| # Where does UMA-AIF belong? | |
| Assessed against the two existing repositories. | |
| ## kaysentinel — no fit | |
| [kaysentinel](https://github.com/kaymyg/kaysentinel) is a Rust protocol-integrity | |
| runtime for blockchain state execution: it extracts canonical state mutations | |
| from Geth/Reth traces into a Structural Sufficient Representation, commits them | |
| with domain-separated BLAKE3, and checks cross-client consensus at the mutation | |
| level. | |
| Different language, different domain, different threat model. The only shared | |
| vocabulary is the word "consensus", and it means something else in each — cross- | |
| client agreement on state transitions there, agreement between reference model | |
| subspaces here. Nothing to merge. | |
| ## ames — adjacent, but deliberately closed to this class of signal | |
| [ames](https://github.com/kaymyg/ames) is the near miss, and it is worth being | |
| precise about why it is still a miss. | |
| AMES is a nine-layer containment framework: L1–L4 prevent an agent from leaving | |
| its execution environment, L5–L7 detect, L8 commits evidence to a hash-linked | |
| BLAKE3 ledger, and L9 terminates. It is Python, it is about AI agent safety, and | |
| L6 is literally called "Cognitive Drift". On the surface UMA-AIF looks like it | |
| wants to be an L6 implementation. | |
| Three properties of AMES's own design say otherwise. | |
| **1. AMES's evidence/testimony split puts UMA-AIF on the wrong side of the | |
| line.** The L8 ledger records host-observed evidence only — VM lifecycle events, | |
| image measurements, broker decisions. Guest-reported claims are quarantined into | |
| a reserved actor-ID band and excluded from evaluation. UMA-AIF reads activation | |
| tensors from inside the model the agent is running. In AMES's taxonomy that is | |
| testimony, not evidence: it is produced by the thing under observation, and a | |
| model that could manipulate its own activations to evade the auditor is exactly | |
| the adversary the framework assumes. Feeding UMA-AIF output into L8 as evidence | |
| would break the invariant the ledger exists to maintain. | |
| **2. L6 is advisory by construction.** AMES structurally prevents cognitive | |
| drift from triggering a kill — there is no parameter on the L9 path that accepts | |
| a drift finding. That is a deliberate choice, and a good one: drift is a soft | |
| signal and L9 is an irreversible action. A statistical activation auditor is a | |
| softer signal still. Merging UMA-AIF into L6 would add code that, by design, | |
| cannot affect any outcome. | |
| **3. The deployment surfaces do not overlap.** AMES contains an agent process | |
| inside an ephemeral microVM and watches it from the host. It does not need — and | |
| mostly cannot have — access to model weights or forward-pass internals, because | |
| the agents it contains typically call a model over an API that terminates | |
| outside the VM. UMA-AIF requires hooks into the transformer itself. Different | |
| side of the trust boundary, and a torch dependency that a containment runtime | |
| targeting Rust for production should not inherit. | |
| AMES also has no plugin or detector interface. Its architecture is closed on | |
| purpose, in service of "structural impossibility over monitoring". UMA-AIF is | |
| monitoring, and probabilistic monitoring at that. | |
| ## Recommendation: a separate repository, cross-referenced | |
| The two are complements, and the complementarity is worth stating explicitly in | |
| both READMEs because it is the interesting part: | |
| | | AMES | UMA-AIF | | |
| |---|---|---| | |
| | Vantage point | Host, outside the guest | Inside the forward pass | | |
| | Object of study | The agent process | The model's internal representation | | |
| | Evidence class | Host-observed | Guest-internal (testimony) | | |
| | Guarantee style | Structural impossibility | Statistical anomaly detection | | |
| | Failure mode | Availability (a kill that shouldn't fire) | Calibration (a score that means nothing) | | |
| | Enforcement | L9 terminates | Advisory; projection at most | | |
| AMES answers "did the agent leave the box". UMA-AIF asks "did the computation | |
| inside the box drift from its references". Neither subsumes the other, and each | |
| is weakened by pretending to be the other. | |
| ## The one integration worth building | |
| If a link is wanted later, the clean seam is **UMA-AIF as an evidence source for | |
| an L8-style ledger, entered in the testimony band, never in the evidence band.** | |
| Concretely: emit a per-step audit record — seed, sampled layer indices, raw | |
| metrics, calibrated scores, calibrator fingerprint — and hash-chain it with the | |
| same domain-separated BLAKE3 scheme. That gives a tamper-evident, replayable | |
| audit trail with no claim to being host-observed and no path to L9. | |
| Everything that record needs is already deterministic: `LayerSampler` and | |
| `EnvelopeNoiseInjector` are seeded, `ScoreCalibrator.state_dict()` serialises the | |
| baseline, and `test_audit_step_is_reproducible_from_seed` asserts a step can be | |
| re-derived bit-for-bit from its inputs. That was designed in for this reason. | |
| This is not implemented. It is a note about where the seam is, so that if the | |
| two projects are ever joined it happens at the boundary that respects both | |
| architectures rather than by dropping a torch dependency into a containment | |
| runtime. | |