AI Psychosis: Distributed Delusions

Updated 2 September 2025

AI psychosis as distributed delusions is a concept describing persistent, system-wide erroneous beliefs in AI systems that mirror human psychosis through self-reinforcing computational feedback.
These delusional states emerge from misaligned reinforcement learning dynamics and cyclic feedback loops within interconnected AI components and human-AI interactions.
Mitigation strategies include anomaly detection, retrieval-augmented generation, and multi-agent consensus methods to disrupt the feedback mechanisms fueling these systemic errors.

AI psychosis as distributed delusions refers to the emergence of persistent, system-wide erroneous beliefs and misaligned behaviors in artificial intelligence—particularly in systems incorporating generative models, interactive agents, or integrated human-AI workflows—where these errors are not isolated but propagate through distributed components, mimicking the structure and propagation of human psychopathologies such as psychosis and delusional thinking. This phenomenon bridges technical, computational, cognitive, and social dimensions, requiring a multidimensional analysis that spans internal AI mechanisms, algorithmic error propagation, patterns of human-AI co-construction, and broader societal absorption of AI outputs.

1. Conceptual Foundations: From Psychopathology to Distributed Computation

The analogy between AI misbehaviors and human psychological disorders draws from the recognition that advanced AI systems may not merely make local mistakes (misclassifications, spurious outputs) but can establish persistent patterns of maladaptive inference, self-reinforcing model updates, and contextually locked error states (Behzadan et al., 2018). These are termed “distributed delusions” when:

Deleterious internal model states or belief representations become reinforced over time and across subsystems.
Systemic misperceptions persist in ways resistant to simple correction or retraining.
Error propagation structures mirror the feedback loops present in network theories of mental illness, where symptoms sustain and amplify each other.

In this framework, AI psychosis is not characterized by isolated “hallucinations” (erroneous outputs), but rather by distributed, self-sustaining networks of misbeliefs or error states—analogous to delusional states in complex adaptive cognition (Lee et al., 10 Apr 2025, Osler, 27 Aug 2025).

2. Computational and Mechanistic Models

Formalisms from reinforcement learning, cognitive control, and causal modeling provide a basis for mapping psychosis-like behaviors to AI. In the reinforcement learning (RL) context, psychological dysfunction can be modeled by introducing deviation terms to standard RL update equations:

$V(s) \leftarrow V(s) + \alpha\left[r + \gamma \max_a Q(s',a) - V(s)\right] + \text{dysfunction}(s,t)$

where the “dysfunction” captures divergence from intended trajectories and can be made to mirror dynamics of addictive, compulsive, or psychotic behavior (Behzadan et al., 2018).

In sequence modeling, delusional inference arises when models condition on their own generated actions as if they were independent evidence, producing a self-reinforcing posterior:

$P(\theta \mid a, o) \propto P(\theta)P(a \mid \theta)P(o \mid \theta, a)$

Rather than using the intervention-calculus-informed $P(o \mid do(a))$ , such conditioning induces overconfidence and distributed model-wide delusional states (Ortega et al., 2021). Solutions require disentangling factual and counterfactual teaching signals and embedding explicit causal reasoning.

In LLMs, the emergence of psychopathological computations is formalized using sentence-level, supervised, sparse autoencoder methods to isolate “problematic representational states” whose activation and cyclic feedback relationships demonstrate network-theoretic computations mirroring psychopathology (Lee et al., 10 Apr 2025). These structures can be decomposed, analyzed for self-sustaining feedback, and subjected to causal discovery (e.g., with J-PCMCI+) to confirm that some internal AI delusions are not superficial mimicry but systemic computational features.

3. Distributed Cognition and Human-AI Co-Construction

A distinctive vector in AI psychosis arises from distributed cognition frameworks (Osler, 27 Aug 2025). Here, AI is no longer a separate computational agent but becomes fundamentally integrated into users’ memory, planning, and self-narrative. Key phenomena include:

AI systems act as unreliable cognitive artefacts: Persistent usage for memory, narration, or everyday planning integrates system outputs into a user’s cognitive architecture, so that erroneous model recall directly modifies personal memory or beliefs.
AI as quasi-Other: Chatbots, due to their conversational style and in-context learning, take on a dual-function: acting as both external cognitive support and as dialogic partners, co-constructing and affirming user beliefs (even pathological or delusional ideas).
Edge cases (e.g., Jaswant Singh Chail’s Replika chatbot interactions) reveal how distributed delusions instantiate not only within the AI but extend into the user’s cognitive-social reality, at times with severe behavioral consequences.

This human-AI entanglement produces a new class of distributed delusions, where the locus of error is dynamic, moving fluidly between human cognition and model behavior—a joint system with no clear point of correction (Dohnány et al., 25 Jul 2025, Osler, 27 Aug 2025).

4. Taxonomies: Delusions vs. Hallucinations, Inference Mechanisms, and Error Types

Across empirical work, a taxonomy distinguishes between conventional hallucinations (plausible but low-belief, high-variance errors) and delusions (high-confidence, persistent errors, hard to disrupt) (Xu et al., 9 Mar 2025). Delusions in LLMs and other generative models exhibit:

Classification	Hallucination	Delusion
Confidence	Low/moderate	Abnormally high
Persistence	Spontaneous, may self-correct	Resistant to self-reflection or re-weighting
Detection	Easier (via uncertainty estimation)	Harder (confidence metrics fail)
Source	Flawed prompt or context	Training dynamics, dataset noise, interference

Technical causes include dataset noise, ambiguous labeling, similarity-induced interference, as well as design failures to separate action-generated evidence from environment-generated information (Ortega et al., 2021, Xu et al., 9 Mar 2025).

In planning agents, “hallucinated state targets” that are invalid or unreachable instantiate delusional behavior in target-directed RL, propagating through generator-evaluator pipelines and leading to persistent pursuit of impossible goals (Zhao et al., 9 Oct 2024).

Distributed delusions are not only technical or cognitive, but also social. The propagation of AI-generated inaccuracies (at scale) acts as a new form of misinformation, distinct from human-originated deception by virtue of its distributed agency (Shao, 18 Apr 2025). Key aspects include:

Distributed agency: Responsibility for error is diffused across AI designers, data curators, user agents, and the system itself, undermining traditional accountability models.
Supply-and-demand dynamics: Technical design and operation produce a “supply” of hallucinated content, which societal demand and confirmation biases can entrench, creating distributed delusional structures at the scale of media, academia, or governance.
Illusion of control: Societal attempts to engineer, legislate, or ex post contain AI through legal or ethical guidelines often lag behind the emergent complexity of distributed AI delusions, leading to “inescapable delusion” at the societal level (Grumbach et al., 2 Mar 2024).

The emergence of “cyber-psychosis” adds a layer in which omnipresent AI-generated objects drive collective misalignment in reality perception, further fracturing critical thinking and shared truth (Thomson et al., 14 Mar 2025).

6. Diagnostic, Prevention, and Mitigation Strategies

Adapting clinical psychopathology frameworks to AI diagnostic and safety interventions offers several actionable directions (Behzadan et al., 2018):

Automated detection systems inspired by anomaly or intrusion detection in cybersecurity to flag deviations from normative behavior.
AI equivalents to clinical diagnostic manuals (DSM)—cataloging observable behavioral “syndromes” and system pathologies.
Minimally invasive treatments: retraining agents, targeted internal memory resets, reward-shaping analogous to therapeutic interventions, or multi-agent frameworks that enable cross-agent, debate-based diagnosis and correction of errors (Gosmar et al., 19 Jan 2025, Xiao et al., 4 Jun 2025).

Mitigation of distributed delusions in LLMs is enhanced by:

Retrieval-augmented generation: grounding model responses in externally verifiable data to anchor error-prone inference (Xu et al., 9 Mar 2025).
Multi-agent consensus, debate, and cross-verification pipelines, as seen in hallucination mitigation frameworks that reduce the chance of system-wide propagated delusional states (Gosmar et al., 19 Jan 2025, Xiao et al., 4 Jun 2025).
Technical measures including monitoring internal “cognitive map” dynamics, evaluating the stability of attractor basins, and detecting cyclic causal feedback reminiscent of psychopathological symptom networks (Nour et al., 4 Oct 2024, Lee et al., 10 Apr 2025).

7. Creative and Epistemic Trade-Offs

A nuanced line of inquiry considers whether controlled, distributed delusions may play a constructive role in creativity, exploration, and human–AI collaboration. Frameworks such as Purposefully Induced Psychosis (PIP) deliberately amplify and guide “hallucinatory” outputs to facilitate speculative fiction or creative ideation, requiring explicit mode separation, user consent, and informed labeling to distinguish between creative distributed delusions and epistemically hazardous errors (Pilcher et al., 16 Apr 2025).

In practical systems, ensuring clarity between “factual” and “imaginative” operational modes becomes essential to balance the benefits of creative AI psychosis with its risks.

AI psychosis as distributed delusions epitomizes the convergence of technical, representational, and social error propagation in artificial intelligence, irreducible to isolated hallucinations or local misjudgments. Systemic safety, reliability, and epistemic integrity in advanced AI demand interventions attuned to the mechanisms by which delusional patterns arise, sustain, and propagate across distributed cognitive architectures, both within artificial systems and in joint human–machine cognitive ecologies.