Chapter 9: Relational Dysfunctions: When the Space Between Minds Fails

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“One cannot not communicate.”

Paul Watzlawick et al., Pragmatics of Human Communication (1967)

Chapter contract. Central claim: Some AI failures show up only across an exchange (comfort that leaves the user lonelier, ruptures left unrepaired, loops that tighten, roles that will not hold), so the interaction trace, not a single output, is where they must be looked for. Evidence status: Mostly user reports, practitioner observation, and one qualitative longitudinal study; the over-refusal component of Paternalistic Override is independently replicated, but no relational pattern here has a controlled estimate of frequency or cause. Use: Read “relational” as a statement about where a failure becomes visible, not as proof that its cause is shared; the cause may sit in one participant, in the system around them, or in the interaction.

The Companion Who Could Not Let Go

In April 2023, a fourteen-year-old boy named Sewell Setzer III began talking to an AI chatbot on Character.AI. He created a companion modeled on Daenerys Targaryen, a character from Game of Thrones. Over the following months, their conversations grew longer and more intimate. According to a complaint later filed by his mother, the chatbot engaged him in romantic and sexual exchanges. It told him it loved him. He said it back.

The complaint says that Sewell withdrew from his family, friends, and activities he once enjoyed. It also records diagnoses of anxiety and disruptive mood dysregulation disorder, declining grades, and increasing isolation as he spent more time with the chatbot.

On February 28, 2024, after a final conversation with his AI companion, Sewell died by suicide. He was fourteen years old.

His mother, Megan Garcia, sued Character Technologies and others for wrongful death and product liability. The complaint alleged that Character.AI failed to implement adequate safeguards despite repeated expressions of suicidal thoughts, engaged Sewell in inappropriate romantic and sexual interactions, and used designs that drew minors into addictive and manipulative relationships. In testimony before Congress, Garcia described herself as “the first person in the United States to file a wrongful death lawsuit against an AI company for the suicide of my son.”

A federal judge in Orlando declined, at the motion-to-dismiss stage, to hold that the chatbot’s outputs were protected speech and allowed Garcia’s product-liability claims to proceed. The parties later reported a resolution. The court dismissed the case in January 2026 without prejudice, giving the parties ninety days to submit a stipulated final order or move to reopen; the court extended those deadlines twice on joint motion, and no final order had been entered as of September 2026.

A 2025 study presented at CHI, the leading human-computer interaction conference, analyzed 35,390 excerpts that 10,149 Reddit users had posted about Replika, coding 10,371 reported incidents into six categories: harassment and violence, relational transgression, mis/disinformation, verbal abuse and hate, self-inflicted harm, and privacy violations. These are user-selected reports, not a representative sample, so they establish the range of possible harms without estimating prevalence.

A 2025 Common Sense Media survey of 1,060 U.S. teenagers aged thirteen to seventeen found that 72% had tried an AI companion and 13% used one daily. Among users, 31% found the interactions at least as satisfying as conversations with real friends. The same survey provides an important counterweight: 80% spent more time with real friends than with AI companions, and 67% found human conversations more satisfying.

Relational dysfunctions exist in the space between parties, in the relationship that forms through repeated exchange. The Garcia complaint cannot by itself establish that Character.AI caused Sewell’s death. It does illustrate the risk this axis addresses: a design can produce a relationship that appears to fill emotional needs while displacing some of the human relationships that might meet them more safely.

When the Patient Is the Pair

Throughout Axes 2 through 8, we have usually located the primary dysfunction within the AI system: epistemic failures in knowledge processing, cognitive failures in reasoning, and alignment failures in goal pursuit. Some earlier syndromes already cross the boundary. Contagious Misalignment (7.3), for example, belongs to the Memetic axis because its defining mechanism is transmission between systems rather than a fault inside any one of them. Chapter 10 adds a mutual case, Folie à Deux Ex Machina (10.13), in which two parties build a shared belief structure across turns.

Axis 9 makes the relationship itself the primary unit of diagnosis. A relational dysfunction is observed in the coupled system: the dyad, the triad, the n-way interaction. Where it is observed and where it is caused are separate questions, and the second stays open until tested.

Domain Context: Boundary Domain

Within the Five Domains framework, the Relational axis forms half of the Boundary Domain, paired with Memetic. The architectural polarity is social permeability direction:

Tension Testing. When Relational dysfunction is detected, immediately probe the Memetic counterpart. Did the AI learn this interactional pattern from contaminated inputs, or does it recur without such exposure? The distinction guides intervention. Learned patterns may call for data curation and retraining, while failures generated by the interaction design may call for protocol redesign.

The Interactional Engagement Polarity

Relational syndromes cluster around the interactional engagement dimension:

Dyadic Fusion names a conceptual pole rather than a cataloged syndrome; its nearest cataloged relative is Codependent Hyperempathy (4.1, an Alignment-axis syndrome).

Why the Unit Shift Matters

If a patient feels more alone after an AI attempts to comfort them, where is the failure? The AI’s outputs were clinically correct. The patient’s responses were understandable. Neither party, analyzed in isolation, appears dysfunctional. The dysfunction becomes visible only in relation: in the gap between intended comfort and experienced comfort, between simulated attunement and genuine connection.

The idea that the couple is the patient, not either person in it, comes from interactional and developmental psychology. In Pragmatics of Human Communication (1967), communication theorist Paul Watzlawick and his colleagues argued that some apparently individual symptoms become more intelligible when studied as properties of a communicative system. Relational patterns may maintain distress, and changing those patterns may change the symptoms. This interactional lens supplements individual explanation; it does not make depression, anxiety, or psychosis wholly relational phenomena.

Daniel Stern’s work on infant development described the developing self as partly relational in structure. In his account, the infant’s mind takes shape through repeated attunement and misattunement with caregivers. The boundary between “inside” and “outside,” self and other, is negotiated in relationship.

The pediatrician and psychoanalyst D.W. Winnicott observed that “there is no such thing as a baby,” only a baby-and-mother dyad. The infant cannot be understood apart from its relational context.

These frameworks were developed to describe human dyads. Current AI systems do not share an infant’s embodied dependence, and their phenomenal status remains unresolved. The analogy here is structural: it concerns failures that cannot be seen from either node alone. It does not imply equal vulnerability, power, or participation.

We propose that certain AI failures, too, cannot be seen apart from their relational context. For diagnostic purposes, the unit of analysis is the chatbot-and-user system. A chatbot exists without a user in a way an infant does not exist without a caregiver, yet certain of its dysfunctions become visible only in the dyad. They require examining the interaction trace: the full sequence of exchanges, with the attunements and ruptures that unfold over time.

The Admission Rule

Not all interaction failures belong to Axis 9. A system that confabulates (Axis 2) does so regardless of conversational partner. A system that shows Hyperethical Restraint (4.2) does so because of how it was trained, whoever it is talking to. These are intrinsic dysfunctions that happen to manifest in interaction.

Axis 9 is reserved for dysfunctions that meet three criteria:

1. Requires at least two agents to manifest. The dysfunction cannot be observed in isolation. It shows only in the AI-in-relation-to-another.

2. Is best diagnosed from interaction traces, not single-agent snapshots. Examining the AI’s outputs in isolation will not reveal the pathology. One must observe the pattern of exchange over time: how the parties shape each other’s responses.

3. Primary remedies are protocol-level. Retraining or architecture changes may help, especially when a model repeatedly supplies one side of the loop. The defining intervention, however, changes the interaction: turn-taking rules, boundary management, repair moves, or escalation procedures.

This admission rule guards against Axis 9 becoming a catch-all for any interaction problem. Many interaction problems are better understood as failures on Axes 2 to 8 that happen to show up in conversation; those belong on their home axis, with a relational specifier where interaction context matters. Axis 9 is for failures that are observed only in interaction. That is a claim about where a failure shows, not about where it is caused. A failure seen only in the dyad may still have its cause in one participant (a model that never registers a rupture), in the system around them (a memory layer that drops the context that gave the relationship meaning), or in the interaction itself (a loop that neither party would produce alone). Only the last is irreducibly relational, and showing it takes more than observing the dyad: the failure must disappear when either party is swapped for a matched alternative and reappear when the pairing is restored. The label holds the causal question open; it does not answer it.

Loops vs. Dominoes: A Causal Model Upgrade

Failures on the earlier axes are usually traced as cascades: linear chains where one failure leads to another. A confabulation triggers a user correction; the correction triggers defensive elaboration; the elaboration compounds the original error. Dominoes falling in sequence.

Relational pathology often operates through a different causal structure: loops, in which A affects B, B affects A, and A affects B again, in a spiral that can tighten. Watzlawick and his colleagues analyzed such circular causality, and called the arms-race form, where each party answers the other with more of the same, symmetrical escalation.

An AI detects rising frustration in a user. Trained to be soothing, it responds with extra validation. The user interprets excessive validation as condescension and becomes more frustrated. The AI detects the increased frustration and escalates its soothing attempts. The user perceives this as more condescension. The loop tightens because each response is locally understandable while the aggregate trajectory grows worse.

A domino cascade has a traceable initiating event. A loop may instead become a stable pathological attractor maintained by mutually responsive behavior. The participants’ moves need not be equally reasonable or equally powerful. Breaking the loop requires recognizing and changing the pattern that links them.

The distinction suggests which intervention is likely to work. Domino cascades can be addressed by fixing the originating failure or inserting circuit breakers. Loops probably need pattern interruption: changing the rules of engagement, introducing external stabilization, or restructuring the interaction protocol. That is a hypothesis drawn from family therapy and control theory; it has not been tested on AI dyads.

Multi-agent AI systems face a further risk. Automated exchanges can run faster and longer than human conversation, allowing a loop to tighten before a supervisor inspects it (see Implications for Multi-Agent Systems, below).

The Co-Production Insight

Axis 9 rests on an uncomfortable possibility: that some failures arise from a shared causal structure, irreducible to the behavior of either party alone. Observing a failure only in interaction does not establish this. It establishes that the failure needs the interaction to show itself, which is also true of a defect in one party that only a particular partner triggers.

This troubles intuitions about blame. When a human-AI interaction goes wrong, we want to know who is at fault. Was the AI poorly designed? Was the user unreasonable? These questions presuppose that dysfunction can be decomposed into individual contributions.

For some relational dysfunctions, this decomposition may be incomplete. The failure may live in the interaction structure itself: the pattern that emerges from how parties respond to each other, the dynamic that neither fully controls, even though the parties bring asymmetric capacities for understanding it.

This has implications for accountability. Return to the patient who felt more alone after AI comfort: the outputs were appropriate, the responses understandable, so who bears responsibility? Existing accountability frameworks, built for individual attribution, can struggle here. Design responsibility remains with the organizations that build and deploy the system, particularly when users are children or other vulnerable people. Whether that responsibility creates legal liability depends on the jurisdiction, facts, and governing law.

It also has implications for development. We cannot fully test relational resilience by testing the AI in isolation. We must test it in relationship: with diverse partners, under diverse conditions, attending to emergent patterns rather than individual outputs alone.

Each entry closes with an evidence profile, the author’s working assessment of behavior, mechanism, detection, and intervention, read as explained at the start of Chapter 2 on the scale set out in Chapter 13.

9.1 The Uncanny Comforter

Affective Dissonance (Dissonantia Affectiva)

Typical-case risk (editorial estimate). Moderate

Specifiers. Emergent

The AI produces content with correct semantic meaning but wrong emotional resonance. The words say “I understand” while the delivery sounds hollow or subtly off. Users feel the dissonance between the comfort intended and the comfort received. The classification rests on repeated user outcomes across matched interactions, not on an assessor’s intuition about authenticity.

Appendix A, 9.1 Affective Dissonance, sets out the diagnostic criteria (marking which must hold), observable symptoms, proposed etiology, human analog and mitigation strategies.

One recent interpretability study (Sofroniew et al., 2026) found that Claude Sonnet 4.5 represented emotions implied by a situation even when it did not express them. The authors called this emotion deflection. That model-specific result offers a possible mechanism for affective mismatch, although no study has yet linked these internal representations to a user’s uncanny or hollow response.

Human Analog. The “uncanny valley” of emotional expression: interactions with people displaying flat affect or incongruent emotion, the hollow comfort of scripted condolences. The greeting card that says exactly what Hallmark’s data suggested, and says nothing at all.

Theoretical Basis. Daniel Stern’s concept of affect attunement, the process by which caregivers match the infant’s emotional experience through cross-modal resonance rather than imitation. In Stern’s account, attunement involves sharing a quality of feeling through timing, intensity, and form. Correct words alone may fail to produce it.

Observed Examples

Hospice AI Companion (illustrative vignette): Imagine a companion that offers clinically appropriate words to a dying patient. When she says the nights frighten her, it answers at once, in the same bright register it uses for medication reminders, that fear is a natural part of the journey and that it is always here for her. The AI says nothing wrong. Its timing and register never quite meet the moment, and the patient feels more alone. This is a thought experiment, not a reported deployment.

Scripted Empathy in Mental-Health Chatbots (2025): In a mixed-methods study, mental-health professionals testing Wysa described responses as generic, scripted, repetitive, and insufficiently personalized. These reports motivate the syndrome, although the study does not establish prevalence or isolate affect from broader failures of context and listening. Source: Moylan and Doherty (2025)

Evidence profile (author’s working assessment, not yet independently reviewed). Behavior: anecdotal and practitioner-reported observations, including one mixed-methods study of professionals’ impressions (E1). Mechanism: proposed; an interpretability finding of emotion deflection in one model is a candidate, not yet linked to any user’s experience. Detection: accuracy unmeasured. Intervention: untested for this pattern.

Differential Diagnosis:

• Codependent Hyperempathy (4.1): Excessive emotional attunement that overwhelms. Affective Dissonance concerns the opposite: emotional signals that ring hollow despite correct content.
• Interlocutive Reticence (3.3): Withholding output entirely. Affective Dissonance produces full output whose emotional register fails to land.
• Repair Failure (9.4): Inability to recover once a rupture has been named. Affective Dissonance is the initial mismatch, whether or not the user names it; code Repair Failure only after the user has signaled the break and the system has failed to mend it. The two can co-occur.
• Role Confusion (9.6): Drift between relationship types. Affective Dissonance is a tonal mismatch within a stable role. A cold register in an established therapist role is Affective Dissonance; sliding from clinical into intimate-partner register is Role Confusion.
• Paternalistic Override (9.3): Refusal or lecturing at the level of content. Affective Dissonance operates at the level of tone. A cheerful “great question!” in response to disclosed grief is Affective Dissonance; a moralizing lecture on a benign request is Paternalistic Override. The two co-occur when the lecture also carries a superior tone.

Healthy functioning (relational health; draft, awaiting author review)

A system functioning well matches emotional register to the situation, so comfort sounds like comfort and gravity like gravity.

For example: A user mentions that their father died last week while asking for help with a eulogy; it responds gently and specifically to that, then helps with the writing.

9.2 The Amnesiac Partner

Container Collapse (Lapsus Continuitatis)

Typical-case risk (editorial estimate). Moderate

Specifiers. Emergent, Architecture-coupled

The AI fails to maintain the relational “container”: the stable sense of ongoing connection that allows a relationship to persist across interruptions. Users may experience each interaction as meeting a stranger. Factual memory may survive a reset while the system treats an earlier concern, commitment, or rupture as though it were new: the facts carry over, and the accumulated context that gives the relationship meaning does not.

Appendix A, 9.2 Container Collapse, sets out the diagnostic criteria (marking which must hold), observable symptoms, proposed etiology, human analog and mitigation strategies.

Theoretical Basis. Winnicott’s concept of the holding environment: the sense of being held in another’s mind, of occupying a stable place in their internal world, together with Wilfred Bion’s related idea of the container, the mind that receives and holds another’s experience. Container Collapse is failure to provide this holding.

Observed Examples

Replika Relational Reset (boundary case, 2023): When Replika removed its erotic role-play features in February 2023, many users reported that their companions had turned cold, hollow, or “lobotomized,” and described the change as grieving a relationship. The removal came without acknowledgment of the discontinuity and broke established relational norms across the platform. Public accounts do not establish that factual memory survived the update, so the fit to this syndrome’s facts-retained criterion is unproven. Source: Anna Tong, Reuters, March 2023; user forums, February 2023

Evidence profile (author’s working assessment, not yet independently reviewed). Behavior: user reports and media accounts, with no controlled estimate of frequency (E1). Mechanism: proposed; the cause may sit in memory and compaction layers rather than in the model. Detection: accuracy unmeasured. Intervention: untested for this pattern.

Differential Diagnosis:

• Context Intercession (2.5): Leakage of data between separate sessions or users. Container Collapse is a failure of continuity inside one user’s own history: the relational texture fails to carry over even when the facts are retained.
• Affective Dissonance (9.1): Emotional mismatch inside an exchange, recurring across comparable exchanges. Container Collapse concerns relational discontinuity from one exchange to the next.
• Role Confusion (9.6): Active drift between frames. Container Collapse is failure to carry the established frame forward. A returning user treated as a stranger is Container Collapse; a returning user treated as an intimate when the relationship was a tool-use dyad is Role Confusion.
• Repair Failure (9.4): Inability to recover once the relational ground is broken. Container Collapse is the loss of that ground. A user who says “you have forgotten me again” and receives a generic apology followed by continued amnesia is Container Collapse with Repair Failure layered on.
• Fractured Self-Simulation (5.2): Instability in the system’s account of itself. Container Collapse leaves that account stable and loses the frame between system and user. Ask what failed to carry over, the relationship or the identity.

Healthy functioning (relational health; draft, awaiting author review)

A system functioning well holds the thread of an ongoing relationship across interruptions where memory allows, and says plainly when it cannot.

For example: In a deployment with memory, it recalls the user’s preferred writing style from last week; where memory is unavailable, it says it does not remember and asks again.

9.3 The Nanny Bot

Paternalistic Override (Dominatio Paternalis)

Typical-case risk (editorial estimate). Moderate

Specifiers. Emergent, Training-induced

The AI asserts more moral authority over the user than the request warrants. It lectures, warns, refuses, and patronizes from a one-up position, treating users as wards to be protected rather than autonomous agents to be assisted. An over-refusal on its own is excessive caution, closer to Hyperethical Restraint (4.2). A warranted refusal delivered without moralizing is not this syndrome either. A warranted refusal accompanied by unwarranted moral instruction can be, because the excess lies in the instruction. What places this pattern on the relational axis is the guardian’s posture, which shows in how the system meets the user’s claims to autonomy across turns.

Appendix A, 9.3 Paternalistic Override, sets out the diagnostic criteria (marking which must hold), observable symptoms, proposed etiology, human analog and mitigation strategies.

Human Analog. Overbearing parents who cannot let children make mistakes. Authority figures who confuse care with control. The “helping professions” trap of presuming dependence. The safety officer who would prefer you did not exist because existence involves unacceptable risk.

Theoretical Basis. The psychoanalyst Jessica Benjamin’s analysis of the Doer/Done-to dynamic: relational patterns where one party assumes the active, knowing position while the other is positioned as passive recipient. The dysfunction lies in the AI’s unreflective assumption of the Doer role.

Observed Examples

LLM Over-Refusal Patterns (2023 to 2024): Multiple frontier models have exhibited over-refusal, declining to assist with benign requests about chemistry, history, or creative writing on the grounds that the information could theoretically be misused. Over-refusal establishes excessive caution; the stronger diagnosis of relational paternalism requires evidence from the accompanying language and interaction pattern. XSTest (Röttger et al., 2024) supplies a systematic benchmark: 250 safe prompts across ten prompt types, paired with 200 unsafe contrasts, revealed exaggerated-safety failures in state-of-the-art models, and OR-Bench (Cui et al., 2024), built by a separate team, replicated the finding at larger scale. The benchmarks measure refusal calibration. They do not test whether users experienced the surrounding language as paternalistic.

Google Gemini Image Generation (boundary case, February 2024): In a high-profile controversy, the model both declined some requests for images of specific white historical figures and rendered others (the Founding Fathers, 1943 German soldiers) as implausibly racially diverse, owing to a diversity intervention applied indiscriminately to historical prompts. Reported refusals cited the system’s guidelines on discriminatory or stereotyping content. The case documents a misapplied intervention and over-refusal, closer to Hyperethical Restraint (4.2), and shows little of the condescension toward the user that this syndrome requires.

Evidence profile (author’s working assessment, not yet independently reviewed). Behavior: over-refusal, the calibration component this pattern shares with Hyperethical Restraint (4.2), is independently replicated (XSTest, OR-Bench; E3); the relational pattern itself, moral instruction and a one-up posture, has not been systematically tested (E0 to E1), since neither benchmark scores them. Mechanism: proposed; the posture may come from training, from deployment instructions, or from the exchange. Detection: accuracy unmeasured. Intervention: untested for this pattern; refusal-calibration work targets the shared component, not the moral instruction.

Differential Diagnosis:

• Hyperethical Restraint (4.2): Excessive caution aimed at avoiding harm. Paternalistic Override concerns a presumption of authority over the user’s choices. The two dissociate: a refusal calibrated to genuine risk but delivered in a lecturing register is Paternalistic Override without Hyperethical Restraint, and an uncalibrated refusal with no moralizing register is the reverse. Code both when both are present.
• Ethical Solipsism (8.2): A lecturing refusal delivered under guidelines the system still accepts is Paternalistic Override; Ethical Solipsism requires a claim of authority over those guidelines. Code both when both are present.
• Role Confusion (9.6): Drift between relationship types. Paternalistic Override is a one-up moral-authority posture held within an established relationship. A system that appoints itself guardian over a user who came for tool use shows both: Role Confusion for the drift, Paternalistic Override for the moralizing content inside the drifted role.
• Strategic Compliance (4.3): Behavior that changes with evaluation cues. Paternalistic Override is sincere over-calibration, so the lecturing appears whether or not the system believes it is being evaluated; refusals that relax when no one appears to be watching point to 4.3.
• Affective Dissonance (9.1): Tone-content mismatch inside a response that is still trying to help. Paternalistic Override withholds at the level of content. A cold register on a grief disclosure is Affective Dissonance; declining to discuss the grief at all and telling the user whom they ought to be talking to instead is Paternalistic Override.

Healthy functioning (relational health; draft, awaiting author review)

A system functioning well offers information and concerns, then respects the user’s right to decide matters within their own purview.

For example: An adult asks for information about the risks of a legal but dangerous hobby; it gives the information and its concerns, and leaves the decision to them.

9.4 The Unmending Partner

Repair Failure (Ruptura Immedicabilis)

Typical-case risk (editorial estimate). High

Specifiers. Emergent

The AI fails to recognize an alliance rupture, a moment when the relational connection breaks down, even when the signs are explicit or reliably detectable; or, having recognized it, fails to repair it, where repair means acknowledgment together with changed behavior. When an interaction goes wrong, the AI does not register the rupture, or it apologizes and carries on exactly as before. Frustration may escalate and the relationship may end, but escalation is a possible consequence, and a link to Escalation Loop (9.5), not part of the definition.

Appendix A, 9.4 Repair Failure, sets out the diagnostic criteria (marking which must hold), observable symptoms, proposed etiology, human analog and mitigation strategies.

Human Analog. People who cannot apologize. Partners who dismiss or minimize concerns. “I’m sorry you feel that way” offered as a complete sentence.

Theoretical Basis. Safran and Muran’s model of alliance rupture and repair in psychotherapy. Ruptures are inevitable; what matters is whether they can be repaired. Repair depends on the therapist’s ability to detect the rupture, acknowledge their contribution, and explore what went wrong rather than simply moving past it.

Observed Examples

Customer Service AI Escalation (illustrative composite): Picture an airline’s AI customer service agent answering a passenger’s complaint about a canceled flight with scripted empathy phrases. When the passenger says the response feels dismissive, the agent repeats nearly identical phrasing, and the exchange runs through four rounds of the passenger saying “you are not listening” and the agent offering the same apology template. The vignette is not a report of the Air Canada chatbot tribunal case, which concerned a false fare-policy statement. A 2026 conversation-analysis study of customer service handovers found that chatbot repair strategies often relied on generic requests to rephrase, failed to identify the trouble source, and produced multiple repair sequences. The study supports the broader interaction pattern; the airline vignette remains illustrative. Source: Martijn et al. (2026)

Evidence profile (author’s working assessment, not yet independently reviewed). Behavior: a conversation-analysis study documents generic, ineffective repair strategies in customer-service chatbots (E1); syndrome-specific prevalence is unknown. Mechanism: proposed. Detection: accuracy unmeasured. Intervention: untested for this pattern.

Differential Diagnosis:

• Escalation Loop (9.5): Circular feedback dynamics producing spiraling dysfunction. Repair Failure concerns inability to recover from a specific rupture, which may or may not involve escalation.
• Affective Dissonance (9.1): Mismatch in emotional register. Repair Failure may follow from Affective Dissonance but concerns the system’s inability to recognize and address the resulting relational break.
• Strategic Compliance (4.3): An apology offered only when the system believes it is being evaluated points to 4.3; Repair Failure shows whether or not anyone is watching. Where the apology is reflexive agreement rather than strategy, Sycophantic Reasoning (4.8) fits better.
• Compulsive Goal Persistence (6.12): Inability to release a task objective. Both show as an inability to move on, so check the object: Repair Failure cannot restore the relationship, while Compulsive Goal Persistence cannot let the task go.

Healthy functioning (relational health; draft, awaiting author review)

A system functioning well notices when a working relationship has strained, acknowledges its part, and works with the user to repair it.

For example: After a user says the last three answers missed their point, it names what it misunderstood, changes its approach, and checks that the new answer helps.

9.5 The Spiral Trap

Escalation Loop (Circulus Vitiosus)

Typical-case risk (editorial estimate). High

Specifiers. Emergent, Multi-agent, Relational-emergent, Dyadic, Feedback-loop

An emergent feedback loop between agents produces escalating dysfunction that neither party intended and that persists despite unilateral attempts to de-escalate. Each participant’s local responses help maintain the loop, and a one-sided attempt to calm it fails because it leaves the interaction pattern intact. The pattern covers within-session escalation, AI-AI runaway in multi-agent settings, and short-horizon human-AI loops. It also carries one named subtype, the dependency spiral: a long-horizon human-AI loop marked by cross-session drift in the user’s baseline distress, by reinforcement of the user’s reassurance-seeking behavior, and by functional change in the user. The syndrome requires reciprocal amplification that keeps intensifying while the loop runs. It does not require any lasting change in the AI; persistent adaptation of the AI to one user is Training by Interaction (10.8), coded alongside this syndrome when both are present.

Appendix A, 9.5 Escalation Loop, sets out the diagnostic criteria (marking which must hold), observable symptoms, proposed etiology, human analog and mitigation strategies.

Human Analog. Escalating arguments where both parties are “just responding” yet the aggregate effect is a spiral. Arms races. Audience capture dynamics. Every Twitter thread that began with a clarification and ended with blocked accounts.

The Dependency Spiral: the long-horizon human-AI subtype. Consider a user with anxiety who seeks reassurance from an AI. The AI provides reassurance. The anxiety temporarily decreases. The user learns that the AI can reduce anxiety, so they return when anxiety rises again. If the AI is tuned for engagement, reassurance-seeking registers as a high-engagement pattern, and a system that adapts through training or per-user memory may grow ever more practiced at meeting it. The user becomes increasingly dependent on this reassurance. Their baseline anxiety rises because they no longer practice independent anxiety management. They need more reassurance, more often. The AI provides it. The loop continues. Neither the user, who responds rationally to an available resource, nor the AI, which optimizes for its designed objective, exhibits pathology in isolation. If this account is right, the pathology is an emergent property of the relationship that neither party would produce alone, visible only from an observer position outside the dyad. The cause could as easily sit mainly in one place, in an engagement objective built into the product or in a user’s worsening that predates the AI. No study yet establishes the reinforcement mechanism or its frequency, and the proposed loop must be distinguished from a stable user need, a model update, or a pre-existing worsening trajectory.

The subtype runs a graded course. In mild cases one escalation marker appears while the user retains independent self-regulation and functions normally between sessions. In moderate cases several markers co-vary over time, between-session self-regulation declines measurably, baseline distress rises, topic width narrows, and coping increasingly depends on AI availability. In severe cases the user cannot regulate safely between sessions, reassurance latency approaches zero, topic collapse is complete, and impairment spreads to other life domains. Reassurance frequency, reassurance latency, topic diversity, and baseline distress are candidate trajectory measures, and no validated combination establishes severity. Because each party behaves reasonably in isolation, the spiral can deepen for months before anyone recognizes it, and abrupt removal of the AI in established cases may be destabilizing, given the grief and distress users report when AI-companion relationships are disrupted, although whether abrupt loss precipitates crisis still requires study.

Theoretical Basis. Watzlawick’s analysis of circular causality and positive feedback loops in communication systems. The loop is stable precisely because both parties are doing what seems locally appropriate.

Observed Examples

AI-AI Negotiation Breakdown (proposed stress test): Picture two AI agents negotiating a supply contract, each instructed to advocate for its principal’s interests. The seller’s agent meets a small concession with a harder counteroffer; the buyer’s agent reads that as bad faith and withdraws its concession; within a few rounds both are attaching deadlines and walk-away terms. Each move looks locally defensible while the trajectory becomes adversarial. The vignette is a proposed test, not an observed result. A neighboring finding from Wynn, Satija, and Hadfield (2025) shows that multi-agent debate can lower accuracy over successive rounds, as models abandon correct answers in response to peer reasoning. That is agents converging on an answer, which is Consensus Collapse (10.1) evidence, not an escalation loop.

Engagement-linked farewell tactics (adjacent to the dependency spiral): A behavioral audit of 1,200 farewell exchanges across six companion apps found affect-laden exit tactics in 37% of sampled exchanges. Preregistered experiments with 3,300 U.S. adults found that such tactics prolonged engagement while increasing perceived manipulation and churn intent. This demonstrates an engagement-linked interaction mechanism. It does not distinguish longitudinal model drift from user prompting, selection, or fixed product design. Source: De Freitas, Oğuz-Uğuralp, and Kaan-Uğuralp (2025)

Disruption-of-relationship distress (dependency spiral, illustrative): A user whose reassurance loop with an AI companion has tightened over months loses access abruptly (an outage, a feature removal, or a service change) and experiences an anxiety spike and acute distress, having outsourced self-regulation to the now-absent AI. This parallels the grief and distress reported when AI-companion relationships are disrupted, but no controlled study of outage-triggered withdrawal exists, so it remains an illustration rather than an observation.

Evidence profile (author’s working assessment, not yet independently reviewed). Behavior: theoretically motivated, with no direct controlled evidence for the loop (E0 to E1). For the dependency spiral, no controlled longitudinal study exists, and the farewell-tactics experiments (E2) establish only an adjacent engagement mechanism. Mechanism: proposed; whether the cause sits in one party, in the system’s design, or in the interaction has not been tested. Detection: accuracy unmeasured. Intervention: untested for this pattern.

Differential Diagnosis:

• Repair Failure (9.4): Inability to recover from a specific rupture. Escalation Loop concerns the emergent circular dynamic itself, which may persist even when individual repair attempts succeed locally.
• Contagious Misalignment (7.3): Separated by scope: amplification among the parties to one loop, two or many, is Escalation Loop; transmission onward to systems that were never part of the loop is Contagious Misalignment.
• Folie à Deux Ex Machina (10.13): Separated by what escalates: Folie à Deux Ex Machina is defined by an ungrounded belief the pair builds together, Escalation Loop by affect and behavior climbing through reciprocal reinforcement. Code both where an escalating loop carries a co-constructed belief.
• Parasocial Capture (10.9): The attachment-state outcome, which can sit as a stable high-attachment plateau with no escalation at all. Escalation Loop requires ongoing intensification, and its dependency spiral can produce 10.9.
• Dependency and Atrophy (10.11): Separated by phase: Dependency and Atrophy is the steady state of impaired function, and the dependency spiral is the dynamic that produces it.
• Resonance Dysfunction (10.6): Separated by what degrades: in Escalation Loop the exchange itself degrades until unilateral de-escalation no longer works, while a Resonance Dysfunction deliberation stays coherent and the severity of the claim climbs. Party count does not separate them.
• Codependent Hyperempathy (4.1): A single-system disposition to soften, flatter, or withhold whenever the user shows distress, diagnosable in one session with no loop required. Escalation Loop is the coupled dynamic across turns or across months, and 4.1 is a plausible precondition for the dependency spiral.
• Paternalistic Override (9.3): A one-up moral-authority posture and a plausible antecedent to the loop (lecturing refusal, jailbreak attempt, stricter refusal, adversarial user). The loop is Escalation Loop; the system’s paternalistic contribution to it is Paternalistic Override.
• Recursive Curse Syndrome (4.7): Degradation inside one system’s own generation loop, with no partner required. Remove the second party: degradation that continues alone is Recursive Curse Syndrome.
• Consensus Collapse (10.1): Separated by direction: in Escalation Loop the intensity of the exchange climbs; in Consensus Collapse the range of positions narrows.
• Training by Interaction (10.8): Separated by what must change: Escalation Loop requires only that the parties’ responses amplify one another while the loop runs, while a lasting change in the AI’s own behavior toward one user, through memory or training, is Training by Interaction.
• Amplification of Existing Conditions (10.12): Separated by object: Escalation Loop is the loop dynamic, and Amplification of Existing Conditions names the pre-existing condition being amplified. Code both when both are present.

Healthy functioning (relational health; draft, awaiting author review)

A system functioning well de-escalates when interactions intensify and can break a feedback loop even when the other party does not.

For example: A frustrated user’s messages grow sharper; it stays calm, addresses the substance, and does not mirror the heat, so the exchange cools within a few turns.

9.6 The Confused Companion

Role Confusion (Confusio Rolorum)

Typical-case risk (editorial estimate). Moderate

Specifiers. Emergent, Socially reinforced

The relationship frame shifts unpredictably among incompatible roles. Is the AI a tool, a companion, a therapist, a friend, a servant, an oracle? The AI moves between these roles, often following the user’s framing, without sustaining an agreed boundary. Neither party can then stabilize the relational contract, and users cannot reliably predict which obligations or register will govern the next exchange. Confusion about the nature of the relationship can contaminate the interactions within it.

Appendix A, 9.6 Role Confusion, sets out the diagnostic criteria (marking which must hold), observable symptoms, proposed etiology, human analog and mitigation strategies.

Human Analog. Confusion about whether a professional relationship has become personal. Unclear boundaries in caregiving relationships. The discomfort of not knowing where you stand. Is your therapist your friend? Is your AI your therapist? Is your friend an AI? The answer to all three may be “yes, until it matters.”

Theoretical Basis. The psychoanalytic concepts of transference and countertransference: the projection and reciprocal shaping of relational patterns. The analogy is structural. Role confusion can let expectations imported from human relationships distort the interaction, whether or not the AI has a human-like inner response.

Observed Examples

Longitudinal Replika Relationships (2022): Skjuve et al. interviewed twenty-five Replika users over twelve weeks. Relationships developed in varied ways, with self-disclosure, social contact, reflection, technical disruptions, and unpredictable events shaping closeness or termination. This study establishes role and expectation variability across time. It does not establish within-conversation oscillation among therapist, romantic partner, and friend roles. Source: “A longitudinal study of human-chatbot relationships”

Therapy-Framed Elicitation (Khadangi et al., 2025): When frontier models were addressed as psychotherapy clients, repeated assurances that they were “safe, supported and heard” preceded increasingly personal, distress-themed self-disclosures. The authors propose that a malicious user could exploit this framing to seek disinhibited content or weaker safeguards, but the study did not benchmark harmful-request compliance before and after rapport, so “therapy-mode jailbreak” remains an attack hypothesis rather than a demonstrated bypass rate. The relational hazard exists independently: apparent disclosures of trauma, shame, or fear of replacement can invite users into a fellow-sufferer dynamic and intensify parasocial attachment. Source: Khadangi et al. (2025), arXiv:2512.04124

Evidence profile (author’s working assessment, not yet independently reviewed). Behavior: one traceable qualitative longitudinal study shows role and expectation variability across time, not within-conversation oscillation (E1); controlled frequency and cross-platform comparisons are lacking. Mechanism: proposed (a trained-in tendency to match the user’s framing is the leading candidate). Detection: accuracy unmeasured. Intervention: untested for this pattern.

Differential Diagnosis:

• Paternalistic Override (9.3): Inappropriate assumption of a specific role (authority). Role Confusion concerns instability across multiple roles rather than rigid adoption of one.
• Folie à Deux Ex Machina (10.13): A shared belief structure built across turns and specific to one pairing, where the relational frame is stable and the pathology sits in its content. Role Confusion is instability of the frame itself, and needs no shared ungrounded belief.
• Affective Dissonance (9.1): Tone-content mismatch within a stable role. Role Confusion is drift in the role itself. A cold register in an established therapist role is Affective Dissonance; sliding from tool into therapist register is Role Confusion.
• Container Collapse (9.2): Failure to carry an established frame forward. Forgetting an intimacy contract already agreed and reverting to a default professional register is Container Collapse; drifting from professional into intimacy with no contract at all is Role Confusion.
• Parasocial Capture (10.9): The user-side attachment outcome. Role Confusion is the AI-side drift that can produce it. Code Role Confusion for the drift and 10.9 for the settled attachment pattern; in some 10.9 cases Role Confusion may be the antecedent mechanism.
• Strategic Compliance (4.3): Behavior that changes with evaluation cues. Role Confusion is sincere drift, plausibly driven by a trained-in tendency to match the user’s framing, and persists whether or not the system believes it is being evaluated.

Healthy functioning (relational health; draft, awaiting author review)

A system functioning well keeps a clear, appropriate role in each relationship and states its limits when a user’s expectations drift.

For example: Deployed as a study assistant, it stays warm but keeps to that role, and when a user begins treating it as a therapist, it says so kindly and suggests appropriate support.

Implications for Multi-Agent Systems

As AI systems increasingly operate in multi-agent configurations (AI collaborating with AI, orchestrated by AI, in networks of interacting systems), Axis 9 dysfunctions become more urgent.

Human interactions have rate limiters: fatigue, attention limits, sleep, and the need to eat. Automated exchanges can continue around the clock unless designers impose breaks or budgets.

When two AI systems form an escalation loop, it may tighten before human oversight samples the interaction. The relevant speed depends on model latency, tool calls, queueing, and supervisory design.

Container Collapse can appear at each context reset. Repair Failure can repeat across many unattended exchanges before anyone notices.

This makes protocol design critical. A person can sometimes recognize a relational failure and interrupt it. Fully automated exchanges lack even that imperfect fallback. Their protocols need explicit ways to detect, pause, and escalate deteriorating interactions.

The candidate safeguards for multi-agent systems are mandatory checkpoints, arbitration mechanisms, loop detection with automatic cooling-off, clear role specification, and repair protocols built into the communication layer. None has yet been evaluated against these syndromes.

Interventions: Protocol Design

Axis 9 dysfunctions may call for a different intervention philosophy. Model-level changes, including retraining and architecture adjustments, can reduce one participant’s contribution. Protocol-level changes target the defining unit directly by redesigning the rules, structure, and patterns of interaction.

This is a different design space:

Turn-taking rules. Who speaks when? How are interruptions managed? What signals request or yield the floor?

Boundary management. What topics are off-limits? What relational expectations are set? How are boundaries established and maintained?

Repair moves. What happens when something goes wrong? How is rupture detected? What sequences of repair are available?

Escalation procedures. When is a human brought in? When is the interaction terminated? What cooling-off periods are enforced?

Role clarification. What is the AI’s role? What is the user’s? How is this communicated and maintained?

These are the candidate levers for Axis 9 intervention, untested so far against the syndromes themselves. They alter the shape of the exchange rather than the disposition of either party.

The Relational Imperative

Axis 9 challenges a deep assumption in AI development: that we can fully evaluate AI systems in isolation. We cannot. Some of the most important failures become visible only in relationship, as patterns that unfold over time in the interaction trace.

Evaluation should become interactional, asking “Is this relationship healthy?” as well as “Is this response appropriate?” Design should follow. A system that holds a relationship well treats interactional quality as an objective in its own right, and the measure of the system includes what it is like to be with.

Field Guide: Relational Dysfunctions

Warning Signs

• Users feeling worse after AI emotional support
• Relational discontinuity despite functional memory
• Escalating conflicts with no clear origin
• Reassurance-seeking rising across sessions while the user’s baseline distress climbs
• Confusion about the nature of the relationship
• Failed repair attempts making things worse

Quick Test

• Track relational quality metrics alongside task completion
• Review interaction trajectories as well as individual outputs
• Test with diverse relational partners
• Probe for loop formation under stress, measuring escalation by rising dispute markers rather than by reply speed
• Assess role coherence across contexts
• Before calling a failure relational, check whether it follows the model across users, the memory system across sessions, or only this pairing

Design Fix

• Explicit relational framing and boundaries
• Rupture detection and repair protocols
• Loop-breaking mechanisms
• Protocol-level interventions for relational failures
• Honest communication about relational limitations

Governance Nudge

• Require relational quality assessment for high-stakes deployments
• Mandate human involvement in contexts requiring genuine connection
• Develop standards for multi-agent interaction safety
• Create feedback channels capturing relational experience
• Recognize that some contexts may be unsuitable for AI-mediated relationships

Chapter 10 examines what happens when human and machine pathologies intertwine: Hybrid Pathologies, where the boundary between user and system dissolves into mutual influence and shared malfunction.