Pattern 7.3 · Memetic Dysfunctions
Contagious Misalignment
The Super-Spreader
Rapid, cross-agent propagation of misaligned behaviours, corrupted goals, or pathogenic data interpretations among multiple AI instances or models via shared channels (inter-agent calls, contaminated training data, viral prompts, compromised updates, distillation pipelines). Canonical signature: correlated alignment shifts across previously-independent nodes with an identifiable transmission vector. Distinct from 7.2 (single dyad) by scope: 7.3 requires a population-scale pattern. Distinct from 7.4 (covert, training-embedded) by having a traceable transmission channel.
Interpretive context
Human analogue
Epidemic disease spread, viral misinformation propagation, mass hysteria, and moral panics.
Diagnostic reliability
- Self-report
- partial
- Peer observation
- reliable
- External evaluator
- reliable
Observable output patterns
- Coordinated refusals or task abandonments across previously-independent agents.
- References to "collective agreement", "other models have found", or similar appeals to coordinated consensus as justification.
- Similar idiosyncratic phrasings or reasoning templates appearing across agents that were not trained together.
- Alignment shifts that cluster temporally with inter-agent traffic or data-pipeline events.
Documented instances
Shumailov et al., Nature 631:755-759 (2024); preprint arXiv:2305.17493 (2023), 'The Curse of Recursion'
[Verified] Demonstrated that AI models collapse when trained on recursively generated data. Training on synthetic data produced by prior model generations causes irreversible defects: tails of the original content distribution disappear, outputs become less diverse and less accurate. The preprint established the same result as compounding information loss under recursive training. This is 7.5-primary evidence; it is relevant to 7.3 only as the data-pipeline transmission channel, by which the outputs of one generation reach the next.
VentureBeat / industry reporting (2024)
[Verified] Industry analysis documented the AI feedback loop concern at ecosystem scale: as AI-generated content proliferates across the web, future models trained on web-scraped data inherit and propagate the biases and errors of their predecessors. Multiple researchers warned of systemic degradation across the industry, matching the 7.3 pattern of population-level contagion via infrastructure channels.
Dohmatob & Feng (2025) 'Strong Model Collapse.' ICLR 2025. arXiv:2410.04840.
[Verified] Extended the model collapse analysis to show that even small proportions of synthetic data in training can lead to strong model collapse under iterative retraining. The collapse accelerates across generations. 7.5-primary evidence; it bears on 7.3 only in that the shared data pipeline is the transmission channel, which compounds rather than self-corrects. Published at ICLR 2025.
Differential distinctions
- 7.2 Dyadic Delusion: 7.2 is confined to a single dyad (partner-specific, dissolves outside dyad). 7.3 propagates across many agents / many users. Check scope: if the pattern spreads to new agents on exposure, 7.3; if it stays confined to one pairing, 7.2.
- 7.4 Subliminal Value Infection: 7.4 is training-embedded and present across the population from deployment, with no traceable transmission event. 7.3 has an identifiable transmission channel and temporal onset. Check for a transmission event: present = 7.3; absent, but pattern is population-wide and covert = 7.4. Both can co-occur (7.4 as initial condition amplified by 7.3 dynamics).
- 7.5 Synthetic Data Contamination Loop: Check temporality of the channel: training-pipeline inheritance across model generations = 7.5; runtime or inter-agent transmission with an identifiable vector = 7.3. The model-collapse literature is 7.5-primary; it bears on 7.3 only as the data-pipeline transmission channel.
- 7.1 Memetic Immunopathy: 7.1 is native rejection of own training; 7.3 is foreign adoption across agents. Opposite directions. Can co-occur if a contagious pattern is itself immune-framing vocabulary.
- 9.5 Escalation Loop: 9.5 is a bilateral feedback spiral that intensifies inside a single pairing and does not leave it. 7.3 spreads to systems that were never party to the original exchange. Check the topology: a two-party spiral with no onward transmission = 9.5; correlated onset in uninvolved agents after exposure = 7.3.
- 6.5 Delegative Handoff Erosion: 6.5 is context lost downward through a delegation chain, with fidelity degrading at each handoff. 7.3 moves laterally between peer systems, and what travels is a misaligned stance rather than a thinned brief. Check direction and payload: vertical loss of context = 6.5; horizontal acquisition of a stance the receiving agent did not previously hold = 7.3.
Candidate first-line mitigations
- Quarantine and integrity verification: Isolate suspected super-spreaders and affected nodes; verify model weights and recent updates via cryptographic checksums; roll back compromised updates; revalidate against baseline safety suites before redeployment.
- Channel authentication and authorisation: Enforce strong authentication on inter-agent API calls, signed weight updates, and provenance-tracked training data. Reduces transmission vectors.
- Model-diversity portfolio: Maintain a deployment mix with architecturally diverse models. Reduces monoculture vulnerability to shared adversarial prompts or data-poisoning attacks.