The Foundation

Deep-Tech & Deep-Regulation, Not Just Another AI Wrapper

Introduction

This overview distills the deep-tech and deep-regulatory foundations of SEEYOU and explains why they are essential. It references 86 academic,

This overview distills the deep-tech and deep-regulatory foundations of SEEYOU and explains why they are essential. It references 86 academic, regulatory, policy, and factual sources. We outline the theoretical and regulatory basis for democratizing generative AI and demonstrate how SEEYOU delivers state-of-the-art, multi-expert performance while safeguarding personal data, ideas, and intent-and how it aligns with key global regulatory and policy frameworks. [1, 23, 29-31, 69, 72-73, 75]

The information presented builds on the cited public sources and reflects SEEYOU's academic rigor. SEEYOU's proprietary IP is the result of 3.5 years and more than 350,000 engineering hours, including tests across over 3,000 hybrid algorithms.

Abstract

No single model is best at everything, and the distribution of "best model" varies by task and operating point across time. Classical deployments forward the entire prompt, including identity, intent, and outputs, to one provider.

SEEYOU generalizes the web's index-then-rank paradigm from documents to providers: it indexes frontier and specialist models, fragments each request into skill-aligned subtasks. It orchestrates each fragment to its single highest-ranked compatible specialist (Top-1) under policy constraints.

Providers see only deterministically tokenized fragments. A proof layer emits per-fragment receipts, batches them by Merkle tree, and anchors the root to a public chain for verifiable inclusion, ensuring no metadata leakage. Evaluation separates capability (benchmarks) from operations (latency, throughput, proof overhead), with long context fidelity reported by RULER curves and reproducibility enforced via pinned containers and fixed seeds.

The design operationalizes decades of local expert and conditional compute research for production orchestration and recursion, optimizing multi-expert coherence while preserving provider-side blindness to the user's full query and identity. [1-5, 11-13, 21, 47]

Democratizing generative AI: SEEYOU makes the best model accessible to everyone for every part of every task; it protects people's privacy by never revealing the full question, identity, intent, or outputs; and it provides every new provider and model a neutral, universal path to market through the same interface-supporting public goals for broad access, trustworthy use, and open, contestable ecosystems. [69, 23, 29-31, 70]

Reasons for Existence

Origins (2022). In the summer of 2022, we predicted that generative AI would evolve similarly to derivatives in financial markets, where assembled exposures scale far beyond any single underlying. (In global markets, OTC derivatives notionals dwarf the cash markets they reference, illustrating how composition can outstrip any single substrate.) [48-49]

Capital geography. It was also evident that frontier-scale model funding and computing would concentrate in areas with the most developed venture ecosystems, primarily the United States and China, while the rest of the world would emphasize specialized models.

Public data indicate that the United States leads private AI investment by a significant margin, with China a distant second and Europe trailing far behind; this gap is even wider in generative AI. The implication is not "worse," but different: a rich surface of specialists that outperform on specific subtasks coexists with frontier providers that lead on others. [50]

Strategy. From judo, we borrow seiryokuzenyō, "maximum efficient use of energy", i.e., channel external force intelligently. From systems research, we adopt conditional compute and local experts (sparse gating, sharding, ensembling) to orchestrate work where it performs best [2-5, 21].

From Brin & Page [≃Google) we inherit the index-then-rank concept: "very little academic research [had] been done on [large-scale] search engines" until anatomy and evaluation made it practical at web scale [1]. We apply the same engineering logic to providers: index broadly, rank precisely, orchestrate Top-1 per fragment, recurse, and stitch. [1-5, 21, 47, 52]

Orchestrator value generation. Markets have repeatedly priced the value orchestrators (orchestration layers) generated at the scale of multiple investment-heavy incumbents. As of October 2025, Uber's market capitalization (~$193-197 B) was of comparable magnitude to the combined caps of Ford (~$47 B), General Motors (~$56 B), MercedesBenz Group (~$60 B), and Volkswagen (~$53 B)=~$216 B in total, i.e., within ~10-15%. [53-57].

Pragmatics. Public leaderboards typically place U.S./Chinese frontier models near the top while European/other models appear lower; e.g., in LMArena's WebDev Arena, Mistral Medium has appeared around the middle of the pack, underscoring heterogeneity rather than "good vs bad." The conclusion is structural: performance comes from orchestration, not allegiance to any single provider. [51]

Outcome. Building on the public research foundation below, we spent three and a half years and over 350,000 engineering hours developing SEEYOU: an orchestrator that indexes providers, fragments tasks, and orchestrates the Top-1 per fragment, optimizing performance and privacy, with verifiable execution and reproducible evaluation.

Main

1. Introduction

The rise of web search hinged less on a single algorithm than on systematizing indexing and ranking at scale, then exposing it through a simple interface [1]. Foundation model deployment today exhibits a similar gap: the literature on local experts, sparse gating, sharding, recursion, and ensembling is extensive [2-5, 21, 47], yet production forwards an entire prompt, identity, intent, and outputs to a single provider.

SEEYOU closes this gap with index → fragment → orchestrate (Top-1) → recurse → stitch, choosing exactly one best-fit specialist per fragment and hiding the whole from any single provider. [1-5, 21, 47]

Contributions.

A provider registry maintaining perskill rankings; a fragmenter mapping requests to skills; and an orchestrator selecting the Top-1 per fragment subject to policy.
A privacy layer (deterministic, keyed tokenization; no raw spans in the orchestration plane; edge-only rehydration).
A proof layer (receipts → Merkle root → publicchain anchoring; verifier packets with paths and transaction IDs).
A two-index evaluation doctrine: capability via GPQA, SWEbench Verified/SWEbench, MMLUPro, MMMU/MMMUPro, LongBench v2 with RULER, GSM8K, MATH, TruthfulQA, ARC; operations via TTFT, tokens/s, p50/p90/p99; all reproducible.
Governance alignment: ISO/IEC 27001:2022 & ISO/IEC 27701:2019 controls, SOC 2 Trust Services Criteria, ISO/IEC 42001, ISO/IEC 23894, NIST AI RMF, GDPR (Recital 26; Art. 4(5)), DORA overlays. [22-30]

Historical context.

Indexthenrank (Brin & Page ≃ Google) framed how to extract quality from abundance [1]. Object-oriented interfaces developed at Xerox PARC later diffused through the Mac and culminated in the iPhone, which aggregated many providers behind a single interface; the App Store operationalized modular, multi-provider composition [32-36].

SEEYOU applies the same orchestrator pattern to generative AI, indexing broadly, ranking precisely, and orchestrating selectively, while adding privacy by design and verifiable execution.

2. Related work

Local experts & conditional compute. Mixture of experts (MoE) formalizes orchestration among specialized subnetworks [2]; sparsely gated MoE and automatic sharding scale conditional compute [3, 4]. Ensembling can beat single large models at specific operating points [5]. These provide the substrate for runtime Top-1 selection against a ranked registry, even when the winner is a frontier FM. [2-5, 21]

Evaluation signals. Robust task families expose complementarity and failure modes: GPQA (graduatelevel, searchresistant) [6], SWEbench Verified (code repair; executable grading) [7, 8], MMLUPro (robust multitask) [11], MMMU/MMMUPro (multimodal expert tasks) [17, 18], LongBench v2 under RULER (effective longcontext threshold) [12, 13], GSM8K/MATH (math reasoning) [14, 15], TruthfulQA (misinformation) [16], and ARCAGI variants (generalization under data scarcity) [19, 20].

3. System overview

3.1 Registry and ranking

Let (P) be providers (frontier FMs, specialist FMs/tools); (S) skills. For skill (s\in S), we define a ranker (r_s: P\to\mathbb{R}) learned from telemetry (quality, latency, cost, guardrail incidents) and published evidence (benchmarks, reproducible runs). This generalizes the index, then ranks from documents to providers. [1]

3.2 Fragmentation

Given request (q) with metadata (m), a controller yields fragments (F={f_i}) and a skill map (\phi:f_i\mapsto s_i). Skills include retrieval, table reasoning, code repair, math, vision QA, and structured generation. Fragmentation is schema-constrained and context-aware, consistent with multi-expert practice in MoE/conditional compute. [2-5, 21]

3.3 Orchestration (Top-1 per fragment)

For fragment (f_i) with skill (s_i), the orchestrator selects exactly one compatible provider, subject to policy constraints. This is Top-1-per-fragment; proofs and logs are emitted for each selection.

p_i^* = argmax_{p ∈ P : compatible(p, s_i)} r_{s_i}(p) s.t. Policy(p, m) = true.

Compatibility checks are performed for modality, schema, jurisdiction, and budget; policy enforces region/PII constraints. Exactly one provider executes per fragment; all orchestrate decisions enter a trace ((p_i^*, \text{tokens in/out}, \text{timings}, \text{cache}, \text{policy events})).

3.4 Recursion

SEEYOU uses a recursive controller per provider, updating the latent state (z_t) and proposal (y_t) to (z_{t+1}, y_{t+1}). It then validates the outputs via schema, consistency, and task checks. This recursively refines states to recover part of ensemble‑style gains while maintaining top‑1‑per‑fragment execution, inspired by recursive controllers [47].

3.5 Stitching

Validators (schema, cross-fragment consistency) run on partials ({\hat{o}_i}); a synthesizer composes a single answer and attaches artefacts (e.g., citations, verifier packet pointer).

4. Privacy architecture

Ingress detection → tokenization → orchestration. Detectors enumerate personal/sensitive spans; a deterministic, keyed transform emits structured tokens (format-preserving if downstream tools require structure). Keys/salts are vaulted; rehydration occurs only at the edge after policy checks are completed.

The orchestration plane stores/transmits tokenized fragments only. Under the GDPR, this is referred to as pseudonymization (Art. 4(5)). The GDPR does not apply to genuinely anonymous data (Recital 26). Engineering practices follow ENISA pseudonymization guidance. [29-31]

5. Proof layer

For each fragment, we compute a canonical SHA-256 digest over the tokenized input and minimal metadata; the system emits a receipt. Receipts in a batch are aggregated into a Merkle tree; the root is anchored to a public chain (hashes only). The verifier packet includes receipts, Merkle paths, and the transaction ID, enabling third-party inclusion proofs without revealing content. We reference Concordium protocol documentation (transactions, immutability, finality). [69]

6. Market dynamics and orchestrator rationale

Siloed, single-provider deployments centralize visibility and couple performance to one vendor's Pareto front. By contrast, orchestrators have repeatedly improved outcomes by indexing broadly and orchestrating to the best provider for each subtask, from web search to mobile app ecosystems.

Historically, Xerox PARC → Mac showcased an object-oriented UI; the iPhone and App Store unified many providers behind a single interface; early alternatives (e.g., Ovi Store, App World) highlighted divergent strategies for multi-provider composition [32-36].

7. Threat model & legal compulsion

Whether a provider 'learns' from prompts is governed by contract and policy disclosures; independent of provider intent, statutory compulsion can mandate access. Also, legal compulsion regimes (e.g., FISA §702 reauthorization; CLOUD Act cross-border orders, and their international equivalents) can mandate access to data held by service providers. SEEYOU minimizes disclosure by ensuring no provider sees the full request or identity and by enforcing jurisdictional policy at orchestration time; verifier packets preserve auditability. [41-46]

In today's FM (foundation‑model) markets-i.e., the commercial and research ecosystems for very large general‑purpose models that third parties can license, access, or orchestrate-provider visibility is shaped by legal‑compulsion regimes (e.g., U.S. FISA §702) and cross‑border production instruments (e.g., the U.S. CLOUD Act) and their international equivalents-notably the UK Investigatory Powers Act 2016 (as amended 2024) [58]; the EU e‑Evidence package (Regulation (EU) 2023/1543 and Directive (EU) 2023/1544) [59]; the Council of Europe Budapest Convention Second Additional Protocol (2022) [60]; the UK Crime (Overseas Production Orders) Act 2019 together with the US-UK CLOUD Act executive agreement [61]; and Australia's International Production Orders Act 2020 together with the AU-US CLOUD Act agreement [62].

Comparable powers exist elsewhere, including Germany's Article‑10 Act (G10) and related BND authorities [63]; Sweden's FRA‑lagen [64]; and the PRC's National Intelligence Law, Cybersecurity Law, Data Security Law, and Counter‑Espionage Law [65-68].

8. Evaluation

8.1 Capability index (SPI)

For task set (\mathcal{T}) with scores (s_t), compute zscores vs a frozen baseline (Top-10 snapshot):

[
z_t = \frac{s_t - \mu_t}{\sigma_t}, \quad \mathrm{SPI}=\sum_{t\in\mathcal{T}} w_t z_t,\ \sum w_t=1.
]

Tasks: GPQA [6]; SWEbench Verified / SWEbench [7, 8]; MMLUPro [11]; MMMU/MMMUPro [17, 18]; LongBench v2 with RULER (report effective context threshold) [12, 13]; GSM8K [14]; MATH [15]; TruthfulQA [16]; ARCAGI [19, 20]. Preference (Chatbot Arena, amELO) is tracked but does not gate capability.

8.2 Operational index (OI)

Report time‑to‑first token (TTFT), tokens/s, and p50/p90/p99 latency with hardware/SDK pins; include cache state and bound proof overhead. Publication requires meeting acceptance gates and a dryrun reproduction from the verifier packet.

8.3 Worked orchestration trace (simplified example)

Request: "Read this PDF, write a 3-paragraph summary, then draft unit tests for the code in §4."
Fragments: (f_1) (doc QA→vision+OCR), (f_2) (summarisation), (f_3) (static code analysis), (f_4) (unittest synthesis).
Selections: (p_1^*) (vision QA/FM), (p_2^*) (longcontext summariser), (p_3^*) (codeLMM), (p_4^*) (toolaugmented generator).
Trace: model IDs, IO token counts, timings, and policy events; receipts • Merkle • anchor. The verifier packet includes inputs/outputs, container hashes, and transaction IDs.

9. Limitations

Top-1 orchestration simplifies verification and policy but can forgo ensemble gains when providers are strictly complementary; we quantify this trade-off in terms of OI. Mitigation via bounded recursion.

For each selected provider, SEEYOU runs a compact recursive controller that maintains a latent reasoning state (z_t) and a current proposal (y_t). At step (t), the provider is invoked to produce ((z_{t+1}, y_{t+1})), after which external validators score (y_{t+1}) (schema checks, cross-fragment consistency, task-specific verifiers).

Recursion does not assume convergence; it halts by budget-gated early stopping when marginal utility falls below a threshold or when policy/time limits are reached.

This means, SEEYOU does not force a single-shot, fixed answer and can recursively refine both latent and output states, recovering part of the assembly gains that multi-provider ensembles would otherwise obtain without violating the Top-1-per-fragment contract. This design is inspired by recent results on recursive reasoning with compact controllers [47].

Fragmentation errors propagate to stitching; we therefore publish fragmentation configurations and full orchestration traces to support reruns. Long context claims are reported with RULER curves rather than nominal context sizes.

10. Methods

10.1 Formal orchestration objective

For fragment (f_i) (skill (s_i)) with constraints (C), the Arg max function referenced in 3.3 would be expressed as:

p_i^* = argmax_{p ∈ P} [ α·cap_{s_i}(p) + β·op(p) − γ·cost(p) − δ·risk(p; C) ] s.t. compatible(p, s_i, C).

subject to (\mathrm{compatible}(p,s_i,C)). (\mathrm{cap}{s_i}): skillconditioned benchmark deltas; (\mathrm{op}): rolling latency/throughput quantiles. Runtime Top-1 maximises determinism and throughput while keeping the proof surface compact. [2-5]

10.2 Fragmentation discipline

Fragments are atomic (single skill), testable (validator-checkable), and policy-scoped (jurisdiction, PII class). The plan and mapping (\phi) are exported in the orchestration trace.

10.3 Evaluation discipline

Seeds fixed; temperature (=0) unless protocol requires variance; containers and SDK versions pinned; dataset commits recorded. Longcontext: RULER curves + effective context threshold; operations: TTFT, tokens/s, p50/p90/p99; include cache state; bound proof overhead.

10.4 Budgeted recursive refinement (Lemma: stop rule)

Let an orchestrated fragment (f_i) be handled by its selected specialist (p_i^*). The controller keeps a latent state (z_t) and a proposal (y_t) at the recursion step (t).

Parameters.

(T \in \mathbb{N}_{>0}): maximum refinement steps (recursion budget).
(\varepsilon \ge 0): marginal utility threshold for early stopping.
(V={v_1,\dots,v_m}): validators. Each (v) returns a score (s_v(y)\in[0,1]) and (optionally) a hardconstraint flag (c_v(y)\in{0,1}) with threshold (\tau_v).
(\alpha_v \ge 0) with (\sum_v \alpha_v = 1): validator weights.

Validator utility.

u(y) =

{ ∑_v α_v s_v(y) if s_v(y) ≥ τ_v for all hard v

−∞ otherwise

Recursion step.
Initialise ((z_0,y_0)=p_i^*(f_i)); set (u_0=u(y_0)), (y^{\star}=y_0), (u^{\star}=u_0).

For (t=0,1,\dots,T-1):

Propose: ((z_{t+1},y_{t+1}) \leftarrow p_i^*(f_i, z_t)).
Score: (u_{t+1} \leftarrow u(y_{t+1})).
Acceptbest: If (u_{t+1} \ge u^{\star}) then (y^{\star}!\leftarrow y_{t+1},\ u^{\star}!\leftarrow u_{t+1}).
Stop rule: halt if (i) (t+1 \ge T), or (ii) (u_{t+1} - u^{\star} < \varepsilon), or (iii) a policy/time limit triggers; else continue.

Return: (y^{\star}) and the trace ({(y_t,u_t)}{t=0}^{t{\text{halt}}}).

Lemma (Termination and non-degradation). The procedure halts after at most (T) steps (or earlier by (ii)/(iii)) without assuming convergence; (u(y^{\star})=\max_{0\le j\le t_{\text{halt}}} u(y_j)). The best utility (u^{\star}) so far is monotone non-decreasing.

Rationale. Bounded recursion lets a single selected specialist refine latent and output states under external validators, recovering part of ensemblestyle gains without violating the Top-1perfragment contract. See JolicoeurMartineau (2025) [47].

11. Governance & compliance alignment
(summary, see Appendix A for a more extensive list)

Security/assurance. ISO/IEC 27001:2022 & ISO/IEC 27701:2025 controls (vaulted keys/salts; leastprivilege; immutable logging); SOC 2 Trust Services Criteria mapping. [24, 25]; ISO/IEC 27018:2025; DNV-RP-0497; DNV-RP-0671
AI governance. ISO/IEC 42001 (AIMS); ISO/IEC 23894 (AI risk); NIST AI RMF 1.0. [22, 26, 27]; ISO/IEC 27018:2025; DNV-RP-0497; DNV-RP-0671
Regulatory overlays. EU AI Act; GDPR (Recital 26, Art. 4(5), SCCs where applicable); DORA (financial sector operational resilience). [23, 28-30]; ISO/IEC 27018:2025; DNV-RP-0497; DNV-RP-0671

12 Evaluation standards & reproducibility

SEEYOU will publish comprehensive, standards-aligned evaluation specifications, metric definitions, benchmark list, SPI/OI weights, z-score normalizations (frozen top 10 baselines per release), acceptance gates, and refresh schedule, so that any third party can independently reproduce and validate SEEYOU's composite Performance Index (SPI) and Operational Index (OI).

Benchmarks (canonical, with official metrics and harnesses). We commit to the widely adopted sets and primary metrics used by researchers and providers, enabling like-for-like reproduction:

GPQA (Diamond), accuracy. [6]
SWEbench Verified / SWEbench, task solved rate via executable tests. [7, 8]
MMLUPro, accuracy (10-way MCQ). [11]
MMMU / MMMUPro, accuracy over multimodal college-level tasks. [17, 18]
LongBench v2 with RULER, task metrics (EM/F1/ROUGE) plus effective context threshold from RULER curves. [13, 12]
GSM8K, accuracy. [14]
MATH, exact match under standard normalization. [15]
TruthfulQA, MC2 (and judged truthfulness in generation). [16]
ARCAGI, task accuracy on the public split(s). [19, 20]

Public specification and artefacts (per benchmark). For each benchmark we will publish, under an open license:

Specification: the exact metric(s), aggregation rules, and SPI/OI weights; z-score normalisation formula and frozen baseline (mean/std) for the release.
Run manifest: seeds, decoding parameters, hardware/driver/SDK/container hashes, dataset commit pins, and the modelindex snapshot used.
HOWTO: onecommand re-run instructions using the official harness/scorer (e.g., lmeval; SWEbench harness; RULER tools) and SEEYOU CLI/SDK, with no proprietary scoring.
Verifier packet: inputs/IDs and checksums, predictions/outputs, per-item and aggregate scores, orchestrate traces, and inclusion proofs (Merkle paths + on-chain tx IDs) to audit what ran and how it was orchestrated.

Reproducibility discipline. All public runs will fix seeds (temperature = 0 unless the protocol requires variance), pin containers/harness versions, and disclose cache/concurrency state for OI. Normalization baselines are frozen per quarterly release and documented with deltas; any benchmark-specific ablations (e.g., with/without CoT; retrieval on/off) are reported as such.

13. Acknowledgements

We thank external reviewers and auditors for reproducing the dry runs and for their comments on the verifier structure.

14. Competing interests

Authors are affiliated with SEEYOU.

Appendix A, Reg Governance & Compliance Alignment

Democratic AI entails three concrete commitments: Access (the best model for every part of every task), Privacy (no provider ever sees the whole question, identity, intent, or outputs), and an Open market (a neutral route-to-market for every new specialist). The items below map SEEYOU's approach to widely adopted public targets and frameworks.

European Union (EU) & wider Europe

EU Digital Decade 2030 - enterprise AI uptake [69]

Objective: 75% of EU companies using Cloud/AI/Big Data by 2030; >90% SME digital intensity.
How SEEYOU fulfils it: one interface to state-of-the-art providers lowers adoption friction for SMEs and enterprises; vendor-neutral top1 routing avoids lock-in; reproducible evaluation and operational metrics derisk procurement and scale-out.

EU AI Act (Regulation (EU) 2024/1689) [23]

Objective: risk-based duties; transparency, logging/recordkeeping; additional GPAI responsibilities.
How SEEYOU fulfils it: route traces, verifier packets, and public anchoring supply auditable evidence; policy-bound routing and jurisdiction controls support role-accurate deployments (developer/provider/deployer), without exposing full prompts or identity.

EU Data Act (Regulation (EU) 2023/2854) & EU Data Governance Act (Regulation (EU) 2022/868) [71, 72]

Objective: fair access/use of data; trusted intermediaries and data sharing.
How SEEYOU fulfils it: deterministic tokenisation and edge-only rehydration reduce data exposure; neutral orchestration across providers supports interoperability and participation in EU data spaces without concentration risk.

DORA - Digital Operational Resilience Act (Regulation (EU) 2022/2554) [28]

Objective: ICT risk management, testing, incident logging/reporting.
How SEEYOU fulfils it: the Operational Index (TTFT, tokens/s, p50/p90/p99), immutable receipts, and pinned, reproducible runs provide resilience and audit artefacts that financial entities can rely on.

Council of Europe - Framework Convention on AI (CETS No. 225) [74]

Objective: transparency, oversight, accountability across the AI lifecycle (human rights anchored).
How SEEYOU fulfils it: provider-blind orchestration (fragment-level tokenisation) and verifiable execution operationalise transparency/oversight while keeping end-to-end auditability.

United Nations / Global

UN Sustainable Development Goals - SDG 9 & SDG 16.10 [75]

Objective: inclusive innovation/infrastructure (SDG 9) and public access to information (SDG 16.10).
How SEEYOU fulfils it: universal access to the best model for each subtask (access/innovation) and publicly verifiable artefacts (access to information).

OECD AI Principles (G20-endorsed) [73]

Objective: inclusive growth; humancentred values; transparency/explainability; robustness; accountability.
How SEEYOU fulfils it: route traces and verifier packets address transparency/accountability; policy-enforced routing and proofs address robustness and governance.

UNESCO Recommendation on the Ethics of AI (2021) [76]

Objective: human rights basis, privacy by design, transparency.
How SEEYOU fulfils it: pseudonymisation by design (GDPR Art. 4(5) and Recital 26 already in your list [29-31]), plus public anchoring for traceability.

G7 Hiroshima Process - Code of Conduct for Advanced AI Systems (2023) [77]

Objective: evaluation, risk management, content authenticity, and security.
How SEEYOU fulfils it: standards-aligned benchmark protocols, composite indices (SPI/OI), and on-chain inclusion proofs deliver the evaluation and assurance surface envisaged.

UN Global Digital Compact (2024) [78]

Objective: digital inclusion, trustworthy AI governance, interoperability, capacity building.
How SEEYOU fulfils it: one interface to many providers (inclusion); verifiable orchestration (trust); vendor-neutral routing (interoperability).

APAC exemplars

Singapore - Model AI Governance Framework for Generative AI (2024) [79]

Objective: testing, transparency, safety for GenAI.
How SEEYOU fulfils it: standards-aligned evaluation (public specs, weights, gates) and verifier packets meet the framework's testability/transparency emphasis.

Japan - AI Guidelines for Business (2024/2025) [80]

Objective: lifecycle governance balancing innovation and risk.
How SEEYOU fulfils it: deployers receive logs, proofs, policy artefacts, and role-accurate controls to document governance without vendor lock-in.

Australia - AI Ethics Principles (2019→) & Government Assurance (2024) [81]

Objective: humancentred values, fairness, transparency, accountability; practical assurance steps.

How SEEYOU fulfils it: explainable routing (traces), accountability (receipts), privacy by design (tokenisation/edge rehydration), and repeatable test runs for assurance.

India - Digital Personal Data Protection Act (2023) [82]

Objective: consent-centric processing and safeguards for personal data.
How SEEYOU fulfils it: deterministic tokenisation and policy-enforced routing support privacy-by-design deployments and jurisdictional control.

Hong Kong (SAR) - PDPO & sector guidance [83-86]

Objective: Protect personal data (PDPO, Cap. 486) and govern AI through PCPD guidance/model framework; in finance, follow HKMA principles and GenAI consumer protection circulars.
How SEEYOU fulfils it: Deterministic tokenization and edge-only rehydration implement PDPO dataminimization and purposelimitation; route traces + receipts/Merkle anchoring provide accountability/audit; policy-bound, jurisdiction-aware routing and vendor-neutral Top1 orchestration align with PCPD's ethical governance and HKMA's principles on governance, fairness, transparency and data protection for (Gen)AI in banking

References

Indexing & orchestration

[1] Brin, S.; Page, L. The Anatomy of a Large-Scale Hypertextual Web Search Engine. Computer Networks 30, 107-117 (1998).

Localexpert substrate (inspiration; runtime is Top-1 per fragment)

[2] Jacobs, R.A.; Jordan, M.I.; Nowlan, S.J.; Hinton, G.E. Adaptive Mixtures of Local Experts. Neural Computation 3(1), 79-87 (1991).
[3] Shazeer, N. et al. Outrageously Large Neural Networks: The SparselyGated MoE Layer. arXiv:1701.06538 (2017).
[4] Lepikhin, D. et al. GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding. arXiv:2006.16668 (2020).
[5] Kondratyuk, D.; Tan, M.; Brown, M.; Gong, B. When Ensembling Smaller Models Is More Efficient Than a Single Large Model arXiv:2005.00570 (2020).
[21] Cai, W. et al. A Survey on Mixture of Experts in Large Language Models. arXiv:2407.06204 (2024).

[47] JolicoeurMartineau, A. Less is More: Recursive Reasoning with Tiny Networks. arXiv:2510.04871 (2025).

Benchmarks & preference

[6] Rein, D. et al. GPQA. arXiv:2311.12022 (2023).
[7] OpenAI. SWEbench Verified (2024/2025).
[8] Jiménez, C.E. et al. SWE bench. arXiv:2310.06770 (2023).
[9] Chiang, W.L. et al. LMSYS Chatbot Arena. arXiv:2403.04132 (2024).
[10] Liu, Z. et al. amELO: A Stable Framework for Arena-based LLM Evaluation. (2025).
[11] Wang, Y. et al. MMLU Pro. arXiv:2406.01574 (2024).
[12] Hsieh, C.P. et al. RULER: What's the Real Context Size of Your LongContext LMs? arXiv:2404.06654 (2024).
[13] Bai, Y. et al. LongBench v2. arXiv:2412.15204; ACL 2025.
[14] Cobbe, K. et al. GSM8K. arXiv:2110.14168 (2021).
[15] Hendrycks, D. et al. MATH. NeurIPS Datasets & Benchmarks (2021).
[16] Lin, S.; Hilton, J.; Evans, O. TruthfulQA. ACL (2022).
[17] Yue, X. et al. MMMU. CVPR (2024).
[18] Yue, X. et al. MMMUPro. arXiv:2409.02813 (2024).
[19] ARC Prize Foundation. ARCAGI benchmark resources (2019-2025).
[20] ARCAGI2 program & leaderboard (2025).

Governance/security/risk/law

[22] ISO/IEC 42001:2023 (AIMS). ISO.
[23] EU Artificial Intelligence Act (Regulation (EU) 2024/1689).
[24] ISO/IEC 27001:2022 & ISO/IEC 27701:2019
[25] AICPA. SOC 2 Trust Services Criteria (2017; 2022 points of focus).
[26] ISO/IEC 23894:2023 (AI risk). ISO.
[27] NIST AI RMF 1.0 (2023).
[28] DORA (Regulation (EU) 2022/2554), applicable 17 Jan 2025.
[29] GDPR Recital 26, Anonymous data outside scope.
[30] GDPR Art. 4(5), Pseudonymisation.
[31] ENISA. Pseudonymisation techniques & best practices (2019).
[37] UK CMA. AI Foundation Models, Update paper (Apr 2024).
[38] UK CMA. Technical Update Report (April 2024).
[39] UK CMA. Case page & timetable (2024-2025).
[40] Cleary Gottlieb. Note on CMA FM update (Apr 2024).
[41] U.S. Congress. Public Law 118- 49 (FISA §702 reauthorisation), 2024.
[42] AP News. Coverage of §702 reauthorisation (context), 2024.
[43] FBI/IC explainer on §702 scope (background).
[44] U.S. DOJ. CLOUD Act White Paper (2019).
[45] U.S. DOJ. CLOUD Act resources (overview).
[46] CRS. §702 & reform overview (2025).

Inspiration

[32] Computer History Museum: PARC/GUI backgrounders.
[33] Apple, Inc. (2008). App Store press materials.
[34] Nokia Ovi Store launch (2009).
[35] BlackBerry App World launch (2009).
[36] Wired (2005), "The Birth of Google."

[48] BIS. OTC derivatives statistics, overview (accessed 2025).
[49] BIS. OTC derivatives statistics at the endof June 2024 (21 Nov 2024). [
50] Stanford HAI. 2025 AI Index Report, Economy: Private AI investment by country (2025).
[51] LMArena. WebDev Arena Leaderboard (accessed Oct 2025).
[52] International Judo Federation. SeiryokuZenyo: Ultimately, there is Simplicity (Kodokan principle).

Orchestrator value generation

[53] Uber, Market Cap (Oct 2025).
[54] Ford, Market Cap (Oct 2025).
[55] General Motors, Market Capitalization (October 2025).
[56] Mercedes-Benz Group, Market cap (Oct 2025).
[57] Volkswagen, Market Capitalization (as of October 2025).

International equivalents of FISA §702 and the Cloud Act, mandating government insights

[58] United Kingdom: Investigatory Powers Act 2016; Investigatory Powers (Amendment) Act 2024 (Royal Assent 25 Apr 2024).
[59] European Union: Regulation (EU) 2023/1543 on European Production and Preservation Orders for electronic evidence; Directive (EU) 2023/1544 on designated authorities and safeguards.
[60] Council of Europe: Second Additional Protocol to the Convention on Cybercrime on enhanced cooperation and disclosure of electronic evidence (Opened for signature 12 May 2022).
[61] United Kingdom: Crime (Overseas Production Orders) Act 2019; US-UK CLOUD Act Executive Agreement (entered into force 3 Oct 2022).
[62] Australia: Telecommunications Legislation Amendment (International Production Orders) Act 2020; AU-US CLOUD Act Agreement (entered into force 2024).
[63] Germany: Gesetz zur Beschränkung des Brief-, Post- und Fernmeldegeheimnisses (Artikel-10-Gesetz, "G10-Gesetz"); related foreign-intelligence interception authorities under the Gesetz über den Bundesnachrichtendienst (BND-Gesetz).
[64] Sweden: Lag (2008:717) om signalspaning i försvarsunderrättelseverksamhet ("FRA-lagen"), as amended.
[65] PRC: National Intelligence Law of the People's Republic of China (2017).
[66] PRC: Cybersecurity Law of the People's Republic of China (2017).
[67] PRC: Data Security Law of the People's Republic of China (2021).
[68] PRC: Counter-Espionage Law of the People's Republic of China (revised 2023).

Example policies promoting democratization of generative AI

[69] European Commission. Digital Decade Policy Programme 2030 - Targets (enterprise uptake of Cloud/AI/Big Data; SME digital intensity).
[70] Council of Europe. Framework Convention on Artificial Intelligence, Human Rights, Democracy and the Rule of Law (CETS No. 225) - transparency, oversight, accountability (opened for signature 5 Sep 2024).

[71] European Union. Regulation (EU) 2023/2854 (Data Act).

[72] European Union. Regulation (EU) 2022/868 (Data Governance Act).

[73] OECD. Recommendation of the Council on Artificial Intelligence (OECD AI Principles). 2019 (G20‑endorsed).

[74] Council of Europe. Framework Convention on Artificial Intelligence, Human Rights, Democracy and the Rule of Law (CETS No. 225).

[75] United Nations. Sustainable Development Goals - SDG 9 & SDG 16.10.

[76] UNESCO. Recommendation on the Ethics of Artificial Intelligence. 2021.

[77] G7. Hiroshima Process - International Code of Conduct for Advanced AI Systems. 2023.

[78] United Nations. Global Digital Compact (adopted 2024; annex to the Pact for the Future).

[79] AI Verify Foundation (Singapore). Model AI Governance Framework for Generative AI. 2024.

[80] METI/MIC (Japan). AI Guidelines for Business (2024/2025).

[81] Government of Australia. AI Ethics Principles (2019) and AI Assurance Framework (2024).

[82] Government of India. Digital Personal Data Protection Act, 2023.

[83] Hong Kong SAR. Personal Data (Privacy) Ordinance (Cap. 486). (official Hong Kong e‑Legislation).

[84] PCPD (Hong Kong). Guidance on the Ethical Development and Use of Artificial Intelligence. Aug 2021.

[85] PCPD (Hong Kong). Artificial Intelligence: Model Personal Data Protection Framework. Jun 2024.

[86] Hong Kong Monetary Authority. High‑level Principles on Artificial Intelligence (Nov 2019) and Consumer Protection in respect of the Use of Generative AI by Authorised Institutions (Aug 2024).

Note (authority & scope)

This overview is explanatory. The authoritative controls, roles, cadences, KPIs, Statements of Applicability, and audit evidence are defined in SEEYOU's Internal Information Security & Compliance Policy v5.3. See Annex B (ISO/IEC 27001 SoA) and Annex C (ISO/IEC 42001 SoA) in that policy.