Arbiter

A detection engine that reports what it did not check.

Most checkers answer one question: what is wrong? When they return nothing, you cannot tell whether they looked and found nothing, or never looked at all. Those two results are printed identically, and only one of them is good news.

Arbiter separates them. Every evaluation that declines to run is recorded — with a machine-readable reason — alongside the findings, and every pass reports how many evaluations it attempted. A clean result means these invariants were tested and held, and it is distinguishable from nothing was testable.

The envelope

A detection pass returns findings, declines, and a denominator:

findings              what was found
not_checked           what was NOT evaluated, and why
checked.invariants    how many (axiom, entity, indicator) evaluations were tried

Every envelope carries meta.schema_version. It names the WIRE SHAPE, not the package version, and it moves only when a reader that worked stops working — adding a key does not move it. COMPATIBILITY.md says what a patch release may change and what waits, and schema/envelope.schema.json is the shape machine-readable. The reason all three exist: the describe payload's nesting moved once between releases with no signal at all, and a consumer who had written against the earlier layout got None back from a lookup — which reads as this engine does not support that rather than this moved.

not_checked entries carry a reason from a closed vocabulary of twelve — not_applicable, insufficient_samples, missing_property, no_current_value, missing_config, missing_entity_type, missing_role, no_threshold, precondition_unmet, undefined_for_values, wrong_indicator_type, checker_error — so a decline is data, not a log line.

Three of them will account for most of what you see. insufficient_samples reports both the count it had and the count it needed, so it tells you how much longer to collect. missing_role means an axiom needed to know what KIND of quantity an indicator is — a count, a percentage, a ratio, a latency — and the model never said. Declare role: on the indicator. The engine does not guess it from the indicator's NAME, so this decline is about the declaration and never about the spelling.

no_current_value is the newest and the reason it exists is worth stating. A threshold axiom reads Entity.properties; a temporal axiom reads observation history. Feed only the second and the value is genuinely present and genuinely unreadable by the checker that wants it — and until 2026-08-16 that said missing_property, which told a caller holding sixty observations of a property that there was no value for it. It now names the count and which store it is in. That was reported from outside, and the vocabulary was the thing at fault: a closed set missing a member does not raise, it reclassifies the case as the nearest member and reports it with confidence.

Why the denominator matters. Findings and declines do not sum to the total: an evaluation that ran and found nothing appears in neither. Without checked.invariants, the statement checked N invariants has no honest value of N — and an envelope reporting a fabricated denominator is the exact failure the envelope exists to prevent.

All eight axiom checkers emit declines (33 call sites). This is not a property of one checker that the others aspire to.

The eight axioms

Declared per-indicator in a domain model, not in code:

BOUNDEDNESS takes a floor as well as a ceiling. warning: and critical: are ceilings; lower_warning: and lower_critical: are floors, and declaring both pairs on one indicator gives you a band. A fan that must not stop declares lower_critical: 1000 and gets back speed_rpm is below critical threshold with the reading it was given — no negated property, no translation layer. Whether you should declare a floor at all is a different question, and MODELING.md answers it: transcribe one when a datasheet or a contract gives you the number, and reach for HOMEOSTASIS when nobody has.

CONSISTENCY answers two separate questions. By default it range-checks a value against what its role: permits — a count is not negative, a percentage is within 0-100 — and that rule reads one indicator and nothing else. Declaring consistency: {agrees_with: [other_property]} adds the second: two readings the model says are redundant have to match, within a tolerance: (relative) or tolerance_absolute:. Redundancy is declared because nothing about two numbers reveals that they measure the same thing. A named peer the entity does not carry is reported in not_checked rather than skipped.

Redundancy is a claim about the system, not an inference from naming — and populating agrees_with from a naming convention is the way this check fails. Two channels of one part are the tempting pair and often the wrong one: a temperature sensor exposing Name and Name1 may be reporting its own die and an external diode, which differ by tens of degrees on a healthy board. Pair them and the engine will faithfully report disagreement between two readings that were never supposed to agree — a false finding on every working machine, produced by a configuration rather than by a fault. The same is true of consecutive record numbers, matching suffixes, and anything else derivable from a string: if a rule can generate the pairs, it does not know the pairs. They have to come from someone who knows the system, and they should be versioned and pinned like any other declaration. This is not hypothetical — it was reported by an integrator who caught the suffix rule in review before it shipped.

- name: cpuUsageNanoCores
  type: NUMERIC
  axioms: [STABILITY, BOUNDEDNESS, HOMEOSTASIS]
  warning: 3
  critical: 10
  window: 1h

An empty axioms: [] is meaningful — the values flow into observation history without a per-cycle check. Silence is a declaration here, not an omission.

role: — two axioms need to know what kind of quantity they are reading

RESPONSIVENESS and CONSISTENCY carry rules about a quantity, not about an entity: a deadline applies to a latency, and 0 <= x <= 100 applies to a percentage. Declare which:

- name: setpoint_error_pct
  role: latency            # latency | count | percentage | ratio
  axioms: [RESPONSIVENESS]
  warning: 5
  critical: 12

Leave it out and the axiom declines missing_role, whatever the indicator is called. The engine used to infer one from the name — response/latency for the first, count/percent/pct/ ratio for the second — and that was a guess about English: two indicators identical in every declared respect were treated differently because of their spelling, and no surface said which had happened. Worse for a reader, the ABSENCE of the decline was not evidence a role had been supplied. It was evidence about the name. An indicator called pulldown_error_c could declare RESPONSIVENESS, be accepted, be listed by model_describe, and never once evaluate.

You do not have to run a cycle to find that out. model_describe reports unreachable_declarations — every declared (indicator, axiom) pair that cannot fire under any input, each with the remedy — and the loader logs the same list. An empty list is the target.

expect_variation: — a reading that stopped moving is not a reading

A sensor frozen at its last value passes every threshold it is under, and STABILITY measures oscillation, so a flat line scores as the most stable input there is. Until 2026-08-16 a dead sensor and a live one produced byte-identical envelopes. Declare that a quantity should move:

- name: speed_rpm
  axioms: [STABILITY, BOUNDEDNESS]
  expect_variation: true
  window: 30m

Then a series that never changes across the window is a finding, frozen_series:<indicator>, naming the value and the count.

STABILITY in that axioms: list is load-bearing, and the field is inert without it. STABILITY is the axiom that reads the series, so an indicator declaring expect_variation: true alongside only threshold axioms gets no finding and no decline — which is indistinguishable from a healthy sensor, and is the thing this field exists to end. Copying the block above works; editing an indicator you already have is where it bites. model_describe names it: unread_fields lists every declared field whose consuming axiom is absent, with the remedy. That check exists because this was reported from outside the day after the field shipped.

It also names a key the engine does not read at all, which is the case that catches a typo. Each row carries a reason: axiom_not_declared for the above, and unknown_key for a key that is not in the schema — with a did_you_mean where one is close. expect_variaton: true is accepted by YAML, read by nothing, and would otherwise leave exactly the silence the field was added to end.

And it names properties you send that nothing reads. unread_properties lists numeric entity properties for which no indicator is declared. It reports what arrived; it does not judge the value, because deciding what a number means needs a rule and this engine takes rules from your model rather than from the property's name. Earlier versions did guess: a key spelled *_count or *_pct was range-checked whether or not you asked. That is gone — declare role: and the axiom to get those checks — and this report is how you find the properties that declaration is missing from.

check carries that report too, and the reason is the guess it replaced. Removing the guess withdrew a check: a value that used to raise impossible_value off its property's spelling now raises nothing. A withdrawn check that says nothing is indistinguishable from one that passed, which is the single thing COMPATIBILITY.md forbids a patch release from doing — so the population rides on check as well, and a run whose faults moved into undeclared properties reads as a run with something unlooked-at rather than as a clean one. It is a report, not a decline: a decline record names an indicator and an axiom, and a property nobody declared has neither.

Leave it out and nothing is reported, and that silence is the design rather than a gap. Whether a constant series is a fault is a question about your domain and not about the number: a CPU temperature that never moves is broken, and a replica count, a nominal setpoint and a switched-off pump are all correctly flat. The engine cannot tell those apart and does not try. You can.

The axioms reading the value are not suppressed when this fires. A sensor frozen above its critical threshold still raises that alarm; you get both, and can judge the threshold verdict knowing the input behind it is dead.

Quickstart

pip install arbiter-engine          # requires numpy and pyyaml, and nothing else

python3 -c "
from importlib.resources import files
from arbiter_engine.api import EngineSession, model_describe

s = EngineSession()
s.load_model(files('arbiter_engine').joinpath('examples/water_tank.yaml').read_text())
print(model_describe(s).to_dict()['checked'])"

The example is read out of the INSTALLED PACKAGE rather than off a relative path, and that detail is load-bearing rather than stylistic. A wheel ships only what lives under the package directory, so the copy at examples/ in this repository reaches the source distribution and not the wheel. This block used to open examples/water_tank.yaml directly: correct from a clone, and FileNotFoundError for anyone who installed the package instead — a failure that could not appear until the install line above stopped saying git clone. Both copies are here, written from one source: examples/ for reading, the packaged one for running.

That prints {'invariants': 0, 'entities': 3, 'declared_invariants': 11} — three entities, eleven declared invariants, and zero evaluated, because no observations have been supplied yet. The zero is the point: it is reported rather than left for you to infer from an empty finding list.

Everything above is on the supported surface. Until 2026-08-11 this example imported load_domain from a deep module path — which works, and which this same README calls importable and unsupported three sections down. The first thing a reader runs should not be the one thing the document tells them not to depend on.

examples/water_tank.yaml is a deliberately synthetic two-tank water system that declares all eight axioms in one file, so it doubles as the schema reference. It is not one of the curated domain models, which are not published — and neither kubernetes_node.yaml nor battery_pack.yaml is one of those either, though all three ship here — and reading it is the fastest way to learn the shape. kubernetes_node.yaml and battery_pack.yaml are the same kind of thing on domains where a floor and a band carry the weight.

Dependencies are two, and that was measured rather than assumed. numpy and pyyaml are required. scipy and rdflib are extras ([confidence], [rdf]) because they are reached only through two deep modules that the public API never touches — so the naive reading of the import list says four, and the measurement says two.

The public API

11 names. Everything else in the package is importable and unsupported — reaching for a deeper path is legitimate and unpromised, and those paths may move without a major version.

from arbiter_engine import (
    TopologyTraverser,          # the kernel: problem-solving as graph traversal
    UnifiedAxiomReasoner,       # evaluates axioms, produces the envelope
    DomainModel,                # your YAML, loaded
    InMemoryObservationHistory, # a concrete history, so it runs without a store
    Entity, Problem, RelationshipGraph, Observation, Axiom, Severity,
    api,                        # the tool surface — see below
)

Ten of those are types and the kernel; the eleventh is a module, and the split is deliberate. arbiter_engine.api is the tool surface: five verbs over a session, each returning the envelope above.

from importlib.resources import files
from arbiter_engine.api import EngineSession, model_describe, check, traverse, gaps, attest

session = EngineSession()
session.load_model(files("arbiter_engine").joinpath("examples/water_tank.yaml").read_text())
model_describe(session)   # what is declared: entity types, indicators, axioms
check(session)            # evaluate the declared invariants over supplied observations
gaps(session)             # what the model says should exist and nothing has been observed

Read out of the installed package again, for the reason given above. This block opened the relative path until 0.1.6, which is the failure that paragraph describes, forty lines further down the same document — so 0.1.5 ships a project page whose second code block raises FileNotFoundError for anyone who installed it. Found by running the README that shipped inside the wheel, from a directory with no repository in it, rather than a rewritten version of it.

Three kinds of input, one feeder each. A session takes the current value of a property, the series behind it, and the edges between entities — and every axiom reads one or both of the first two, except CONNECTIVITY, which reads only the third.

session.add_entity("pump1", "Pump", properties={"speed_rpm": 2900})
session.add_observations("pump1", "speed_rpm", [2900, 2905, 2890, ...])
session.add_relationship("pump1", "feeds", "header")   # source, relation, target

The first two are easy to conflate and worth separating deliberately: threshold checks read the entity's current properties, and the temporal axioms read observation history. Supplying one and not the other is the commonest way to get a clean result over a value that is plainly out of range — the threshold checker never saw it, because the current value lives on the entity.

Omit the third and CONNECTIVITY will report a missing relationship, which is correct: a model that declares a pump must feed a tank is asserting something, and an absent edge falsifies it. That finding is not a complaint that you forgot to load edges — the engine cannot tell those apart, so it reports what the model asserted and lets you decide which it was.

They are a supported contract, and they are listed here as one name rather than six because they serve a different caller: an agent invoking tools, not a library user composing objects. check is not a peer of Entity, and flattening them into one namespace would say it was. The module is the promise; its membership is documented here and does not change inside a minor version.

The same five are exposed over MCP by arbiter_engine.mcp.server, which is a thin transport over exactly these functions and needs the optional mcp extra. That module is a deep path — importable, and not part of the eleven.

What is not here, and why

The engine is open. The knowledge and the operations are not.

• Domain models. The engine reads them; the curated packs are not published. The mechanism is the contribution — the models are the accumulated work. The three files in examples/ are synthetic teaching models, deliberately not among them.
• The operator half. Clinic, planning, the Kubernetes executor, the introspection layer. These are welded to a running deployment and are not v0.1.
• Two lazy imports reach outside the cut, and they behave differently. One root-cause wiring module and an LLM client are imported lazily and are not shipped, so the package still imports cleanly. The root-cause wiring degrades to a no-op — its callsite is guarded and the feature it reports is optional telemetry. The LLM path raises, with a message saying so; it is reachable only through NLTraversalTranslator, which is not part of the supported surface, and the deterministic translate() needs no client. Both measured by running them, not read off the imports.

Status

v0.1. 61 Python files, 59 modules importing on the declared dependencies alone, 11 supported names — counted in this repository, which is the package you are holding.

That basis is stated because it is easy to get wrong in a way nobody notices. The build adds one __init__.py per package level, so a count taken before the build is smaller than the package you are holding — and this line published the smaller figure until 2026-08-12, where any reader could falsify it with find . -name '*.py' | wc -l. A checkable false claim, in the Status section of a project whose subject is checkable claims. Count the artifact, never an earlier stage of it.

The import figure carries the same hazard one layer down, and it depends on what you have installed. Sweeping the package where scipy happens to be present imports 60; on the declared dependencies alone it is the 59 above, because propagation.lp_confidence is the one module that needs scipy and it is a deep path outside the supported surface. Count the artifact in the state the reader will have it, not in the state the person measuring happens to be standing in — this line quoted the with-scipy figure until 2026-08-12, which no reader installing normally could reproduce.

And the count is of SUBMODULES: the root package is not one of them. Walking arbiter_engine for what it contains gives 59; adding the package you imported to reach them gives 60. Both are honest and they are answers to different questions, so a reader who recounts and gets one more has not found a defect — they have used the other convention. Stated because someone did exactly that from outside, and a number published without its predicate can only be agreed with or disagreed with, never checked.

Honest boundaries, stated because you would otherwise find them yourself:

• PREDICT is plumbed but unfed. The traversal mode exists and nothing produces projected values outside a test. It is not a working forecast.
• Three worked examples ship, not a library of them. Modelling a real system is your work.
• Stage I and Stage II of this project are archived, not running. Anything describing them as production is out of date.

How fast is it

Measured on this project's development machine, so treat the shape of the curve as the claim and the absolute numbers as an illustration:

Four indicators per entity across five axioms, 40 observations per series, five runs per size, median reported. It is linear in evaluations, and the per-evaluation cost does not degrade with scale — that is the part worth knowing, and it is the part that does not depend on the machine.

Getting a model and its data in is excluded from that table, and it is three costs on two axes. Feeding entities scales with the entities:

Loading a model scales with the model, and the parse in front of it is the larger cost:

Three things follow, and the third is the one that saves anybody time. The feed is paid for whatever data is added, so a consumer re-feeding every cycle pays it every cycle. load_model() is flat in the entity count, so a session held across cycles pays it once — but it is not flat in the model, and quoting it from a four-indicator fixture is how it gets called negligible. And load_model() takes a mapping, not a file: at 180 indicators the YAML parse in front of it costs about thirty times the load, so a consumer paying a quarter-second to get a generated model in is mostly paying PyYAML. Cache the parsed mapping rather than only the session, and use yaml.CSafeLoader where libyaml is installed — same result, several times faster.

Re-derive it rather than trusting the tables. The two axes are separate arguments, because they are separate questions:

python3 -m arbiter_engine.scripts.benchmark_check --sizes 10,100,1000 --model-sizes 4,40,180,360 --repeat 5

Documentation

Three worked models ship in examples/: water_tank.yaml declares all eight axioms and doubles as the schema reference, kubernetes_node.yaml is the smallest domain where a band matters, and battery_pack.yaml is one where nearly every bound is a floor somebody published.

Built on this engine: bmc-sensor-audit audits firmware sensor coverage; fleet-sensor-baseline aggregates its output across a fleet. Both are by this author rather than independent adopters, so take them as worked examples of the shape in BRIDGES.md and not as evidence anyone else has done it.

Evidence and technical write-ups live in evidence/ — architecture, deployment runbook, fault-scenario catalogue, and the observation logs from the closed-loop alpha, including the findings that went against us.

Licence

Apache License 2.0. See LICENSE and NOTICE.

TRADEMARK.md is separate and narrower: Apache Section 6 withholds any trademark grant, and that file says what use of the name is permitted. Arbiter is a project name, not a licence grant.

Contributing

See CONTRIBUTING.md. Adversarial findings are the most useful thing you can send: if the engine reports a clean pass over something it did not actually evaluate, that is the bug this project most wants to hear about.