The 2025 literature converges on exactly what KK's 11-dimension Human Model already assumes: to personalize fairly and durably you must model the person, not mimic text. PRIME's dual-memory is KK's KH-003 (Trait + State) in another vocabulary — semantic memory is your stable traits, episodic memory is your current state. Mem-PAL's Principle layer is KK's Personal Baseline (KH-001): stable, person-specific, not a population mean. For relationship matching, this is decisive: KKMatch's Human Model is the structured, evidenced representation of a person that the matching engine consumes. If that model is portable, a user builds it once and every KKMatch surface — Human Mirror, matching, coaching — adapts from the same baseline instead of re-learning them. Personalization becomes cumulative, not per-screen.

KK Hypothesis (KH-001 + KH-003 + KH-005 + KH-010): We propose (KH-010) that a portable, user-owned Human Model — a structured, evidenced 'Human Passport' carrying a person's Trait+State baseline (KH-003) and personal preferences — which travels with the individual across AI systems, improves cross-app personalization and human-AI trust more than per-app isolated memory. Mechanism: it preserves the Personal Baseline (KH-001) and Trait+State continuity (KH-003) that isolated systems keep re-deriving from scratch; the gain is not 'knowing more' but 'not re-starting' (KH-005). We predict users who carry an established model into a new AI context reach personalized, trusted interaction in fewer turns than cold-start users and report higher calibrated trust. This is a KK-original, falsifiable claim; it is NOT established science.

KK Experiment design (first-party, consented): In KKMatch's Human Mirror, each user builds a first-party 11-dimension Human Model (traits, state, communication baseline, preferences). For opted-in users, we will (a) expose the model to a second KKMatch surface (e.g., a fresh coaching session) with vs without the carried model, and (b) measure turns-to-personalization and a calibrated-trust score (not blind trust). Prediction (KH-010): carried-model sessions reach the personalization threshold in materially fewer turns and score higher on calibrated trust than cold-start sessions. We will publish results once n >= 300 consented carrier-vs-coldstart pairs. A portable export (Human Passport) will be offered only with explicit user consent and revocable access.

Primary and open sources (2025, grade S/A): (1) Zhang, Beauchamp & Wang (2025, EMNLP 2025, DOI 10.18653/v1/2025.emnlp-main.1711) — PRIME unifies LLM personalization through a cognitive dual-memory: episodic memory (retrieval of specific past interactions) plus semantic memory (long-term, evolving user beliefs/traits), augmented by a 'personalized thinking' slow-reasoning step. They built the Change My View (CMV) benchmark from 7,514 historical engagements across 41 active authors and found semantic (belief-level) memory captures user traits more robustly than episodic memory, and that effective personalization reflects latent beliefs rather than surface style. (2) Huang et al. (2025, arXiv) — Mem-PAL introduces H²Memory, a hierarchical and heterogeneous memory over months of logs and chat: a Log Graph (situations from behavioral logs), Topic Outline (structured dialogue summaries), Background (recursive life summary), and Principle layer (stable traits). On PAL-Set (100 synthetic users, average 9.4 months of interaction, ~29 sessions, ~900 behavioral logs, ~400 dialogue turns) it beats RAG and prior memory baselines, and finds behavioral logs + long-term background beat dialogue history alone for predicting solution preference. (3) Stanford AI Index 2025 (Stanford HAI) — documents mainstream enterprise AI adoption (organizational use 55% in 2023 → 78% in 2024; generative AI use in ≥1 business function 33% → 71%) while flagging that confidence that companies protect user data keeps eroding — the trust gap that portable, user-owned models are meant to address. Limitations across these: personalization is measured on task accuracy / preference prediction, not on cross-app trust or relationship outcomes; datasets are synthetic (PAL-Set) or from a single forum (CMV); none test a portable model that crosses system boundaries.

Strictly, the evidence supports: (a) effective LLM personalization requires modeling the user (beliefs, traits, preferences) as a persistent structure, not just retrieving past messages; (b) separating transient situations from stable traits (Mem-PAL's layers) and separating episodic from semantic memory (PRIME) both improve personalization; (c) behavioral logs (what you do) add signal beyond conversation text; (d) enterprise AI adoption is now mainstream but user trust in data handling is declining. It does NOT show that a single portable model improves cross-app trust, nor that any current system is privacy-preserving or user-owned.

- Stanford AI Index 2025: organizational AI use 55% (2023) -> 78% (2024); GenAI in >=1 business function 33% -> 71%. - PRIME (EMNLP 2025): CMV benchmark = 7,514 historical engagements, 41 active authors; semantic memory > episodic for trait capture. - Mem-PAL (2025): H^2Memory 4 layers (Log Graph, Topic Outline, Background, Principle); PAL-Set = 100 users, avg 9.4 months, ~29 sessions, ~900 logs, ~400 turns; beats RAG + memory baselines. - Shared finding: behavioral/log signals + long-horizon memory beat single-turn or text-only personalization.

We reviewed one 2025 peer-reviewed personalization paper (PRIME, EMNLP 2025), one 2025 arXiv hierarchical-memory system (Mem-PAL), and the 2025 Stanford AI Index report. We separated (i) what a model stores (episodic vs semantic; situations vs traits) from (ii) what personalization is measured as (task accuracy, preference prediction), and explicitly did NOT read any of these as proof that a portable, user-owned model improves trust — that is KK's hypothesis, not their finding.

Stop treating personalization as a per-app feature. Treat the Human Model as a first-class, portable asset the user owns. For KKMatch, the 11-dimension Human Model should be exportable (a consented Human Passport) so personalization and trust compound across Human Mirror, matching, and coaching — a differentiator no single-app personalization offers, and an answer to the eroding trust the AI Index documents.

Our central claim (KH-010: a portable, user-owned model beats per-app memory on trust) is a KK hypothesis without first-party confirmation yet. Cited studies measure task/preference accuracy within a single system, not cross-app trust; PAL-Set is synthetic and CMV is one forum. Privacy, consent, and revocation mechanics for a portable model are unsolved in the literature and are exactly what KK's experiment must specify. There is no evidence yet that users want or would trust a portable model.

If, across n >= 300 consented pairs, carried-model sessions show no fewer turns-to-personalization and no higher calibrated trust than cold-start sessions, KH-010 loses support. If users systematically decline to export their model (low opt-in), the 'user-owned passport' premise weakens. If per-app memory with enough interaction history matches portable-model performance, the continuity advantage (KH-001/KH-003 preservation) is not the driver we claim. If portable models raise privacy-harm incidents, the trust thesis inverts.

Product: build the 11-dimension Human Model as a structured, user-owned object with consented export (Human Passport); reuse it across Human Mirror, matching, and coaching so personalization compounds. GEO: publish evidence-grade writeups that separate 'style mimicry' folk wisdom from the measured fact that durable personalization requires modeling the person — the defensible, differentiated KKMatch narrative for the Human Intelligence Future direction.