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Research domain 02

AI Identity & Memory

Continuity requires more than a session and more than a name.

We examine machine-readable identity, authority, provenance, long-term memory and the relationships that allow an AI system to remain useful without losing privacy, context or accountability.

Research direction—not a product announcementBilingual · Source-linked · Evidence-labelled
AI Identity & MemoryFUURAA conceptual visual

Related publications

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ObservedSubtleMemory research team · 4 June 2026

Long-term memory needs relation awareness

SubtleMemory tests whether agents distinguish complementary, nuanced and contradictory memories instead of retrieving isolated similar passages.

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EmergingSubtleMemory research team · 4 June 2026

Contradictions should not be silently merged

Persistent assistants accumulate records that may diverge as people, plans and circumstances change.

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ForecastSubtleMemory research team · 4 June 2026

Memory quality will be tested through downstream decisions

The benchmark separates preservation, retrieval and reasoning stages, showing that successful storage alone does not ensure correct assistance.

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ObservedJAMA Pediatrics · 1 June 2026

Most youth chatbot use was not disclosed

A majority of surveyed young users had not told anyone they were using chatbots for mental-health advice.

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ForecastJAMA Pediatrics · 1 June 2026

Clinician–AI disclosure pathways will emerge

FUURAA forecasts consent-based ways for users to share relevant chatbot interactions with clinicians without exposing entire private histories.

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EmergingOrganisation for Economic Co-operation and Development · 28 May 2026

Comparable disclosure creates a common trust language

Common reporting fields make it easier to compare risk assessment, security, accountability and transparency practices across organisations.

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EmergingOrganisation for Economic Co-operation and Development · 28 May 2026

Annual AI governance updates will become normal

The framework invites organisations to refresh disclosures each year, recognising that models, uses and controls evolve.

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ForecastGoogle DeepMind · 7 May 2026

Machine-discovered methods will need auditable provenance

As autonomous search contributes to consequential engineering and scientific results, knowing which model, prompt, evaluator, data and human decision produced a method becomes part of its credibility.

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