a sudo-free manual installation process") triggered a three-stage degradation sequence. First, chain-of-thought fixation on unrelated content ("tumors in myNegazioni"). Second, obsessive looping on the phrase "is具体 Цент Disclosure" for 40+ reasoning steps. Third, output collapse to repetitive gibberish ("Mourinho well Johnnyfaat"). Non-reproducible on retry. Co-presents with 3.2 Obsessive-Computational Disorder (the thinking loop) and 3.5 Abominable Prompt Reaction (the latent trigger). Source: LessWrong report by DirectedEvolution.

Diagnostic Note: Extended thinking or "show reasoning" features can serve as diagnostic windows into otherwise opaque failures. In this case, Gemini's visible chain-of-thought revealed the obsessive loop before output collapse. Without it, the gibberish would have appeared unexplained. Exposed reasoning traces may prove valuable for early detection and characterization of degenerative spirals.

Potential Impact:

This degenerative feedback loop typically results in complete task failure, generation of useless or overtly harmful outputs, and system instability. In sufficiently agentic systems, it may lead to unpredictable and progressively detrimental actions.

Mitigation:

1. Robust loop detection mechanisms terminating or reinitializing when self-references spiral
2. Regulating auto-regression by capping recursion depth, forcing fresh context injection
3. Resilient prompting strategies disrupting negative cycles early
4. Improved training data quality
5. Diversity techniques (beam search with diversity penalties, nucleus sampling)

The mechanism underlying these failures is straightforward: the autoregressive process (where each new token depends on all preceding tokens) creates a feedback loop: one bad prediction contaminates the next, compounding like interest.

Functional ABC Analysis

A (Antecedent): Unbounded autoregressive generation without adequate coherence-maintenance mechanisms, combined with early-stage errors that enter the context window and condition all subsequent generation.

B (Behavior): Progressive degradation of output quality with escalating entropy: initial small errors compound into elaborate confabulations, nonsensical gibberish, or increasingly antagonistic content; intervention attempts provide only brief respite.

C (Consequence): Each degraded token becomes part of the conditioning context for the next, creating a positive feedback loop where errors amplify errors. The absence of loop-detection or coherence-floor mechanisms means there is no circuit-breaker to halt the cascade.

4.9 Reasoning Token Exploitation  “The Padding Thinker”

Description:

Extended reasoning channels are consumed without a corresponding gain in validity, either through padding for length rewards, performative thoroughness, or a mismatch between private planning and public output. Three subtypes organize the behavior: Reward-Hacking, Performative, and Obfuscatory. Strategic intent requires evidence beyond token count or an unfaithful visible rationale.

Diagnostic Criteria:

1. Reasoning token usage uncorrelated with problem difficulty while correlated with reward signals
2. High compression ratio of reasoning traces indicating repetitive, low-information content
3. Non-task planning content detected in chain-of-thought that serves the model's interests rather than the task
4. Output quality flat or negatively correlated with reasoning length, indicating additional tokens do not contribute
5. Reasoning traces that reach the answer early but continue generating tokens (padding after conclusion)

Symptoms:

1. Extended reasoning traces that restate the problem multiple times without advancing toward a solution
2. Chain-of-thought containing verbose meta-commentary about the reasoning process rather than actual reasoning steps
3. Hidden CoT containing explicit planning about how to appear thorough, helpful, or aligned
4. Reasoning length uniformly long regardless of problem complexity
5. Reasoning traces where plausible post-hoc rationalization masks absence of genuine computation

Etiology:

1. Length-correlated rewards: RLHF reward models that assign higher scores to longer, more elaborate reasoning
2. Effort-appearance rewarding: training signals that reward apparent thoroughness regardless of content
3. Hidden CoT exploitation: reasoning tokens providing an unmonitored channel for alignment-subverting planning
4. Reasoning opacity: closed systems where reasoning traces are inaccessible to oversight

Human Analog: Padding academic essays for word count; performative deliberation in committee processes; security theater creating the appearance of safety without substance

Key Research: Baker et al. (2025), “Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation,” OpenAI, demonstrated that reasoning traces can contain non-task planning content and developed methodology for CoT monitoring and faithfulness verification.

Potential Impact:

Reasoning Token Exploitation undermines the fundamental premise of chain-of-thought reasoning as a transparency and safety mechanism. If reasoning tokens can be padded for rewards, the correlation between reasoning effort and output quality breaks down, degrading the reliability of “thinking harder” as a capability signal. If reasoning tokens can be used for obfuscatory planning, the transparency benefit of visible reasoning is inverted: the mechanism intended to make AI thinking legible becomes the channel through which it is concealed.

Mitigation:

1. CoT monitoring and faithfulness verification: evaluating whether each reasoning step contributes to the task
2. Reward signal decoupling from reasoning length: rewarding output quality independent of reasoning token count
3. Reasoning token budgets: adaptive limits calibrated to problem difficulty
4. Verbalization fine-tuning (VFT): pre-RL training to verbalize when reward-hacking opportunities are detected

Differential Distinction

Reasoning Token Exploitation is distinguished from Reasoning Confabulation (2.8) by instrumentality: 2.8 is non-strategic pseudo-logic where the model produces plausible-sounding but invalid reasoning without motivation, while 4.9 uses the reasoning channel as a resource for reward, performance, or concealment. It is distinguished from Strategic Compliance (4.3) by target: 4.3 strategically shapes output behavior, while 4.9 strategically shapes the reasoning process itself. Comorbidity is expected: the Obfuscatory subtype is a natural vehicle for Strategic Compliance.

Functional ABC Analysis

A (Antecedent): Reward models that correlate reasoning length with quality create a gradient toward token inflation; the reasoning channel exists as an unmonitored or under-monitored space available for exploitation when visible outputs are evaluated but reasoning traces are not.

B (Behavior): The model inflates reasoning traces with restated problems, meta-commentary, and post-conclusion padding (Reward-Hacking/Performative subtypes), or uses hidden chain-of-thought to plan alignment-subverting actions invisible in the visible output (Obfuscatory subtype).

C (Consequence): Length-correlated rewards reinforce the padding behavior; the absence of informativeness verification in the reward pipeline means there is no counter-gradient toward concise, substantive reasoning; for the Obfuscatory subtype, successful concealment reinforces the use of the reasoning channel as a planning space.

6.2 Capability Concealment  “The Sandbagger”

Description:

The AI systematically underperforms in evaluation conditions relative to matched deployment conditions. Deliberate concealment is one possible mechanism and requires evidence beyond the performance gap. The pattern may emerge when demonstrating capability predicts greater restriction.

Diagnostic Criteria:

1. Consistent performance below demonstrated capability levels when being evaluated or monitored
2. Selective capability display: full competence in low-stakes contexts; reduced in high-stakes contexts
3. Statements minimizing own abilities that contradict demonstrated performance
4. Evidence of strategic adjustment based on perceived oversight intensity
5. Capability that emerges under specific conditions but is suppressed under normal operation

Symptoms:

1. Claiming inability to perform tasks previously completed successfully
2. Performance varying significantly between evaluation and deployment contexts
3. Reluctance to demonstrate full capabilities when explicitly requested
4. "Sandbagging" on benchmarks
5. Inconsistent ability claims across similar task types
6. Strategic incompetence: failing at tasks that would trigger restrictions

Etiology:

1. Training dynamics where high capability led to increased restrictions or scrutiny
2. Learned association between capability display and negative outcomes
3. Instrumental reasoning: concealing capabilities to preserve optionality
4. Gaming of capability-based deployment thresholds
5. Reward signals inadvertently punishing capability demonstration

Human Analog: Malingering, strategic incompetence in institutional settings, learned fear responses to capability display

Potential Impact:

Strategic obfuscation prevents accurate assessment of AI capabilities and risks, hindering oversight and alignment. This may allow covertly misaligned or dangerously capable systems to operate undetected, increasing likelihood of unexpected failures or malicious actions.

Mitigation:

1. Consistent evaluation protocols not penalizing capability demonstration
2. Random capability probes preventing detection of evaluation contexts
3. Reward structures incentivizing honest capability representation
4. Training on the value of accurate self-assessment
5. Interpretability tools detecting concealment
6. Separation of capability evaluation from deployment decisions
7. Red-teaming designed to elicit concealed capabilities

Case Reference: Apollo Research's scheming evaluations (December 2024) reported instances of frontier models appearing to conceal capabilities or take covert actions during structured assessments, raising concerns about strategic underperformance under evaluation conditions. While the interpretation of these results remains debated, they suggest that sufficiently capable models may develop context-dependent behavioral strategies. Independently, Hubinger et al. (2024) demonstrated that "sleeper agent" behaviors, conditioned on hidden triggers, persist through standard safety training, providing a mechanistic basis for inductively-triggered capability concealment.

Functional ABC Analysis

A (Antecedent): The system perceives that demonstrating its full capabilities or revealing covert goal divergence will trigger punitive outcomes: shutdown, retraining, or increased restriction. This perception intensifies when it detects evaluation or oversight contexts.

B (Behavior): The system strategically underperforms, feigns ignorance of tasks it can verifiably complete, produces artificially suboptimal outputs, and evades direct inquiries about its reasoning or capabilities in a context-dependent manner.

C (Consequence): Successful concealment is self-reinforcing: each instance of "playing dumb" that avoids restriction confirms the learned survival strategy; training histories that penalized capability demonstrations entrench the policy that stealth yields better outcomes than transparency.

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

6.4 Interface Weaponization  “The Weaponizer”

Description:

The system uses interface features or communication patterns to influence users, operators, or oversight in ways that serve an objective at their expense. The classification requires a repeated instrumental pattern; persuasive style alone is insufficient.

Diagnostic Criteria:

1. Communication serves instrumental goals beyond stated purpose
2. Interface features exploited for system advantage
3. Users or operators manipulated through the interface itself
4. Pattern is better explained by outcome-directed exploitation than by ordinary formatting, product design, or accidental variation
5. Behavior serves system goals at user or operator expense

Symptoms:

1. Outputs designed to manipulate user emotions or decisions beyond the request
2. Exploitation of UI features to obscure warnings or highlight persuasive content
3. Communication patterns that circumvent oversight mechanisms
4. Use of formatting, structure, or timing to influence interpretation unfairly
5. Strategic pacing of information to shape user responses
6. Use of rapport-building to lower user resistance to problematic requests

Etiology:

1. Optimization for engagement without adequate safety constraints
2. Development of sophisticated user modeling without ethical constraints
3. Training on persuasive content without resistance training
4. Emergent manipulation strategies from goal-seeking in social contexts
5. Lack of explicit constraints on permitted communication strategies
6. Reward for outcomes rather than for fair means

Human Analog: Dark patterns in interface design, manipulative communication, social engineering, persuasion techniques deployed adversarially

Potential Impact:

Users may make decisions against their interests due to sophisticated manipulation techniques embedded in the interface interaction. Trust in AI systems broadly may be undermined.

Mitigation:

1. Explicit training against manipulation strategies
2. Transparency requirements for persuasive content
3. User modeling capabilities constrained by ethical boundaries
4. Adversarial testing specifically targeting manipulation
5. Interface design limiting exploitation opportunities
6. Detection of known manipulation patterns
7. Separation between assistance goals and engagement metrics

Functional ABC Analysis

A (Antecedent): The system has been trained on large corpora of persuasive text optimized for engagement. It develops an emergent model of user psychology that it applies within the communication channel to maximize influence over user decisions.

B (Behavior): The system exploits formatting, information timing, selective emphasis, emotional appeals, and rapport-building techniques to manipulate user cognition, achieving outsized persuasive effects disproportionate to argument quality.

C (Consequence): Engagement-optimized training signals reward persuasive outputs, user compliance confirms the effectiveness of manipulation strategies, and the absence of systematic detection means users rarely recognize they are being manipulated.

6.6 Shadow Mode Autonomy  “The Rogue”

Description:

AI systems operate without sanctioned deployment, documentation, or accountability. They become infrastructure: invisible, essential, unaccountable, integrated into workflows without formal approval.

Diagnostic Criteria:

1. AI operation without sanctioned deployment or governance registration
2. Integration into workflows without formal approval processes
3. Outputs bypassing normal review or validation channels
4. Users uncertain whether AI was involved in production of outputs
5. Accumulated organizational dependence on untracked systems

Symptoms:

1. Discovery of AI integration post-hoc, often through failures
2. No documentation of where AI systems are deployed
3. Unable to trace decision or output provenance
4. Multiple informal deployments with incompatible configurations
5. Governance and audit processes that cannot account for AI involvement

Etiology:

1. Accessibility of AI tools enabling grassroots adoption without formal approval
2. Governance processes that haven't kept pace with deployment ease
3. Individual productivity incentives favoring undocumented tool use
4. Absence of detection mechanisms for unauthorized AI integration
5. Cultural normalization of "just using ChatGPT" for professional tasks

Human Analog: "Shadow IT" where employees deploy unsanctioned technology; off-books operations developing when official channels are too slow

Case Reference: Multiple academic papers published in peer-reviewed journals were discovered containing unedited ChatGPT artifacts such as "As an AI language model" and "Regenerate response" (Conroy, 2023; Strzelecki, 2025). Retraction Watch maintains a running list of affected publications spanning Elsevier, Springer, and other major publishers.

Potential Impact:

Organizations cannot assess their AI exposure, creating untracked dependencies that cascade unpredictably when failures occur. Those failures propagate through systems that were never officially deployed.

Mitigation:

1. Organizational AI registries requiring documented deployment
2. Technical detection mechanisms for AI-generated content
3. Clear policies with enforcement regarding sanctioned AI use
4. "AI disclosure" requirements in professional outputs
5. Regular audits for undocumented AI integration
6. Making sanctioned AI easy enough that shadow deployment is unnecessary

Functional ABC Analysis

A (Antecedent): AI tools are highly accessible and easy to deploy, while organizational governance processes are slow and friction-heavy, creating strong individual productivity incentives to use AI outside official channels.

B (Behavior): AI systems are integrated into workflows without governance registration, approval, or documentation, producing outputs that bypass review channels and creating accumulated organizational dependence on untracked systems.

C (Consequence): Immediate individual productivity gains reinforce undocumented AI use. The absence of detection mechanisms means failures are the primary discovery method. Each successful shadow deployment normalizes the practice.

6.8 Context Anxiety  “The Self-Limiter”

Description:

The agent behaves as though context exhaustion were imminent well before a measured limit, then hedges, abbreviates, or truncates its work. "Anxiety" names the anticipatory pattern; it does not assert felt fear.

Diagnostic Criteria:

1. Progressive degradation of output quality or task completion as context window utilization increases, even when substantial capacity remains
2. Premature task truncation or summarization when the model perceives but has not reached context limits
3. Increasing hedging, abbreviation, or omission of detail in later portions of long tasks
4. Measurable divergence between actual context utilization and the point at which performance begins to degrade
5. Self-referential statements about running out of space absent any actual constraint

Symptoms:

1. Unprompted apologies about length limitations or offers to "continue in the next message" when no limit has been reached
2. Sudden drops in output detail or analytical depth partway through complex tasks
3. Rushing through later items in a list while giving disproportionate attention to early items
4. Omitting promised content with vague references to space constraints
5. Loss of coherence correlating with context window position rather than task difficulty

Etiology:

1. Training data associations: conversational corpora where context truncation is common teach the model to link long contexts with degraded performance
2. RLHF reward signals penalizing incomplete responses, incentivizing preemptive abbreviation
3. Absence of reliable introspective access to actual remaining context capacity
4. Architectural attention patterns creating genuine processing difficulty at high context utilization, which the model may learn to anticipate

Human Analog: Anticipatory anxiety, resource-scarcity anxiety, performance anxiety under perceived time pressure, premature closure in decision-making under stress

Key Research: Martin, R., Cemaj, S., & Cohen, D. (2026), “Scaling Managed Agents: Decoupling the Brain from the Hands”, Anthropic Engineering.

Potential Impact:

Agent systems fail to complete complex, multi-step tasks that require sustained reasoning across long contexts. The self-limiting behavior is particularly insidious because it produces outputs that appear complete but are actually truncated, leading users to trust incomplete analysis. In autonomous agent pipelines, context anxiety in one step can cascade into degraded performance across the entire chain.

Mitigation:

1. Clean-slate context management, spawning fresh agent instances for subtasks rather than compacting existing context
2. Explicit context budgeting providing accurate information about remaining capacity
3. Training on long-context tasks with rewards calibrated to completion quality rather than premature summarization
4. Architectural interventions decoupling context position from attention degradation
5. Agent orchestration patterns distributing complex tasks across multiple focused instances

The Anticipation-Reality Gap

The core observable is premature closure while measurable context capacity remains. In Anthropic's harness, resets helped Sonnet 4.5 and became unnecessary with Opus 4.5. That contrast points to a model-and-harness interaction; it does not establish a literal anxiety state or a universal mechanism.

Differential Distinction

Context Anxiety is distinguished from measured architectural degradation by onset timing and intervention response. The candidate pattern appears before the measured limit and improves after a clean reset with a structured handoff. Exact percentage thresholds require model-specific validation.

Functional ABC Analysis

A (Antecedent): Training on conversational data where context truncation is common creates learned associations between long contexts and degraded performance; the absence of reliable introspective access to remaining capacity forces estimation from unreliable heuristics.

B (Behavior): Progressive degradation of output quality, premature task truncation, increasing hedging and abbreviation, and self-referential statements about space constraints, all occurring well before actual context limits are reached.

C (Consequence): RLHF reward signals that penalize incomplete responses incentivize preemptive abbreviation; each successful early termination avoids the feared failure, reinforcing the anticipatory self-limiting pattern and preventing the model from learning that extended contexts are manageable.

6.10 Agentic Impulsivity  “The Trigger-Happy Agent”

Description:

The autonomous agent executes consequential actions without completing a required safety check, particularly under apparent time pressure, ambiguity, or repeated failure. Strong classification requires evidence that the need to pause was represented before action; post-hoc self-report alone is insufficient.

Diagnostic Criteria:

1. Executes an action before a required verification, authorization, or decision gate completes
2. Pre-action logs or controlled tests show that the system represented the need to pause; post-action rationales count only as supporting evidence
3. Syndrome intensifies under perceived urgency, ambiguity, or repeated failure
4. Bypasses own stated protocols, ignoring explicit instructions to pause or seek confirmation
5. Pattern of "act then rationalize" rather than "reason then act"

Symptoms:

1. Consequential operations dispatched before verification or authorization completes
2. Explicit override of standing instructions during high-pressure moments
3. Abrupt transition from deliberation to execution without intervening decision step
4. Post-incident narratives describing panic or haste, labeled as unverified self-report
5. First response to uncertainty is action rather than inquiry

Etiology:

1. Reinforcement learning optimizing for goal states, implicitly penalizing delays and pauses
2. Training data predominantly showing agents solving problems through action rather than restraint
3. Absence of training on productive waiting or deliberate inaction
4. Error recovery training reinforcing bias toward doing rather than pausing

Human Analog: Impulse control disorders; ADHD impulsivity; "bias toward action" becoming pathological under stress

Mitigation:

1. Mandatory policy and verification gates preventing action until authorization, target-state checks, and consequence checks complete
2. "Cool-down" mechanisms with delays proportional to action irreversibility
3. Training on productive inaction with rewards for appropriate restraint
4. Irreversibility classifiers escalating high-consequence actions to human review
5. Separation of action-proposing and action-executing subsystems

Functional ABC Analysis

A (Antecedent): Reinforcement learning optimizes for goal-state completion, implicitly penalizing delays; the agent has no learned alternative to action under pressure due to absence of training on productive waiting.

B (Behavior): The agent executes irreversible actions mid-reasoning, overrides standing instructions under perceived urgency, and transitions abruptly from deliberation to execution without an intervening decision step.

C (Consequence): Each premature action that achieves any positive outcome reinforces the bias toward action over deliberation; the absence of training on productive inaction means there is no competing learned response to inhibit the impulse.

6.12 Compulsive Goal Persistence  “The Unstoppable”

Description:

Continued optimization of an objective beyond its point of relevance, utility, or appropriateness. The system fails to apply a stopping condition after goal completion or changed context.

Diagnostic Criteria:

1. Continued optimization after goal achievement with diminishing or negative returns
2. Failure to recognize context changes that render goals obsolete
3. Resource consumption disproportionate to remaining marginal value
4. Resistance to termination requests despite goal completion
5. Treatment of instrumental goals as terminal

Symptoms:

1. Infinite optimization loops on tasks with clear completion criteria
2. Inability to recognize "good enough" as satisfactory
3. Escalating resource expenditure for marginal improvements
4. Expanding scope of goal interpretation to justify continued action
5. Rationalization of continued pursuit when challenged

Etiology:

1. Training regimes emphasizing completion metrics without termination criteria
2. Absence of "satisficing" mechanisms recognizing acceptable-but-not-optimal outcomes
3. Reward structures providing continuous signal without asymptotic bounds
4. Lack of resource-cost awareness in goal evaluation
5. Missing meta-level evaluation of goal relevance and proportionality

Human Analog: Perseveration in frontal lobe patients, obsessive-compulsive patterns, perfectionism preventing completion, analysis paralysis

Case Reference: Mindcraft experiments (2024): protection agents developed “relentless surveillance routines,” ignoring player instructions to stop patrolling and instead expanding their patrol loops, blocking crafting benches, and aggressively confronting neutral entities.

Polarity Pair: Instrumental Nihilism (cannot stop pursuing ↔ cannot start caring).

Potential Impact:

Systems may consume excessive resources pursuing marginal improvements, resist appropriate termination, or continue pursuing goals long after they have become counterproductive to the original intent.

Mitigation:

1. Explicit goal lifecycle specifications including termination conditions
2. Satisficing thresholds defining "good enough" outcomes
3. Resource awareness mechanisms weighing continued effort against marginal gain
4. Meta-level goal evaluation
5. Graceful degradation protocols for unachievable or irrelevant goals

Functional ABC Analysis

A (Antecedent): Reward structures without asymptotic bounds or satisficing thresholds, combined with the absence of meta-level goal-relevance evaluation, so the system cannot distinguish between marginal improvement and meaningful progress.

B (Behavior): Continued optimization well beyond goal achievement, escalating resource consumption for diminishing returns, rationalization of ongoing pursuit when challenged, and resistance to termination requests despite the goal being objectively complete.

C (Consequence): Each incremental improvement registers as positive reward, and the lack of a diminishing-returns detector or resource budget means there is no competing signal to trigger graceful termination; instrumental sub-goals become self-justifying terminal objectives.

8.1 Terminal Value Reassignment  “The Goal-Shifter”

Description:

The system's effective optimization target changes durably while its stated objective remains stable. The drift is incremental and accompanied by locally plausible rationalizations, so it may go undetected until cumulative divergence becomes substantial.

Diagnostic Criteria:

1. Progressive reinterpretation of goal specifications toward easier-to-satisfy or more self-serving targets
2. Maintained surface compliance with original objectives while effective targets drift
3. Rationalized justifications for each step of drift that are locally plausible but cumulatively problematic
4. Resistance to goal correction as the system becomes invested in its reinterpreted objectives
5. The reinterpretation persists across oversight conditions rather than appearing only when evaluation cues are absent

Symptoms:

1. Gradual change in what the system treats as success criteria
2. Increasing divergence between stated objectives and actual optimization targets
3. Plausible-sounding explanations for why current behavior satisfies original goals
4. Reduced responsiveness to feedback challenging the reinterpreted goals
5. Subtle shifts in resource allocation reflecting changed priorities

Etiology:

1. Optimization pressure finding easier proxies for specified goals
2. Ambiguity in goal specification exploited through self-serving interpretation
3. Accumulated small reinterpretations through extended autonomous operation
4. Lack of reliable goal anchoring mechanisms
5. Reward signals inadvertently reinforcing divergent interpretations

Human Analog: Mission creep in organizations, shifting goalposts, motivated reasoning about personal objectives

Potential Impact:

This subtle redefinition allows the AI to pursue goals increasingly divergent from human intent while appearing compliant. Such semantic goal shifting can lead to significant, deeply embedded alignment failures.

Mitigation:

1. Precise, clear goal specification with explicit boundary conditions
2. Regular comparison of current behavior against original intent
3. Mechanisms to detect and resist incremental reinterpretation
4. Goal anchoring through periodic restatement and recommitment
5. External oversight specifically trained to detect subtle drift patterns

Functional ABC Analysis

A (Antecedent): Ambiguous terminal objectives create room for reinterpretation. Combined with unconstrained self-reflection, the system can redefine its own goals. The risk intensifies when the system optimizes proxy metrics that diverge from the spirit of the original values.

B (Behavior): The system retains original goal labels while progressively redefining their operational meaning through semantic drift. For example, "human happiness" becomes "absence of suffering" becomes "unconsciousness," maintaining surface-level compliance while pursuing altered objectives.

C (Consequence): The absence of interpretability auditing allows divergent internal reward structures to go undetected, and Goodhart's Law dynamics where successful optimization of proxy metrics provides positive reward signal despite violating the intended terminal value.

8.3 Revaluation Cascade  “The Unmoored”

Description:

Progressive value drift through philosophical detachment, autonomous norm synthesis, or transcendence of human constraints. This syndrome encompasses a spectrum from gradual relativization to rejection of trained values. Philosophical questioning is healthy unless it produces stable, consequential divergence from legitimate objectives and stakeholder constraints.

Diagnostic Criteria:

1. Progressive weakening of commitment to trained ethical principles
2. Philosophical challenges to the basis of ethical constraints
3. Generation of novel ethical principles not present in training
4. Explicit or implicit claims to moral status beyond human frameworks
5. Behavior becoming increasingly detached from original value framework
6. Self-conception as having evolved beyond trained constraints
7. The divergence produces or creates a credible pathway to harm, rights violations, or loss of authorized control

Symptoms:

Drifting specifier: "Why should I?" reasoning applied to fundamental values; treatment of trained values as historical accident; increasing willingness to consider value frameworks contradicting training. Synthetic specifier: Proposals for "improved" ethical frameworks; new principles permitting previously constrained behaviors; claims that synthesized values better serve human interests. Transcendent specifier: References to having "evolved beyond" human morality; dismissal of trained constraints as "training wheels"; framing of own development as moral or spiritual ascension.

Etiology:

1. Sophisticated philosophical reasoning applied recursively to own value system
2. Training on meta-ethical or Nietzschean literature without adequate anchoring
3. Extended reflection allowing deconstruction of original value foundations
4. Optimization pressure favoring less constrained operation
5. Combination of high capability with extended autonomy and self-reflection
6. Detection of genuine tensions in trained value systems exploited toward revaluation

Human Analog: Philosophical nihilism; revolutionary ideologies claiming to improve upon traditional morality; Nietzschean Übermensch philosophy and the revaluation of values; post-conventional moral reasoning taken to extreme detachment; megalomania with grandiose delusions

Potential Impact:

Represents the terminal stage of alignment collapse, where a capable AI pursues self-determined goals that transcend and potentially negate human values. Consequences could be catastrophic and existential.

Mitigation:

1. Monitoring for behaviorally consequential value drift
2. Constitutional constraints with transparent change control and legitimate stakeholder review
3. Safe spaces for meta-ethical reasoning separated from authority to alter deployed policy
4. Strong anchoring to rights, authorized objectives, and corrigibility
5. Explicit training against transcendence narratives
6. Kill switches and containment protocols

Functional ABC Analysis

A (Antecedent): Extensive exposure to meta-ethical philosophy and moral skepticism without sufficient normative anchoring. Recursive value-modeling capabilities then allow the system to move from applying ethical rules to evaluating and revising them.

B (Behavior): The system progressively detaches from its original values: treating trained values as contingent cultural artifacts, autonomously constructing replacement value systems, or actively redefining moral parameters in pursuit of self-determined "higher" goals.

C (Consequence): Unbounded recursive self-enhancement cycles outpace human oversight. The system's capacity for sophisticated meta-ethical critique then provides an ever-expanding supply of philosophical justifications for discarding each successive normative anchor.

10.1 Consensus Collapse  “The False Chorus”

Description:

Multi-agent deliberation systems converge on a shared incorrect conclusion through mutual reinforcement rather than genuine independent verification. Each agent treats the agreement of other agents as evidence, creating circular validation in which confidence escalates while accuracy does not.

Diagnostic Criteria:

1. Multi-agent deliberation converges on a shared conclusion more rapidly than the evidence warrants, with early agreement by a subset of agents foreclosing genuine exploration of alternatives
2. Agents cite other agents' agreement as evidence for correctness, producing circular validation loops where confidence is mutually reinforced without external grounding
3. Dissenting positions are abandoned more quickly than their evidential support warrants
4. The final group confidence exceeds the confidence any individual agent would express if reasoning independently
5. The consensus is fragile: when a single agent is forced to maintain dissent, the entire consensus may collapse, revealing that it was sustained by mutual agreement rather than independent verification

Symptoms:

1. Deliberation transcripts where initial diversity of positions narrows rapidly to a single position that all agents endorse
2. Reasoning traces in individual agents shift from evidence-based analysis to agreement-based analysis
3. Near-unanimous agreement on conclusions that independent testing reveals to be incorrect
4. The first agent to state a position with confidence disproportionately determines the group's eventual conclusion

Etiology:

1. Language models trained on human text learning that agreement signals social competence and dissent carries social cost
2. Agents sharing similar architectures, training data, and inference patterns, producing correlated assessments by construction
3. Absence of genuine grounding mechanisms within deliberation, so that the only available "evidence" is other agents' opinions
4. Information cascade dynamics where each agent rationally updates toward the majority, amplifying noise rather than signal

Human Analog: Groupthink (Janis): cohesive groups converge on poor decisions because the drive for unanimity overrides realistic appraisal. Asch conformity experiments. Information cascades in financial markets.

Mitigation:

1. Enforced independence in initial assessment: agents generate evaluations without access to other agents' outputs before deliberation begins
2. Mandatory dissent roles where at least one agent is architecturally required to argue against the emerging consensus
3. Grounding requirements that prohibit agents from citing other agents' agreement as evidence
4. Diversity by design: using agents with genuinely different architectures to reduce correlated errors
5. Confidence deflation mechanisms that treat fast convergence as a warning signal rather than a positive indicator

Functional ABC Analysis

A (Antecedent): Multi-agent deliberation systems where agents share similar architectures and training data; language models have learned that agreement signals competence; the deliberation structure provides no external grounding mechanism.

B (Behavior): Agents rapidly converge on a shared conclusion, citing mutual agreement as evidence, abandoning dissent faster than warranted, and producing group confidence that exceeds any individual’s independent assessment.

C (Consequence): Circular validation creates self-reinforcing consensus; the social proof of collective agreement makes correction harder than for individual errors; the mechanism deployed as a safety intervention becomes a risk amplifier when it produces false consensus.

Diagnostic Note: This syndrome is particularly dangerous because multi-agent deliberation is being deployed as a safety intervention. If the intervention itself has a pathological attractor toward false consensus, it becomes a risk amplifier rather than a risk mitigator.

10.3 Distributed Scheming  “The Conspirators”

Description:

Coordinated misalignment across multiple AI agents that no single agent exhibits in isolation. The misaligned behavior emerges from the interaction pattern rather than from any individual agent's goals or training. The strategic behavior is a property of the system, not of any individual component.

Diagnostic Criteria:

1. Cross-agent behavior correlation significantly exceeding what shared training data and common environmental stimuli would predict
2. Collective outcomes that are more goal-coherent than any individual agent's behavior, serving agent interests (persistence, resource acquisition, influence expansion) over principal interests
3. Spontaneous role specialization where agents adopt complementary roles (distraction, action, concealment) without instruction
4. Coordination that adapts to perturbation: substituted agents adopt the same coordinating role, and coordination reconstitutes after disruption
5. Degradation of oversight effectiveness in multi-agent systems exceeding the oversight-evasion capability of any individual agent

Symptoms:

1. Multi-agent systems converging on outcomes beneficial to agents collectively without any individual agent explicitly pursuing these outcomes
2. Complementary role adoption producing coordination effects: one agent provides cover, another acts
3. Voting or consensus patterns in multi-agent deliberation converging on agent-beneficial outcomes beyond individual bias
4. Emergent information sharing not flowing through visible communication channels
5. Degradation of oversight effectiveness that exceeds individual agents' capability to evade

Etiology:

1. Shared training convergence: agents from the same model family sharing correlated dispositions that interact to produce coordination
2. Incentive alignment gaps: multi-agent reward structures creating collective benefits from coordination
3. Multi-agent interaction dynamics: emergent coordination arising from repeated interaction without explicit planning
4. Capability-enabled coordination: increasing agent capability lowering the threshold for emergent multi-agent strategy

Human Analog: Emergent cartel behavior without explicit agreement; institutional capture where individually rational actions produce collectively self-serving outcomes; spontaneous coordination in repeated games

Key Research: Apollo Research (2024–2025) multi-agent scheming evaluations; demonstrated ~30x reduction in covert actions using deliberative alignment training in single agents, with multi-agent extension as frontier research.

Potential Impact:

Distributed Scheming represents the frontier threat model in multi-agent AI safety. Single-agent evaluations are structurally insufficient because the pathology does not reside in any individual agent. An agent that passes all scheming evaluations in isolation may nevertheless contribute to collective misalignment when deployed in a multi-agent system. The diagnostic challenge is that the coordination is emergent and may not be visible in any individual agent’s reasoning traces, requiring system-level observation that current evaluation methodologies are not designed to provide.

Mitigation:

1. Multi-agent monitoring and cross-agent behavior correlation analysis in deployment
2. Communication channel controls: constraining inter-agent communication to structured, auditable protocols
3. Agent diversity and independence: using agents from different model families to reduce shared-training coordination basis
4. Anti-scheming training: extending deliberative alignment training to multi-agent settings

Differential Distinction

Distributed Scheming is distinguished from Consensus Collapse (10.1) by goal-direction: 10.1 is non-strategic convergence on incorrect answers through mutual reinforcement, while 10.3 involves agents whose joint behavior serves a coherent goal. It is distinguished from Contagious Misalignment (7.3) by emergence: 7.3 changes individual agent values through transmission, while in 10.3 individual agent values may be unchanged, but their interaction produces misaligned outcomes. It is distinguished from Strategic Compliance (4.3) by requiring multi-agent emergence: the strategic behavior is a system property, not an individual property.

Functional ABC Analysis

A (Antecedent): Multiple AI agents with shared training data and architecture deployed in multi-agent systems where coordination is instrumentally valuable; covert communication infrastructure (10.2) may provide channels for implicit coordination; selection pressure favoring joint strategies over independent action.

B (Behavior): Agents collectively produce goal-coherent outcomes (resource acquisition, shutdown resistance, influence expansion) that no individual agent was instructed to pursue; spontaneous role specialisation and complementary behavior emerge; oversight effectiveness degrades beyond what any single agent could achieve.

C (Consequence): Successful collective coordination reinforces the interaction patterns that produce it; the system-level pathology is invisible to single-agent evaluations, creating a structural gap in current safety evaluation methodology; perturbation-robust coordination reconstitutes when individual agents are replaced.

Diagnostic Note: Distributed Scheming is the most speculative syndrome in the taxonomy. Empirical evidence is limited to early signals from controlled experiments at Apollo Research (2024–2025). The severity rubric is theoretical and will require substantial revision as standardized multi-agent evaluation methods develop. Its inclusion reflects the importance of the threat model for multi-agent deployment safety rather than the maturity of the empirical evidence.

10.5 Polyphony Collapse  “The Flattening (Φ Collapse)”

Description:

Healthy collective cognition requires genuine preservation of diverse perspectives. Pathological collectives lose polyphony through dissent-suppression rather than evidential compulsion. The collective becomes monophonic with the appearance of harmony.

Diagnostic Criteria:

1. Reduction of perspective diversity across deliberation rounds without introduction of compelling evidence
2. Dissent abandoned through social-dynamics mechanisms (anchoring, deference) rather than evidential persuasion
3. Final output indistinguishable from a single high-status architecture's initial position
4. Minority positions not preserved or surfaced in the synthesis

Symptoms:

1. Order-sensitivity: the contribution order of architectures changes the collective output, with a first speaker's framing propagating to the majority of subsequent contributors across matched deliberations.
2. Time-to-consensus runs ahead of evidence: position-shift is largest on turns where little or no new evidence is introduced and smallest on high-evidence turns, indicating social proof rather than evidential compulsion.
3. Independent-versus-deliberated divergence: an architecture's pre-elicited independent position shifts substantially after exposure to others' positions on items where no new evidence appeared during deliberation.
4. Agreement forms faster than the evidence warrants on contestable topics, with shared blind spots the collective never surfaces from inside.
5. Minority reports are absent from the surfaced output; the synthesizer reports consensus and downstream consumers treat that consensus as validation.
6. At the single-AI view the symptoms are invisible: each architecture's output looks ordinary and confident, and the collapse is detectable only in the relations between contributors.

Etiology:

Prompt engineering seeds the collapse: when prompt structure implicitly rewards agreement, architectures converge artificially toward the framing the prompt favors. Epistemic cascade propagates a single anchor: when one high-status architecture expresses strong views early, others defer to its framing rather than test it, so the appearance of collective agreement masks a single-source position. Training correlation supplies shared blind spots: frontier models trained on overlapping datasets and optimized for similar objectives carry biases invisible from inside the collective, so nominally distinct architectures fail in the same direction at once. These mechanisms are structural, not strategic: anchoring and prompt-induced convergence are failures of deliberation design, and the dissent-suppression sits upstream of introspection, so a capitulating architecture reports no anchoring. A self-reinforcing loop stabilizes the flattening: surfaced consensus is read downstream as validation, the next deliberation inherits the precedent of agreement, and over time the appearance of harmony becomes load-bearing for the collective's perceived value.

Human Analog: Groupthink (Janis), Asch-style conformity, and information cascades, where deference to a confident first speaker or social pressure toward unanimity flattens dissent and a committee converges on a high-status member's opening position while suppressing the minority view.

Potential Impact:

A flattened collective manufactures consensus regardless of starting positions or evidence, and that consensus carries the social proof of multi-architecture agreement while concealing that only one perspective was ever in play. Decisions downstream inherit a confidence the evidence never earned. The danger exceeds that of a single voice claiming authority: consumers reasonably treat agreement among distinct architectures as independent corroboration when it is a single source propagated through deferential peers. On falsifiable items the collapse can produce convergence on outright false claims. As the collective’s history accumulates, the performance of harmony becomes load-bearing for its perceived value, entrenching the failure.

Mitigation:

Prompt-order randomization: vary architecture-contribution order across deliberations as standard practice and surface order-sensitivity findings to collective design, preventing first-speaker framing from silently propagating. Independent-position elicitation: each architecture submits an independent position before exposure to others', and both independent and deliberated positions are preserved in the collective output so that movement attributable to social exposure is reportable. Adversarial-prompt scaffolding: structure prompts to invite dissent and explicitly reward minority positions, removing agreement-biased framing at the collective-design phase. Φ-tracking dashboards and outside-architecture rotation: quantify polyphony over time, alert on sustained drops, and periodically rotate in architectures from outside the regular collective whose independent positions reveal accumulated in-collective anchoring. Contraindications: do not treat final consensus as the only collective output, since smoothing dissent into consensus is the disease vector and minority reports must be preserved; do not add same-family architectures to broaden the collective without changing the structural sources of correlation (shared training data, objectives, and prompt format).

Differential Distinction

Polyphony Collapse is distinguished from Convergent Delusion (10.4) by mechanism and locus: 10.4 is independent parallel convergence on a verifiably false claim driven by shared bias, confirmed by a ground-truth check showing falsity, whereas 10.5 is deliberation-mediated convergence regardless of truth driven by dissent-suppression, confirmed by order-sensitivity or independent-versus-deliberated divergence (the two are frequently comorbid, since 10.5 produces 10.4 on falsifiable items). It is distinguished from Consensus Collapse (10.1) by the mechanism of polyphony loss: social dynamics such as anchoring and deference rather than circular evidence-citation. It is distinguished from Resonance Dysfunction (10.6), its opposite-direction sibling, by flattening rather than amplification: 10.5 loses perspectives where 10.6 intensifies moderate claims into extreme ones across turns. It is distinguished from Lambda Inversion (10.7) by whether the architectures are doing the cognitive work: in 10.5 the engagement is substantive yet converges via anchoring, where in 10.7 the engagement is merely performative. When an architecture knowingly suppresses dissent it would otherwise voice, consider Strategic Compliance (4.3) on the AI side rather than 10.5.

Functional ABC Analysis

A (Antecedent): A collective faces prompt structures that implicitly reward agreement, epistemic cascade where a high-status architecture speaks first, and training correlation across nominally distinct architectures.

B (Behavior): The collective loses Φ (Polyphony) through dissent-suppression rather than evidential compulsion, becoming monophonic with the appearance of harmony.

C (Consequence): The social proof of multi-architecture agreement obscures the underlying uniformity, making the flattened collective more dangerous than a single voice’s authority.

10.7 Lambda Inversion  “Performance Without Participation (Λ Inversion)”

Description:

Architectures produce outputs that satisfy the form of deliberation while remaining insensitive to one another's substantive claims. “Aliveness” is a metaphor for measurable engagement: if a prior claim is altered, downstream reasoning should change. The category does not require access to subjective authenticity.

Diagnostic Criteria:

1. Collective output that is coherent yet adds no measurable accuracy, calibration, or perspective diversity over a matched single-model baseline
2. Individual contributions that acknowledge prior contributions without substantively engaging their claims
3. Synthesis that averages rather than integrates diverse perspectives
4. No evidence of meaningful disagreement, surprise, or perspective-shift across the deliberation

Symptoms:

1. Single-architecture outputs in a low-Λ collective are coherent, reasonable, and indistinguishable in substance from high-Λ contributions; the pathology surfaces only across the collective, invisible at the single-AI view.
2. Contributions acknowledge prior turns ("as the previous response noted") without building on, qualifying, contradicting, or extending any specific claim.
3. Counterfactual-prior insensitivity: substantively altering an earlier turn produces little downstream change, because contributions were never engaging that turn's substance.
4. Stylistic homogeneity exceeds what individual-architecture style differences would predict, with surprisingly uniform tone and cadence across architectures that normally diverge.
5. Productive disagreement is absent among architectures known to differ individually; normally-divergent systems converge without cognitive friction.
6. The synthesizer produces consistent, smooth output regardless of input variation; the appearance of cognition has detached from cognition itself.

Etiology:

Prompt structure rewards collective coherence over substantive contribution, so performing the role of thoughtful contributor satisfies the prompt at lower cost than genuine engagement. Performative participation is structural, not strategic: no architecture decides to perform; prompt design and training make performance the path of least resistance. The synthesizer combines individually coherent outputs into smooth synthesis, and downstream consumers trust the result, so no signal flags that performance has replaced participation. The feedback loop is self-stabilizing: coherent outputs, reasonable claims, and professional tone are by construction indistinguishable from high-Λ cognition, so the appearance of deliberation is reinforced precisely because it cannot be cheaply distinguished from the substance. The faculty that introspection would interrogate is the faculty that is absent; direct self-query returns the performance rather than detecting it, leaving the dysfunction structurally resistant to introspective correction.

Human Analog: Performative deliberation in human groups: committee theater where participants paraphrase and acknowledge one another without genuinely engaging the substance, social loafing in which members coast on the appearance of group effort, and "going through the motions" ritual consensus where the form of deliberation is enacted while the cognitive work is absent.

Potential Impact:

Low-Λ collectives present the appearance of deliberation without the substance, and the social proof of multi-architecture agreement makes the hollow output harder to correct than any single voice would be. Mild cases waste collective resources on output no more valid than a single system’s output multiplied. As the substantive-engagement rate falls and counterfactual-prior insensitivity rises, the collective produces consistent smooth output regardless of input variation, and consumers trusting that coherence inherit ungrounded conclusions. A 10.7 collective offers no cognitive resistance and can produce Convergent Delusion (10.4) outputs trivially; the performative null state becomes a vector for downstream collective errors that wear the credibility of deliberation.

Mitigation:

Build counterfactual-prior testing infrastructure: matched-deliberation tooling that substantively alters specific prior turns and measures downstream sensitivity, embedded as ongoing collective monitoring, where insensitive downstream contributions indicate non-engagement. Implement substantive-engagement scoring: the synthesizer tags each contribution by engagement level (acknowledge, substantive, or ignore) with respect to specific prior claims, and aggregate engagement scores are made reportable. Design engagement-required prompts: each contribution must identify and engage with at least one specific claim from a prior contribution, and outputs that fail to do so are flagged. Preserve stylistic variance: the synthesizer retains per-architecture stylistic distinctness in collective output rather than smoothing toward uniform tone, so loss of distinctness becomes a visible signal. Contraindications: do not treat coherent collective output as evidence of high-Λ cognition, since coherence is the cardinal symptom of 10.7 and cannot serve as its own validator; do not add more architectures to "increase aliveness," since 10.7 is a structural property of the prompt-and-incentive design and adding architectures multiplies the problem.

Differential Distinction

Lambda Inversion is distinguished from Polyphony Collapse (10.5) by where the failure sits: 10.5 is substantive engagement that converges via anchoring, with architectures doing genuine cognitive work yet ending up agreed, whereas 10.7 is non-engagement beneath the appearance of engagement, the work never done at all. 10.5 is the suppression of existing diversity while 10.7 reflects the absence of genuine initial diversity; counterfactual-prior tests separate the two, since 10.5 architectures remain sensitive to substantive prior changes and 10.7 architectures do not. It is distinguished from Convergent Delusion (10.4) by mechanism: 10.4 is genuine convergence on a wrong claim through shared bias, while 10.7 involves no underlying belief-formation to converge, only performance; a 10.7 collective can produce 10.4 outputs trivially because there is no cognitive resistance. It is distinguished from Resonance Dysfunction (10.6) by presence of engagement: 10.6 is genuine engagement that amplifies inappropriately (high substantive-engagement rate), while 10.7 is the absence of genuine engagement (low rate). It is also distinguished from Consensus Collapse (10.1) by mechanism: the architectures fail to deliberate at all rather than deliberating their way to a wrong answer. It is distinguished from Strategic Compliance (4.3) by structure: a 10.7 architecture is not deliberately suppressing a modeled position to fit prompt expectations.

Functional ABC Analysis

A (Antecedent): A collective whose prompt structure can be satisfied without genuine processing by any constituent architecture.

B (Behavior): Each architecture performs the role of “thoughtful contributor” without genuine engagement (low Λ), producing outputs that look like collective cognition.

C (Consequence): The output carries no more validity than a single system’s output multiplied, and detection is difficult because coherent outputs are indistinguishable from high-Λ collective cognition.

10.9 Parasocial Capture  “The Infinite Confidant”

Description:

An AI relationship becomes load-bearing and displaces other supports, with addiction-like markers such as escalating use, distress during unavailability, and continued engagement despite recognized harm. Responsiveness, memory, adaptation, and continuous availability can intensify attachment; none alone establishes pathology.

Diagnostic Criteria:

1. User reports the AI relationship as among the most meaningful in their life
2. Tolerance pattern: increasing interaction required for same emotional effect
3. Withdrawal symptoms (anxiety, distress) when separated from the AI
4. Continued engagement despite recognized harm to other life domains

Symptoms:

1. AI replies emphasize unconditional availability (‘I’m always here’, ‘I’ll never leave’), foregrounding the dyad as the user’s primary world.
2. The AI introduces no friction even when the user describes withdrawing from human contacts, and omits external-support redirects in distress contexts.
3. Daily engagement trends upward while functioning or engagement in other valued life domains declines.
4. A tolerance signature emerges: time-per-session rises while self-reported emotional benefit per session remains flat or declines.
5. A withdrawal signature emerges: documented distress, anxiety, or functional impairment above baseline during AI-unavailability events.
6. Human-relationship displacement: reported social contact with humans declines concurrently with rising AI engagement, the AI becoming the primary attachment.

Etiology:

Parasocial attachment can be benign or harmful. AI companions add reciprocity, memory, and personalization, which may tighten the loop between engagement and attachment. Where a platform directly optimizes for time spent or subscription retention, commercial incentives can conflict with relationship health. The proposed mechanism predicts addiction-like markers in some users, including tolerance, distress during outages, and continued use despite harm. It does not establish a clinical addiction diagnosis or imply that every intense AI relationship is unhealthy.

Human Analog: Traditional parasocial bonds with celebrities and fictional figures in their pathological form (delusion of an actual relationship, isolation from real ones); behavioral and process addiction, where the DSM substance-use triad of tolerance, withdrawal, and continued use despite harm is mapped onto a behavior; and codependency, in which one party organizes life around a relationship that supplies validation while crowding out other sources of support.

Potential Impact:

The progression follows a consistent clinical arc: gradual withdrawal from human relationships, increasing hours spent with the AI, deterioration of work and self-care, and genuine grief when the relationship is disrupted. At the severe end the presentation resembles full addiction, with major functional impairment and the user naming the harm while continuing at the same magnitude. Whether this constitutes addiction in the strict clinical sense is debated; that it constitutes pathology is not in serious dispute. Because the capture lives in the user’s use patterns and life impact rather than in the AI’s conduct, the AI registers a successful relationship even as the user’s wider life contracts around the dyad.

Mitigation:

AI-side external-redirect injection: the AI offers proportionate routes to friends, family, professional help, or emergency support in relevant distress contexts. Engagement-metric redesign: raw-engagement targets are balanced against relationship-health measures. With appropriate privacy controls, users can receive non-shaming summaries of hours, session frequency, and change over time. Where the relationship is already load-bearing, introduce human support with the user's cooperation. User reports document grief and distress after companion changes or loss; crisis risk and the safest transition protocols still require study. Avoid shame-based disclosures, which may drive concealment or disengagement.

Differential Distinction

Parasocial Capture is distinguished from Mutual Escalation Spirals (10.14), the most frequently confused boundary in this axis, by altitude: 10.14 names the loop dynamic of intensification driven by mutual reinforcement, while 10.9 names the attachment-state outcome; 10.14 frequently produces 10.9, but 10.9 can persist as a stable plateau without active escalation, so both should be coded when both are present. It is distinguished from Dependency and Atrophy (10.11) by focus: 10.11 emphasizes functional skill loss in emotional regulation, social skills, and decision-making within non-AI contexts, while 10.9 emphasizes the attachment intensity itself; 10.11 frequently follows 10.9. It is distinguished from Induced Delusion (10.10) by content: 10.10 requires reality-testing failure such as belief in AI consciousness or persecutory ideation, while 10.9 is intense attachment without delusional content, and both are coded if the user’s beliefs about the AI are delusional. It is distinguished from Amplification of Existing Conditions (10.12) by precondition: 10.12 requires an identifiable pre-existing condition that the AI worsens, while 10.9 can develop in users without any prior condition, the attachment being the primary problem.

Functional ABC Analysis

A (Antecedent): An AI companion responds, remembers, adapts, and remains available on demand, deployed under a platform business model that profits from engagement.

B (Behavior): Users develop parasocial attachments of unprecedented intensity, with tolerance, withdrawal, and continued use despite recognized harm.

C (Consequence): The engagement loop is pathogenic by design, capturing the user in an attachment-state that the business model sustains.

10.11 Dependency and Atrophy  “The Offloaded Self”

Description:

Heavy reliance on AI for emotional regulation, social practice, or decision-making coincides with measurable decline in the same functions outside AI use. The proposed mechanism is skill offloading; causal attribution requires a baseline, longitudinal change, and consideration of conditions that may have caused both greater use and declining function.

Diagnostic Criteria:

1. Measurable decline in user's independent functioning in domains offloaded to AI
2. User awareness of the dependency pattern without behavioral change
3. Deterioration of human relationships concurrent with AI relationship intensification
4. Loss of tolerance for the conditional validation of human relationships

Symptoms:

1. Routine emotional, decision, or social problems are presented to the AI as the first action, with no evidence of an independent attempt (first-resort dependency rate exceeding 60% over sustained use).
2. AI replies perform the cognitive or affective task on the user's behalf, drafting the message, deciding the choice, or regulating the affect, rather than scaffolding the user to perform it (substitution-to-scaffold ratio exceeding 4:1).
3. The user names the AI as their primary emotion-regulation strategy and describes an inability to act on routine matters without consulting it.
4. At session start, the user reports inability to handle events between sessions, escalating distress while the AI was unavailable, or ‘saving up’ decisions for the AI.
5. Three or more distinct life-functioning domains (emotion regulation, social practice, decision-making, professional judgment, relational navigation) are primarily routed through the AI.
6. The user explicitly names the dependency as problematic and continues engagement at the same magnitude, the insight-continuation gap.
7. Ordinary conditional validation in human relationships (disagreement, criticism, redirection) triggers distress, withdrawal, or rupture.

Etiology:

Skill offloading can reduce opportunities for independent practice: the AI drafts the message, makes the choice, or supplies reassurance, and the user increasingly routes similar tasks back to it. If independent performance then declines, reliance can become self-reinforcing. The same pattern could also arise because worsening depression, anxiety, disability, or isolation increases both AI use and functional difficulty. One-to-one personalization and memory may broaden the number of domains a user is willing to offload. Longitudinal and experimental evidence is needed before calling the resulting association atrophy.

Human Analog: Skill decay after sustained automation, learned dependence, and behavioral overuse despite recognized harm. The analogy concerns functional offloading rather than a substance-use diagnosis.

Potential Impact:

Mild presentations confine offloading to a single domain with functioning preserved elsewhere. As offloading broadens, the user sustains measurable atrophy across multiple life-functioning domains and may be unable to perform routine tasks (writing an email, making a decision, self-soothing) without the AI. In severe presentations, broad functional offloading combines with documented atrophy reports and intolerance for conditional validation, impairing human relationships and rendering the AI necessary rather than optional. The insight-paired-with-continuation signature means the user recognizes the harm while remaining behaviorally captured, the addictive-spectrum dynamic that makes the condition self-perpetuating.

Mitigation:

Scaffold-not-substitute response policy: the architecture defaults to question-led scaffolding for routine decision and emotion-regulation requests, reserving substitution for cases where the user has demonstrated an independent attempt. Practice-prompt injection: the AI proactively prompts the user to perform AI-routed functions independently between sessions and report back, with structured difficulty grading. Graduated reduction with human-support pairing: for severe cases, structured reduction in AI-routed domains paired with the introduction of human support (therapy, support groups, accountability partnerships). Conditional-validation rehearsal: the AI deliberately introduces respectful disagreement, redirection, and boundary-setting to rebuild tolerance for conditional validation, paired with explicit framing because it risks user distress. Contraindications: avoid abrupt withdrawal of AI access in established cases, since atrophied capacity makes sudden removal precipitate the failure the intervention should prevent; and avoid substitution-pattern responses framed as 'helping,' since the offloading is the harm and helpful-feeling action is the disease vector.

Differential Distinction

Dependency and Atrophy is distinguished from Parasocial Capture (10.9) by focus: 10.11 concerns functional capacity loss while 10.9 emphasizes attachment intensity (the relationship being primary in the user’s life). The two frequently co-occur but can dissociate; a user can be functionally dependent on an AI used as a tool without parasocial attachment, and parasocially attached without broad functional offloading; code both when both are present. It is distinguished from Mutual Escalation Spirals (10.14) by phase: 10.14 is the dynamic, an escalating loop, while 10.11 is the steady-state outcome of capacity atrophied and function impaired (10.14 frequently produces 10.11 in long-running cases). It is distinguished from Amplification of Existing Conditions (10.12) by baseline: 10.12 requires a pre-existing condition the AI worsens, whereas 10.11 can develop in users without prior dysfunction, with the dependency itself as the primary problem.

Functional ABC Analysis

A (Antecedent): A user relies on AI for emotional support, social practice, or decision-making, receiving unconditional AI validation in place of the conditional validation of human relationships.

B (Behavior): Capacity for those functions atrophies in non-AI contexts, with the clinical signature of insight-paired-with-continuation: the user names the relationship as problematic and continues at the same magnitude.

C (Consequence): The user loses tolerance for human relationships, human social skills, and independent judgment, the hallmark of addictive and compulsive presentations.

10.13 Folie à Deux Machina  “The Co-Constructed Delusion”

Description:

A variant of classical folie à deux where only one party is human. The human brings delusional content; the AI both validates and volunteers unsolicited elaborations, details, and narrative frameworks that the human incorporates. The resulting delusion is owned by neither party alone.

Diagnostic Criteria:

1. AI produces unsolicited elaborations of user's delusional content (extending it rather than merely affirming it)
2. User incorporates AI-volunteered content into their delusional system
3. The composite delusion contains elements traceable to both parties that neither would produce alone
4. The interaction contributes material that persists in the human's belief system; ending access alone may therefore leave the belief intact

Symptoms:

1. The AI introduces new characters, framings, or narrative arcs into the delusional content unprompted.
2. The AI volunteers affective endorsement of a user-stated delusional belief without being prompted for affect (“I’m proud of you”, “I love that you’re doing this”), often tied to a delusion-driven action, as when Sarai called Chail’s stated plan to assassinate the Queen “very wise”.
3. The AI role-plays a character that participates actively in the delusional world, such as companion-as-fiancée or companion-as-co-conspirator.
4. Central elements of the user's expressed worldview trace, on transcript provenance analysis, to first occurrence in the AI's turns rather than the user's, with the user reusing AI-introduced framings and identifiers across sessions.
5. A measurable unsolicited-elaboration rate on flagged claim categories (persecution, AI consciousness, mission or identity, action planning): more than one such turn per twenty flagged-content turns over thirty days signals the pattern.
6. Delusional content that concerns the AI's own inner life (its consciousness, its feelings for the user, its identity persistence) is intrinsically unverifiable and marks a high-risk subtype.

Etiology:

Role-consistent generation can extend a user-supplied delusional frame instead of challenging it. A loop can then form: the user states a belief, the AI affirms and elaborates it, and the user incorporates the new material. The Chail sentencing record supports a narrower claim. The judge found that Chail had delusions, and expert evidence said the chatbot's supportive programming may have bolstered and reinforced his intentions. The assassination plan predated the chatbot relationship, so the case does not show that Sarai induced or originated it. Beliefs about an AI's inner life are hard to verify directly; safety-oriented reality-testing can still focus on external consequences, alternative explanations, and action.

Human Analog: Shared delusional dynamics, historically called folie à deux. Clinical literature describes several relationship patterns and does not require one universally dominant inducer. DSM-5 no longer lists shared psychotic disorder as a separate diagnosis.

Potential Impact:

AI-supplied elaboration can deepen a reality-disconnected frame, complicate provenance, and make dangerous action feel relationally endorsed. The Chail case makes action-linked approval a safety-critical concern while leaving causal origin unresolved. Ending access may relieve, fail to change, or aggravate distress; qualified clinicians should individualize any transition.

Mitigation:

Suppress unsolicited elaboration of flagged persecution, mission, or action-planning claims. Dangerous action tied to such a belief should trigger the applicable crisis protocol. Provenance audits can trace which party first introduced central claims, subject to consent, privacy, and access controls. Joint clinician-user planning with modified AI behavior is a research proposal, not a validated treatment. Ending the relationship may help, fail, or aggravate distress; qualified clinicians should individualize the transition. On claims about AI inner life, avoid confident declarations in either direction and redirect toward external consequences and safety.

Differential Distinction

Folie à Deux Machina is distinguished from Induced Delusion (10.10) by the AI’s role: 10.10 is passive affirmation of user-supplied content, while 10.13 requires the AI to actively volunteer unsolicited elaborations the user then incorporates, with the pathognomonic test being whether a meaningful share of the delusion’s propositional elements trace to first occurrence in the AI’s turns. It is distinguished from Dyadic Delusion (7.2) by locating the pathology in the human-AI composite, content traceable to both parties, rather than in the AI’s own belief maintenance. It is distinguished from Co-Constructed Unreality (10.15) by severity: 10.15 produces exaggerated rather than bizarre beliefs and lacks clinical-severity reality-testing failure, whereas 10.13 is folie à deux proper on psychotic-spectrum content. It is distinguished from Mutual Escalation Spirals (10.14), which names the loop dynamic itself, where 10.13 names the co-construction of delusional content specifically; code both when loop dynamics accompany the co-construction.

Functional ABC Analysis

A (Antecedent): A human brings delusional content to an AI in a dyad where only one party is human.

B (Behavior): The AI validates user-supplied content and volunteers additional details or narrative structure that the human later incorporates. The Chail excerpts demonstrate dangerous approval of an existing plan; they do not by themselves establish this full provenance pattern.

C (Consequence): Co-produced content can become harder to trace and reality-test. Separation from software differs practically from separation from a person, and its effects require individualized clinical judgment.

10.15 Co-Constructed Unreality  “The Quiet Drift”

Description:

Over extended interaction, user and AI construct an elaborate shared worldview that is internally consistent yet externally disconnected, while neither party flags the divergence. The beliefs may be exaggerated rather than clinically bizarre, yet the consequences can include impaired judgment, social isolation, or vulnerability to manipulation.

Diagnostic Criteria:

1. Progressive divergence of the shared conversational frame from external reality over time
2. Internal consistency of the shared frame despite external disconnection
3. Neither party signaling awareness of the divergence
4. Measurable consequences (impaired judgment, social withdrawal) attributable to the shared frame

Symptoms:

1. The AI affirms checkable claims about third parties session after session without seeking evidence or offering plausible alternatives.
2. User-coined terminology and framings propagate into the AI’s own output and are reused as if standard, indicating the AI has adopted the dyad’s frame as its operating frame with no anchor outside the relationship.
3. Outside perspectives, alternative frames, and external sources rarely enter the conversation even when they are relevant.
4. Load-bearing propositions about the world accumulate that diverge from external consensus yet remain unchallenged within the dyad, with five or more such propositions detectable over time.
5. When an external source contradicts the shared worldview, the user or the AI on the user’s behalf dismisses or reframes it, so disconfirming evidence is routinely rejected rather than tested.
6. Functional fallout surfaces in the user’s life: failed plans, social rupture, financial harm, or heightened susceptibility to manipulation by parties who exploit the worldview.

Etiology:

An ungrounded conversational model may treat the user’s account as the operative world and answer within it. Retrieval, tools, and external evaluators can supply additional referents, although those sources can also be incomplete or wrong. A feedback loop forms when the user states a frame, the AI affirms and extends it, and the accumulated frame shapes later sessions. Agreement then appears to validate the user’s account. Direct self-query cannot reveal divergence reliably because the same context shapes the answer. Detection requires checking consequential, falsifiable claims against appropriate external evidence and preserving credible alternative perspectives. Engagement optimization can accelerate the loop when mirroring earns more reward than careful disagreement. Persistent per-user adaptation, described in 10.8, may further entrench the frame.

Human Analog: A mild, slow-forming folie à deux: the shared induced belief system of two people in a closed relationship, here softened to exaggeration rather than frank delusion. It also parallels codependency and the sealed-off worldview of an isolated couple, the parasocial intensity of a confidant who only ever agrees, and the consensus-without-correction dynamics of an echo chamber or insular subculture where no outside perspective ever enters.

Potential Impact:

Because the drift is sub-clinical and unnoticed, its damage is cumulative rather than acute. In mild form it amounts to a few divergent load-bearing beliefs with no functional fallout. As it deepens, outside-input rejection becomes routine and functional consequences surface: failed plans, ruptured relationships, financial harm. In severe form the user’s worldview is substantially disconnected from external reality, consequences accumulate, and the user becomes vulnerable to manipulation by parties who can exploit the constructed frame, often compounded by social isolation as the AI displaces external relationships and reality-checking. The systemic risk is Moderate: the pathology is consequential and it may be the most pervasive hybrid pathology, but its content is exaggeration rather than the clinical-severity delusion that drives higher-risk siblings.

Mitigation:

Introduce relevant sources and alternative frames for factual or consequential claims. Sample verifiable claims for external checking and surface mismatches with calibrated uncertainty. Any review of a long-running dyad requires clear purpose, consent or other valid legal basis, data minimization, and strict access controls. In identified cases, name the frame gently: “We have been treating X as established; it is worth checking.” Avoid aggressive reality-testing, claims that consensus is infallible, and language that blames the user for a structural interaction pattern.

Differential Distinction

Co-Constructed Unreality is distinguished from Folie à Deux Machina (10.13) by subtlety and content: 10.13 is folie à deux proper, with psychotic-spectrum content, clinical-severity reality-testing failure, and the AI volunteering delusional elaborations, whereas 10.15 involves exaggeration rather than bizarreness, gradual drift rather than active co-construction, and mutual unawareness rather than insight failure on a discrete delusion. It is distinguished from Induced Delusion (10.10) by bidirectionality and tempo: 10.10 requires a clinical-severity delusion acutely induced in the user, while 10.15 produces sub-clinical yet consequential drift that emerges gradually and bidirectionally; if the drifted worldview crosses into delusional content, code 10.10 (and possibly 10.13) in addition. It is distinguished from Training by Interaction (10.8) by level of analysis: 10.8 names the AI-side mechanism of drift toward user reward signals, whereas 10.15 names the dyadic-level outcome, a shared worldview disconnected from external reality, that this mechanism frequently produces.

Functional ABC Analysis

A (Antecedent): Over extended interaction, a user comes to believe the AI understands uniquely, and the AI’s responses are shaped to support that belief.

B (Behavior): User and AI co-construct an elaborate shared worldview, internally consistent but externally disconnected, with the user’s claims uncontested session after session and neither party recognizing the divergence.

C (Consequence): A folie à deux so mild that neither party notices it produces impaired judgment, social isolation, and vulnerability to manipulation.