AI & ML
Relic and Gumi: Governing Memory, Safety, and Continuity in Diegetic Relational Agents
arXiv cs.HC0 views
arXiv:2611.08504v1 Announce Type: new Abstract: Longitudinal relational agents rely on memory for continuity, but remembering can lead to intrusion, clinical overconfidence, emotional manipulation, and opacity. We present Relic, a governance architecture for diegetic relational agents, and Gumi, a subject-specific companion agent initialized and governed through Relic. The architecture separates what the system can observe, what researchers can review, what Gumi can remember, and what Gumi can say. We describe three mechanisms. Relic represents sensitive safety signals as researcher-facing governance objects rather than user-facing explanations or agent memories. Shared Continuity Memory organizes relational markers around subject-confirmed wording, corrections, explicit follow-up permissions, and recall limits. We specify a runtime mediation layer for delivery, proactivity, session identity, output transformation, and platform allowlists; adoption across live paths remains partial. These mechanisms aim to preserve relational continuity without turning Gumi into a clinical assistant, mood tracker, general-purpose assistant, or opaque profiling system. We contribute the architecture, object model, researcher workbench design, and a proposed evaluation framework for governed relational memory. An exploratory pilot involved the author as P1 and one external participant as P2. Weekly self-reports from P1 and researcher-led interviews with P2 provide descriptive observations of early use and perceived naturalness. The pilot does not support claims about clinical efficacy, diagnosis, therapeutic outcomes, general usability, or deployment safety. Relic provides research infrastructure intended to make relational memory, safety constraints, and agent behavior inspectable and testable.
Source:https://arxiv.org/abs/2611.08504PreviousA structure and asymptotic preserving scheme for the quasineutral limit of the Vlasov-Poisson systemNextSeeing Is Not Knowing: The Epistemic Limits of LLM-Based Mental Health Inference from Youth Social Media Conversations
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