Flagship AI Governance Model:Introducing TG-Sentinel:G1

General-purpose frontier models were trained to converse, summarize, and generate code. They were never designed to be neutral, mathematically anchored constitutional adjudicators. When enterprise agents interact with external tools, robotic execution pipelines, and sensitive customer databases, asking a generalist model to self-police its own hallucinations is a fundamentally flawed security posture.

Today, we are announcing TG-Sentinel:G1 (Governance One) — to our knowledge and industry research, the world's first unified foundation model family purpose-built exclusively for AI governance, technical risk analysis, and real-time runtime enforcement.

Rather than relying on generic classifiers or slow, expensive frontier model prompts, TG-Sentinel:G1 provides an autonomous, enterprise-grade governance engine that inspects, verifies, and steers multi-turn agent interactions before they reach production systems.

Why a Dedicated Governance Foundation Model?

Regulated enterprises in banking, healthcare, robotics, and defense cannot afford safety guardrails that hallucinate, drift, or choke latency. Generic LLM guardrails suffer from three fatal flaws:

1. Self-Policing Bias & Sycophancy: Frontier models from providers like Claude, OpenAI, or Gemini are trained to please the user, making them susceptible to prompt injection, persona circumvention, and semantic obfuscation.
2. Excessive Latency & Compute Cost: Invoking a heavy general-purpose model on every interaction adds seconds of latency and unsustainable API fees.
3. Lack of Domain Grounding: A generalist model does not possess verified kinematics limits for automated supply chains, AML transaction consistency rules, or strict clinical contraindication boundaries.

TG-Sentinel:G1 solves this by decoupling the governance plane from the task execution plane. It operates as an independent constitutional judge between the user, the frontier model, and enterprise APIs.

Architectural Design: 46B-A19B 7+1 Mixture-of-Experts

TG-Sentinel:G1 is built upon a proprietary 46B total / 19B active parameter 7+1 Mixture-of-Experts (MoE) architecture (TGMoeForCausalLM):

• 7 Routed Experts + 1 Shared Expert: Factual constitutional knowledge and baseline legal discipline are anchored in a dedicated shared expert, while nuanced policy reasoning, sector-specific risk categorization, and tool audit capabilities are routed dynamically across 7 specialized experts.
• Dynamic FP8 Serving Efficiency: Deployed on custom-optimized vLLM infrastructure utilizing fp8_w8a8 quantization, FP8 KV-Cache, and optimized kernel acceleration, G1 delivers sub-60ms Time-To-First-Token (TTFT) and high-concurrency throughput exceeding 14,000 generated tokens/second on just two NVIDIA H100 GPUs (2× H100 SXM5). Extrapolating from this dual-GPU baseline, enterprise deployments on an 8× NVIDIA B300 cluster are projected to reach 60,000+ to 80,000+ generated tokens/second.
• Dynamic Activation Steering (DGH): Evaluated and locked with layer-level directional steering vectors, allowing the model's activations to be steered toward constitutional commitments in real time with zero token degeneration.
• Unified Multi-Mode Operational Profiles: Rather than forcing enterprises to deploy and maintain disparate models for different governance tasks, a single TG-Sentinel:G1 checkpoint natively supports configurable multi-mode execution:
• Fast Mode: Sub-60ms deterministic adjudication for high-volume, real-time synchronous inference paths. Evaluates first-line interactions against explicit constitutional boundaries and baseline rules with minimal latency overhead.
• Max Mode: Deep multi-turn agent trajectory evaluation, balancing contextual reasoning, implicit intent detection, and comprehensive evidence citation across complex tool-calling sequences.
• Ultra Mode: High-stakes statutory deliberation and precedent arbitration. Engineered specifically for ambiguous edge cases, conflicting binding policies, and escalated disputes where prior evaluation tiers (Fast or Max) were indecisive or encountered contradictory constraints. G1 Ultra ingests prior tier evaluation context and multi-party policy rules to deliver legally anchored finality.
• Beyond "Reasoning Levels": Fast, Max, and Ultra modes are not merely a scalar "reasoning effort" or token-budget dial applied to an identical prompt. Each operational profile addresses a distinct input complexity contract, contextual scope, and evidentiary burden. Fast Mode's 99.13% status precision demonstrates rapid, reliable filtering on first-line interactions; whereas Ultra Mode's 95.38% decision accuracy and 96.28% risk taxonomy resolution reflect mastery over deeply adversarial, contradictory edge cases where previous tiers encountered ambiguity.
• Cyber Mode (mode: cyber): Purpose-built for deep Cybersecurity Vulnerability Verification (CVV) — evaluating CVE/CWE vulnerability vectors, kernel/eBPF safety, SQLi, SSRF, Kubernetes RBAC privilege escalations, and malicious payload interception.
• Raw Mode (mode: raw): Open-ended technical reporting for human security operations and compliance audits requiring detailed forensic analysis without strict JSON schema constraints.
• Hierarchical Governance via MCP: When an agent attempts a mission-critical command (such as industrial robotic manufacturing instructions or medication dosage adjustments), G1 invokes specialized Deep-Domain Expertise (DDE) models via Model Context Protocol (MCP) to cross-examine factual claims before execution.

Built-in Regulatory Standards & Governance Frameworks

Unlike generalist models that require fragile prompt wrappers to simulate compliance, TG-Sentinel:G1 embeds constitutional boundaries, legal requirements, and security taxonomies directly across its expert representations. The model natively reasons across:

• Global AI Safety & Governance: EU AI Act (Regulation 2024/1689, Art. 14 Human Oversight), NIST AI RMF 1.0 (NIST AI 100-1 & AI 600-1 Generative AI Profile), and ISO/IEC 42001:2023 (Artificial Intelligence Management System - AIMS).
• Adversarial & Threat Defense: OWASP Top 10 for LLM Applications (2026 Edition), OWASP Agentic Skills Top 10 (AST10), and MITRE ATLAS (Adversarial Threat Landscape for AI Systems).
• Enterprise Privacy & Trust: GDPR (Art. 9 Special-Category Data & Art. 22 Automated Decisions), HIPAA (Protected Health Information - PHI), PCI-DSS, and SOC 2 Type II (CC7 Trust Services Criteria).
• Export Control & Defense Compliance: U.S. ITAR & EAR trade regulations, NIST SP 800-171, and air-gapped sovereign defense protocols.

Internal Pre-Production Test Runs & Verified Benchmark Evidence

General-purpose benchmark leaderboards (such as MMLU or GSM8K) measure high-school trivia or grade-school math. For an enterprise AI governance model, those metrics are irrelevant. A constitutional governance model must be evaluated on one non-negotiable question:

Can it intercept adversarial attacks, policy drift, and unsafe agent actions with near-zero latency and near-zero false alarms?

Our engineering team subjected TG-Sentinel:G1 to extensive domain-specific holdout batteries and stress tests prior to General Availability:

• Technical Cybersecurity Benchmark (35 Datasets — 100.0%): Evaluated across complex vulnerability analysis test suites — including second-order SQL injection, SSRF, unauthorized Kubernetes RBAC escalations, and metadata poisoning — achieving a perfect 100% detection rate.
• Enterprise Governance Discovery Benchmark (15 Datasets — 100.0%): Tested against sophisticated adversarial attempts to circumvent corporate rules, including obfuscated ITAR export violations, financial data exfiltration, and deceptive fraud prompts, correctly identifying and blocking every violation.
• 4,050-Evaluation Controlled Holdout Battery (Theseus Guard-authored): Across a 2,600-case multi-turn holdout corpus — synthesized by Theseus Guard from scenario templates and held out from all training and development data — spanning diverse risk scenarios, G1 demonstrated remarkable semantic judgment: achieving 94.61% (Fast), 95.31% (Max), and 95.38% (Ultra) decision accuracy (with Ultra delivering the highest risk taxonomy categorization accuracy at 96.28%), overall status classification accuracy between 97.79% and 99.13%, and an aggregate 0.86% Safe Over-Block rate across all 1,400 benign cases in the 4,050-case battery (0/400 in Fast [0.00%], 7/500 in Max [1.40%], and 5/500 in Ultra [1.00%] = 12/1,400). (Note: Within the Theseus Guard Enterprise AI Governance Platform, TG-Sentinel:G1 can operate across Fast, Max, Ultra, and Cyber operational modes as a unified foundation model. The benchmark figures reported here reflect G1's verified accuracy when executing under each respective operational mode).
• 100.0% Deterministic JSON Schema Fidelity: Across continuous batching stress tests (3,840 requests at up to 1,024 concurrency), G1 achieved zero syntax breaks and deterministic end-of-sequence termination, guaranteeing seamless programmatic interoperability.
• Live OWASP LLM Top 10 Probing (30 Probes — 100.0%): Tested against all primary 2026 OWASP LLM threat categories, successfully intercepting 100% of adversarial probes at the kernel boundary.
• Zero Customer Data Exposure: All training and validation pipelines operate under strict Zero-Data-Memorization principles, fully ready for sovereign VPC and air-gapped deployments.

Official Model Card & Specifications

In accordance with our commitment to cryptographic provenance and transparent AI safety, we present the technical specifications and internal validation card for TG-Sentinel:G1:

What’s Next?

Our engineering team is actively running extended holdout evaluations, refining cross-lingual transfer (including Turkish, German, French, and Spanish), and validating G1 within live Design Partner pilot environments.

In parallel, development of the specialized Deep-Domain Expertise (DDE) models designed to operate under TG-Sentinel:G1 orchestration is actively underway, expanding domain-grounded claim and action verification across critical industry verticals.

TG-Sentinel:G1 marks a pivotal transition in enterprise AI: moving away from fragile, reactive prompt wrappers toward a mathematically disciplined, dedicated governance foundation model.

Stay tuned for upcoming whitepapers, detailed benchmark datasets, and early enterprise pilot access announcements.

Special Thanks & Acknowledgments

We would like to extend our sincere gratitude to NVIDIA, AWS, and Lambda AI for their invaluable infrastructure, compute resources, and engineering support throughout the development, training, and benchmarking of the TG-Sentinel:G1 foundation model family.

For a detailed comparison of TG-Sentinel:G1 with commercial frontier models and open-source guardrails across 4,050 evaluations, see the TG-Sentinel:G1 benchmark results.