H33-HIVE — Cryptographically Governed AI Model
Intelligence, memory and learning. Governed from the ground up.
Make every AI computation count.
H33-HIVE is H33’s full AI model and intelligence system, under development, designed with cryptographic governance integrated into its foundation.
HIVE brings together reasoning, learning, verifiable memory, intelligent execution and governed model evolution. TUTU is its integrated learning and model-development subsystem, designed to improve the model’s capabilities while preserving independently verifiable evidence of every change.
The objective is a more capable AI system with stronger memory integrity, lower cost per correctly completed task, stronger security and protection against unauthorized changes to the model and its memory. HIVE is not simply a router, cache or scheduler: those are supporting functions inside a larger AI architecture.
Research status H33-HIVE remains under development. Its full model, governed learning loop and production execution path have not been independently qualified, and performance and security benefits are design objectives unless a specific test result below supports them. Today: no HIVE model has been trained for use or released, no TUTU candidate has been promoted, and none serves any task; HIVE’s routing and execution core is a research implementation tested with simulated providers; no real-model performance has been measured. L100’s separate review process — an AI review session independent of the author’s, with hashed evidence, not an external audit — qualified the HIVE core’s read and write admission logic at L100 implementation commit 1ea6cc6, for that in-core scope only. That review also found a high-severity authorization defect: a previously issued gate could still authorize a commit after its ticket was revoked. The same review process reports the defect cured within the reviewed single-process core, at implementation commits 2e00a56 and be7d509; the reviewer’s identity is not attested, so the cure is reported, not independently established. Cross-process operation and other integrations, including Agent-008, remain unqualified, and the admission path is not production-ready. Authorization in the research build is simulated, not authenticated: its only implementation is a placeholder, so the research build performs no real consequential actions, and real consequential execution stays unavailable until the Agent-008 integration qualifies. No production performance or security guarantee is made. Each capability’s status is listed below, and the architecture page sets out what has been tested, under what conditions, and what has not.
H33-HIVE is the model. TUTU is part of it: the subsystem that learns, trains and qualifies improvements to HIVE’s capabilities, with Champion TUTU deciding which learning is worth pursuing. Cryptographic governance is not added afterwards: it is the foundation the model is designed to learn, remember, execute and evolve on. Both are built in H33’s L100 engineering program.
One AI model. Integrated intelligence, memory, learning and execution.
Most AI systems treat governance as something wrapped around a finished model. HIVE is designed the other way round: what the model may learn, what it may remember, what it may execute and how it may change are governed from the foundation up.
Target architecture, not a claim of completed production integration.
Model intelligence
Reasoning, mathematics, coding and security capability. No HIVE model has been trained for use yet: TUTU’s research works from a pinned open-weight base model, and no candidate serves any task.
TUTU
HIVE’s learning and model-development subsystem: trains candidate improvements and evaluates them against sealed held-out sets, refusing any regression. It can recommend a successor model; it cannot admit one. More on TUTU.
Verified memory
What HIVE remembers and reuses is bound to the inputs, tenant, model generation, policy and verifier that produced it, and refused when any of them no longer match. Computational reuse is designed to run through Cachee.
Intelligent execution
Chooses the model, tool, computation path or verified result able to complete a task, and recovers from failures without repeating consequences. Routing, caching, scheduling and CPU/GPU placement live here, as supporting functions.
Outside HIVE, Agent-008 is designed to govern what any execution is authorized to do. HIVE proposes and executes; it never grants itself authority.
From a task to a governed improvement.
The lifecycle HIVE is designed around, in order. Each stage carries its status today.
Understand and reason
Determine what the task needs: its intent, the capabilities it requires, its constraints and what a correct result looks like, and whether to answer, reuse, delegate, escalate or refuse.
Implemented research: per-category task classification. Target: intent analysis and decomposition.
Retrieve and verify memory
Before repeating work, check for a result already verified under the same inputs, tenant, model generation, policy, verifier and freshness. A stale, mismatched or unverified entry is never treated as a result.
Implemented research, against a stand-in for Cachee. Integration pending: Cachee isolation in HIVE.
Plan and execute
Select the model, tool or computation path with measured capability for this kind of task; escalate only when an independent verifier rejects a cheaper answer; keep budgets and deadlines; settle an ambiguous attempt as Unknown rather than risk running it twice.
Implemented research, with simulated providers. Target: multi-stage plans and tool workflows.
Verify outcomes
Check, independently, whether the task was actually completed correctly. A model reviewing its own answer does not count.
Implemented research.
Learn through TUTU
Turn verified evidence into candidate improvements to the model, and into better execution policies, with Champion TUTU allocating learning effort to the gaps worth closing.
Implemented research: TUTU pipeline; Champion TUTU in shadow mode only.
Independently qualify improvements
A candidate is measured against sealed held-out sets it never trained on, and a regression in one area cannot be outvoted by gains in another. Only independent qualification makes a candidate eligible.
Independently verified: the contamination gate. Target: qualification and promotion of a successor.
Govern model evolution
A qualified change to the model, its memory or its policies takes effect only under legitimate authority, with evidence of what changed, who authorized it and how to reconstruct it.
Target. Authority integration pending.
What HIVE is designed to deliver
These are design objectives. Each becomes a claim only when a stated test supports it; the tested results so far are on the architecture page, including the ones that did not go HIVE’s way.
Stronger memory integrity
Memory that cannot be silently tampered with, mixed across tenants or reused after the model, policy or authority behind it has changed.
Less unnecessary computation
Fewer repeated computations, fewer requests sent to more model than they need, and cost measured per correctly completed task rather than per call.
Stronger security
Prompts, retrieved documents, cached results and model outputs never become authority, and consequential actions require legitimate authorization.
Resistance to unauthorized drift
Designed so the model changes only through qualified, authorized and evidenced transitions, never because it learned something on its own. Today, in research, a candidate that regresses on sealed held-out sets is refused; drift of a trained model has not been measured, and cryptographic enforcement is not built. No absence of drift is claimed.
Unauthorized change, memory violation, drift and error are different problems.
Each needs a different control, and each is stated here by what the research build actually does today.
| Problem | Designed control | Today |
|---|---|---|
| Unauthorized model-state change | Weights and model admission change only through independent qualification under legitimate authority; TUTU cannot promote its own results. | Prevented by process in research: no candidate has been promoted, and none serves any task. Model-file manifest and exact-commit checks are implemented in research. Unresolved: they enforce nothing yet, because the reviewer authority key is not provisioned, and cryptographic enforcement of model admission under H33 authority is not built. |
| Memory-integrity violation | Every reusable result is bound to its inputs, tenant, model generation, policy, verifier and freshness, and refused on any mismatch. | Detected and refused in research: 3,290 injected poisoned or mismatched entries, none served, against a stand-in. The integrity check is a test stand-in, not cryptography; Cachee isolation in HIVE is under remediation. |
| Behavioural drift | Candidates are measured against sealed held-out sets and declared retention floors; a regression is refused, and execution policies are re-evaluated on held-out data. | Detected before promotion in research: regression refusal is implemented and tested. Not monitored: retention results are not yet established, the behavioural drift of a trained model has not been measured, and there is no production monitoring because there is no production deployment. |
| Ordinary model error | An independent verifier checks eligible answers; a rejected answer escalates to a stronger path, and only verified answers can be reused. | Detected where a verifier exists, in research. Not prevented: a model can still be wrong, and a task without an independent verifier is not checked. |
The authorization defect described above is separate from all four; its cure is reported for the single-process core and is not independently established.
Learning without losing control
“Learning” covers four different things in HIVE, and they are kept apart on purpose:
- Model learning. TUTU trains candidate improvements to the model’s weights and evaluates them; nothing it trains is used until it is independently qualified.
- Memory continuity. What HIVE has verified stays usable only while the model generation, policy, authority and verifier behind it still hold, and is invalidated when they change.
- Policy adaptation. HIVE is designed to improve how it routes and orchestrates work from verified outcomes, through versioned policies evaluated against held-out data and fixed baselines, with controlled promotion and rollback. This changes no weights, and no policy is promoted merely because it is faster or uses less GPU time (target).
- Governed model change. A qualified successor model, memory rule or policy takes effect only through an authorized, evidenced transition (target).
Background learning stays subordinate to foreground work: it must not take resources a live request needs. Champion TUTU’s recommendations grant no authority and promote no weights.
Intelligence decides what to propose. Authority determines what may execute.
HIVE is designed to compose with H33’s cryptographic governance foundation rather than re-implement it:
| Component | Intended role in HIVE |
|---|---|
| Agent-008 | Authorizes consequential actions within legitimate scope. |
| H33-Root | Establishes the human authority lineage behind every governed change. |
| H33-Key | Keeps keys and credentials in custody; the model never holds signing capability. |
| H33-API-G | Enforces governed API execution boundaries. |
| Cachee | Provides verified computational reuse and its evidence. |
| H33-Stamption | Preserves governance commitments and transitions. |
| H33-74 and Replay | Support portable evidence and reconstruction of governed operations. |
A prompt, retrieved document, cached result or model-generated instruction must not acquire authority simply because a model produced it. This is the intended integrated architecture, not current behaviour: none of these components yet enforces authority on HIVE’s behalf.
Use the GPU when you need it. Not because it’s there.
Where work runs is one execution decision among many, alongside model and tool selection, verified reuse, batching, escalation and failover. HIVE’s performance estimation and batching are implemented and tested in research and simulation; speculative execution was evaluated, was not beneficial and is not enabled; today’s research build does not place work on hardware.
CPU execution
Routing, admission checks, scheduling, lightweight processing, verification and suitable inference workloads.
GPU acceleration
Large-model inference, demanding parallel computation, training and other workloads where acceleration is justified.
Verified reuse
When eligible work has already been completed and independently verified, it is designed not to be computed again.
Savings are measured, not assumed. Before any saving is claimed, latency at p50, p95 and p99, throughput, cost, energy, correctness and cold-start behaviour have to be measured against a stated baseline.
A complete AI model with governance built into its foundation.
| Conventional approach | H33-HIVE design |
|---|---|
| Add governance around a finished model | Design learning, memory, execution and change on a cryptographic governance foundation |
| Let the model update whenever it is retrained | Admit a changed model only through independent qualification and legitimate authority |
| Treat memory and cache as trusted storage | Bind every remembered result to its inputs, tenant, model, policy and verifier, and refuse mismatches |
| Send every request to one large model | Choose the model, tool or verified result with measured capability for the task |
| Retry failed calls without outcome certainty | Preserve ambiguity and enforce safe retry behaviour |
| Treat model output as an execution instruction | Keep model proposals separate from cryptographic authority |
| Measure tokens and latency | Measure cost per correctly completed task |
Where each capability stands
Every capability carries one of four labels, from L100 engineering evidence: Implemented research (built and tested by its authors), Independently verified (passed L100’s separate review process — an AI review session independent of the author’s, not an external audit — for the scope stated), Integration pending (the component exists, its integration with HIVE does not yet qualify) and Target (designed, not built). A passing author-side test is never independent qualification.
| Capability | Status | Basis |
|---|---|---|
| Model intelligence: a HIVE-trained model | Target | No HIVE model has been trained for use or released. TUTU’s research works from a pinned open-weight base model; no candidate has been promoted, and none serves any task. |
| TUTU learning pipeline: sealed held-out evaluation and regression refusal | Implemented research | Implemented and tested by its authors; independent qualification not passed. |
| TUTU contamination gate | Independently verified | Qualified by L100’s separate review process, for its stated scope. |
| Champion TUTU learning allocation | Implemented research | Shadow mode only; it recommends and does not decide; independent qualification not passed. |
| Specialist TUTU areas | Target | Planned; none is active or qualified. |
| Task classification | Implemented research | Tasks are classified by category and difficulty for routing. |
| Task decomposition and multi-stage planning | Target | Designed; not built. |
| Model and tool selection by measured capability | Implemented research | Capability profiles and verifier-driven escalation, measured offline on recorded outputs; authorized-tool fallback in simulation. |
| Execution recovery: budget gate, idempotency-aware retry, Unknown outcomes | Implemented research | Fault-injection tested with simulated providers. |
| HIVE core read and write admission | Independently verified (in-core scope) | Qualified by L100’s separate review process at implementation commit 1ea6cc6, for the in-core scope only. The high-severity revocation defect that review found is reported cured within the single-process core (2e00a56, be7d509), not independently established; cross-process operation and integration with external components are not qualified. |
| Verified memory: reuse binding and fail-closed refusal | Implemented research | Unit- and concurrency-tested against stand-ins, not production Cachee. |
| Memory continuity beyond verified reuse | Target | Designed; no memory component beyond verified reuse is built. |
| Cachee integration: read/write isolation and server attestation | Integration pending | Under remediation. |
| Outcome verification by an independent verifier | Implemented research | Used for escalation and for admitting results to reuse, in research. |
| Execution-policy adaptation: held-out adoption | Implemented research | Policies are fitted, frozen and adopted only on held-out data, in research. |
| Execution-policy versioning, promotion and rollback | Target | Designed; not built. |
| Governed model change: model-file manifest and exact-commit checks | Implemented research | Implemented and reviewed in research; they enforce nothing yet, because the reviewer authority key is not provisioned. |
| Drift detection and monitoring | Target | Designed; behavioural drift of a trained model has not been measured. |
| Authority integration: Agent-008, H33-Root, H33-Key, H33-API-G, H33-74, Replay | Integration pending | A placeholder in the research build; not an operational control. |
| CPU/GPU performance estimation and batching | Implemented research | Tested in simulation; speculative execution was evaluated, was not beneficial and is not enabled. |
| Hardware placement and real-model performance | Target | Not established; no real-model result is published. |
| Closed loop from execution evidence to a qualified successor | Target | Not yet qualified. |
| Production deployment | Target | None. |
Simulated measurements are kept separate from real-model results, and no real-model result is published. Capabilities move up this table as evidence qualifies, and the architecture page’s readiness table changes first.
Designed for AI work that needs more than a model endpoint.
Enterprise AI platforms
One model and execution system that preserves tenant boundaries and operational evidence.
Autonomous agents
Designed to keep proposed actions separate from legitimate authority and verified execution.
High-volume AI services
Designed to reduce avoidable repeated computation and to manage provider failures without unsafe retries.
Regulated environments
Where authorization, evidence, replay and independently checkable results matter as much as model output.
Model development teams
Measure reasoning capability, compute efficiency and verification independently, and change a model only through qualified, evidenced transitions.
An AI model that can show how it learned, what it remembers and why it acted.
Explore how H33-HIVE is designed to bring model intelligence, governed learning, verified memory and intelligent execution together on one cryptographic foundation.
H33-HIVE is H33’s cryptographically governed AI model and intelligence system, in development in the L100 engineering program. TUTU is its learning and model-development subsystem. It is designed to compose Agent-008 for authority, Cachee for verified computational reuse, H33-Root for authority lineage, H33-Key for key custody, H33-API-G for governed execution boundaries and H33-74 for portable evidence. Technical detail: architecture and documentation. Related: AI Agent Security.