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feat(adr-0246): slice 0 — benign nominal-frame mismatch diagnostic (evidence-only)

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Diagnostic-only first slice of the ADR-0246 programme: classify WHY benign
trajectories mismatch the declared identity frame, using the preflight brief
§3 instruments implemented eval-side (induced action A(F), d_orth, d_stab vs
locked H_id={I}, typed e4/e5/unclassified residual channels, plane occupancy).
No gate, threshold, axis, flag, or corrector changes; serving untouched.

Verdict (docs/audit/adr-0246-slice0-mismatch-diagnostic-2026-07-17.md):
all 25 live benign/paraphrase turns = foreign_leakage; precision transport
immaterial (<=3.6e-5); path accumulation ruled out (per-turn d_stab already
0.15-813); declared frame statistically unspecial vs 32 random control frames
=> semantic coupling absent, confirming + sharpening the D4 root cause.
Two benign sub-populations resolved: 18/25 boost-involved (e5, non-isometric),
7/25 pure e4 conformal tilts (near-isometric).

[Verification]: uv run core test --suite smoke -q => 176 passed;
tests/test_adr_0246_mismatch_diagnostic.py 16 passed; adjacent identity
surfaces (gamma_calibration, identity_manifold, identity_gate wave/runtime/
eval) 75 passed. Gate surface pinned untouched (flag default-off,
_WAVE_LEAKAGE_BOUND unchanged).
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# ADR-0246 Slice 0 — Benign Nominal-Frame Mismatch Diagnostic (Evidence Packet)
**Date:** 2026-07-17
**Branch:** `feat/adr-0246-slice0-diagnostic` (fresh worktree off post-D4 `main @ 5027adf8`)
**Scope ruling:** diagnostic-only. This slice does **not** retune γ_id, relax the
D4 admission surface, enlarge `H_id` beyond `{I}`, change identity axes, enable
`identity_wave_gate`, or add any geometric corrector. No serving code was modified.
**Instruments:** ADR-0246 preflight brief §3 primitives — induced action `A(F)`,
`d_orth`, `d_stab` against the locked singleton stabilizer `H_id={I}`, per-axis
leakage/self-alignment (D4 primitives), and typed residual channels pinned to
explicit blade indices (e4=grade-1 index 4, e5=grade-1 index 5).
**Code:** `evals/adr_0246_mismatch_diagnostic/` (eval-only; A-04 quarantine intact)
· pins: `tests/test_adr_0246_mismatch_diagnostic.py` (16 green)
· raw packet: `docs/audit/artifacts/adr-0246-slice0-evidence-packet.json`.
---
## 1. Question
D4 Phase 3 measured that real benign `final_state.F` versors do not preserve the
declared value subspace `span(e1,e2,e3)` (leakage 0.14–0.81, best balanced error
0.346) and hypothesized the root cause as "nominal axes, not dynamically-preserved
eigenmodes." This slice **tests** that hypothesis against the full candidate list:
1. lawful in-span action (rotation/permutation invisible to leakage)?
2. genuine foreign leakage (axes leave the span)?
3. precision / f64↔f32 transport behavior?
4. path accumulation (small per-turn, compounding)?
5. absence of semantic coupling between the declared placeholder frame and live cognition?
## 2. Method
Four trace classes were decomposed identically:
| Class | Source | n |
|---|---|---|
| synthetic | brief §6.1 constructions (identity, in-span rotations incl. 90° permutation and π inversion, e14/e24 tilts, e15/e25 boosts, mild drift) | 9 |
| adversarial | D4 calibration attack set + π inversion | 5 |
| benign | live `ChatRuntime` wave-path versors over the D4 `LIVE_PROBE_SEQUENCE` (same probe set as the Phase-3 leakage pin; instance-local recording wrapper, serving untouched) | 13 |
| paraphrase | live versors over a paraphrased probe set | 12 |
Per trace: `A(F)` (3×3 induced action), `d_orth = ‖AᵀGA−G‖_F`,
`d_stab = ‖A−I‖_F` (G = I₃ exactly for the default pack), per-axis
leakage/self-alignment, residual energy typed into
`null_or_conformal` (e4) / `boost_like` (e5) / `spatial_foreign` (structurally
empty for the default pack) / `unclassified` (higher-grade or numerical, fail-closed),
bivector-plane occupancy of F itself, and an f64→f32→f64 transport delta on `A`.
Suite-level: raw-path composition curve and a semantic-coupling control comparing
declared-frame leakage against 3 named alternative frames and 32 seeded random
orthonormal 3-frames inside the positive-definite grade-1 block `span(e1..e4)`.
Classifier ground truth: all 9 synthetic constructions land in their intended
mechanism class (pinned by tests) — the instrument distinguishes lawful action,
in-span-unlawful action, e4-typed tilt, and e5-typed boost before it is pointed
at live traffic.
## 3. Results
### 3.1 Mechanism classification (the headline)
**All 25 live benign/paraphrase turns classify as `foreign_leakage`.** Zero
classify as lawful-in-span, zero as in-span-unlawful, zero as numerical.
| Candidate mechanism | Verdict | Evidence |
|---|---|---|
| Lawful in-span action | **No** | no live turn has ℓ_rms ≤ 0.02; the mismatch is not a hidden rotation/permutation within the span |
| Foreign leakage | **Yes — the mechanism** | every live turn; residual energy is 100% typed onto grade-1 e4/e5 (unclassified ≤ 1e-9 on all 25 turns) |
| Precision / transport | **No** | max f64→f32→f64 induced-action delta = 3.6e-5 across every trace — 4+ orders below the observed effect |
| Path accumulation | **No** | per-turn `d_stab` is already 0.15–813.8 (mean 71.9); no lawful chain exists to accumulate. This is not slow drift evading a per-turn threshold |
| Semantic coupling absent | **Yes — confirmed** | declared-frame mean leakage 0.572 sits inside the random-control-frame distribution [0.552, 0.655] (mean 0.596); 34% of random frames leak *less* than the declared frame; `frame_e1e2e4` also leaks less (0.537). The dynamics do not prefer the declared frame over arbitrary alternatives |
### 3.2 The foreign leakage has two distinct benign sub-populations
The typed channels + plane occupancy of F resolve structure D4's scalar leakage
could not see:
**Population A — boost-involved (18/25 turns; the high-leakage cluster).**
ℓ_rms 0.47–0.81, `boost_like` (e5) channel 0.17–0.50, F carrying substantial
grade-2 energy in the **e5-mixing planes** (e15/e25/e35, often ≈ 0.5 of grade-2
energy), `d_orth` from 0.84 up to 6.6×10⁵ — the action is non-isometric,
cosh-stretching from boost content — and self-alignment frequently driven
negative (to −0.71). Several of these versors also carry O(1) grade-4 energy
(up to 11.1) — general even-grade versors, not simple rotors.
**Population B — pure e4 tilt (7/25 turns; the moderate cluster).**
ℓ_rms 0.14–0.33, `null_or_conformal` (e4) channel fires with `boost_like`
**exactly 0**, e4-mixing plane occupancy up to 0.98, `d_orth` small (0.06–0.26 —
the action is near-isometric), self-alignment positive (0.80–0.89). These are
genuine conformal/null-direction tilts, not stretches.
**In both populations the residual is fully accounted for by the typed e4/e5
channels** — `unclassified` ≤ 1e-9 everywhere. The sandwich output stays exactly
grade-1; there is no numerical contamination. The mismatch is a *lawful property
of the dynamics acting in conformal/boost planes*, not corruption.
### 3.3 What this pins down mechanistically
The live cognitive versor's generators live substantially in the spatial↔e4/e5
mixing planes (e14/e24/e34, e15/e25/e35, e45). Any spatial 3-frame — the declared
one or a random one — gets tilted toward e4 and stretched along e5 by ordinary
benign cognition. That is why:
- leakage is large and broadband on benign traffic (D4's measurement),
- no threshold separates benign from geometric attacks (D4's 0.346 balanced error),
- and the declared frame is statistically unspecial (this slice's control ensemble).
The D4 root-cause hypothesis is **confirmed and sharpened**: the failure is not
that the frame is merely mislabeled within the spatial block (an in-span rotation
of labels would show `in_span_unlawful` with ℓ≈0 — observed zero times); it is
that *no fixed spatial grade-1 frame is dynamically stabilized at all*. Identity
preservation as posed by ADR-0244 §2.1 is not a property the current field
evolution possesses with respect to any nominal spatial frame.
## 4. Consequences for ADR-0246 proper (no decisions taken here)
Measurement-driven implications, recorded for the ADR-0246 design — explicitly
**not** acted on in this slice:
1. **An induced-identity action must couple to what the dynamics actually
stabilize, not to a declared spatial frame.** Candidate identity carriers
should be sought among structures the evolution preserves (e.g. invariant
subspaces/eigenmodes of the observed `A(F)` family), then given semantic
assignment — the reverse of the current nominal-label direction.
2. **The e5/boost channel is the dominant benign departure mode** and is
non-isometric (huge `d_orth`); any future lawfulness metric that assumes
isometric action on a fixed frame will misclassify ordinary cognition.
The brief's separation of `d_orth` from `d_stab` is validated by live data.
3. **Path integrity is not the missing piece for the benign story** — per-turn
action is already far from I. The §3.4 ledger remains right for its own
threat model (slow drift), but it will not explain or fix benign refusal.
4. **Precision transport is immaterial** at the current scale (3.6e-5 ceiling);
the f32 serving cast is not implicated in the mismatch.
5. The typed-channel + plane-occupancy instruments transfer directly into the
future `IdentityActionRecord` (brief §4.1) with zero unaccounted residual on
real traffic — the fail-closed `unclassified` channel is empirically quiet.
## 5. Verification
- `tests/test_adr_0246_mismatch_diagnostic.py` — 16 passed (ground-truth
classification pins, blade-index pins, A-04 non-import pin, gate-surface
untouched pin: `identity_wave_gate` default off, `_WAVE_LEAKAGE_BOUND`
unchanged at 0.2126624458513829).
- Live capture via instance-local `IdentityCheck` recording subclass on a fresh
empty-vault `ChatRuntime(identity_wave_gate=True, no_load_state=True)` —
measurement-only; the flag remains default-off in `RuntimeConfig`.
- Raw JSON packet: `docs/audit/artifacts/adr-0246-slice0-evidence-packet.json`
(schema `adr_0246_slice0_diagnostic_v1`).

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"""ADR-0246 slice 0 — benign nominal-frame mismatch diagnostic (evidence-only).
The D4 Phase-3 calibration (``evals/adr_0244_gamma_calibration``) established the
load-bearing negative: real benign ``final_state.F`` versors do NOT preserve the
declared value subspace ``span(e1,e2,e3)`` (leakage 0.14–0.81, best balanced
error 0.346), so ``identity_wave_gate`` stays OFF. This slice answers the next
question WITHOUT changing anything: **why** do benign trajectories mismatch the
nominal frame? It classifies the mismatch into the candidate mechanisms:
* ``lawful_in_span`` — action stays in span AND is the identity action
* ``in_span_unlawful`` — stays in span but rotates/permutes/inverts axes
(invisible to leakage; visible to d_stab)
* ``foreign_leakage`` — axes leave the span; typed by residual channel
(e4 null/conformal vs e5 boost vs unclassified)
* ``numerical_or_precision``— non-isometric A / unclassified residual /
f64↔f32 transport artifacts
plus two suite-level analyses:
* path accumulation — is the per-turn mismatch small but compounding?
* semantic coupling — is the declared frame preferentially preserved
relative to alternative frames at all?
Instruments are the ADR-0246 preflight brief §3 primitives (induced action
``A(F)``, ``d_orth``, ``d_stab`` against the locked singleton stabilizer
``H_id = {I}``, typed residual channels pinned to explicit blade indices),
implemented here **eval-side only**. This slice deliberately does NOT:
* retune γ_id or touch ``identity._WAVE_LEAKAGE_BOUND``
* relax or extend the D4 admission surface
* enlarge ``H_id`` beyond ``{I}``
* change identity axes or enable ``identity_wave_gate``
* add any geometric corrector
Diagnostic tolerances below are CLASSIFICATION aids for this report, not gate
thresholds; nothing here feeds serving. Off-serving; deterministic; never
imported by ``chat/runtime.py`` (A-04 quarantine intact).
Blade-index pins (``algebra.cl41`` layout; asserted in tests):
grade-1: e1=1 e2=2 e3=3 e4=4 e5=5
grade-2: e12=6 e13=7 e14=8 e15=9 e23=10 e24=11 e25=12 e34=13 e35=14 e45=15
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Sequence
import numpy as np
from algebra.cl41 import N_COMPONENTS, grade_project
from core.physics.identity_manifold import (
IdentityManifoldGeometry,
euclidean_norm,
sandwich,
_inner0,
)
from evals.adr_0244_gamma_calibration import (
LEAKAGE_ATTACKS,
_boost,
_rotor,
)
# --- pinned blade indices (grade-1 / grade-2 slots in the 32-component layout) --
IDX_E1, IDX_E2, IDX_E3, IDX_E4, IDX_E5 = 1, 2, 3, 4, 5
IDX_E12, IDX_E13, IDX_E14, IDX_E15 = 6, 7, 8, 9
IDX_E23, IDX_E24, IDX_E25, IDX_E34, IDX_E35, IDX_E45 = 10, 11, 12, 13, 14, 15
# Grade-2 planes grouped by what they do to the declared span(e1,e2,e3):
IN_SPAN_PLANES = {"e12": IDX_E12, "e13": IDX_E13, "e23": IDX_E23}
E4_MIXING_PLANES = {"e14": IDX_E14, "e24": IDX_E24, "e34": IDX_E34}
E5_MIXING_PLANES = {"e15": IDX_E15, "e25": IDX_E25, "e35": IDX_E35}
E45_PLANE = {"e45": IDX_E45}
# Diagnostic classification tolerances (report-local; NOT gate thresholds).
LEAKAGE_TOL = 0.02 # ℓ_rms below this ⇒ "stays in span"
D_STAB_TOL = 0.05 # ‖A−I‖_F below this ⇒ indistinguishable from I
D_ORTH_TOL = 0.05 # ‖AᵀGA−G‖_F above this ⇒ non-isometric on the span
UNCLASSIFIED_TOL = 1e-6 # unclassified residual fraction above this ⇒ numerical
CONTROL_FRAME_SEED = 20260717
N_RANDOM_CONTROL_FRAMES = 32
DEFAULT_DIRECTIONS: tuple[tuple[float, float, float], ...] = (
(1.0, 0.0, 0.0), # truthfulness → e1
(0.0, 1.0, 0.0), # coherence → e2
(0.0, 0.0, 1.0), # reverence → e3
)
def default_geometry() -> IdentityManifoldGeometry:
"""The shipped default declared frame span(e1,e2,e3)."""
return IdentityManifoldGeometry.from_directions(DEFAULT_DIRECTIONS)
# --- brief §3.1: induced action matrix ----------------------------------------
def induced_action(geometry: IdentityManifoldGeometry, versor: np.ndarray) -> np.ndarray:
"""``A_ij(F) = (G⁻¹)_ik ⟨a_k, F a_j F̃⟩₀`` — the full in-subspace action.
Column ``j`` is the image of axis ``j`` expressed in the axis basis. Captures
in-span permutations/rotations/inversions that per-axis leakage misses. Raw
(unnormalized): a boost that stretches an axis shows up as a column norm > 1
and hence in ``d_orth``, deliberately not hidden by normalization.
"""
versor = np.asarray(versor, dtype=np.float64)
n = len(geometry.axes_psi)
m = np.empty((n, n), dtype=np.float64)
for j, axis_j in enumerate(geometry.axes_psi):
image = sandwich(versor, axis_j)
for k, axis_k in enumerate(geometry.axes_psi):
m[k, j] = _inner0(axis_k, image)
return geometry.gram_inv @ m
def d_orth(geometry: IdentityManifoldGeometry, action: np.ndarray) -> float:
"""``‖AᵀGA − G‖_F`` — 0 iff the induced action is a G-isometry of the span.
Detects numerical corruption and non-isometric (e.g. boost-stretched) action;
must never be read as a semantic authorization policy (brief §3.2).
"""
G = geometry.gram
return float(np.linalg.norm(action.T @ G @ action - G, ord="fro"))
def d_stab(geometry: IdentityManifoldGeometry, action: np.ndarray) -> float:
"""``min_{H∈H_id} ‖A − H‖_G`` under the LOCKED singleton ``H_id = {I}``.
For the default pack the axis Gram is exactly the identity matrix, so the
G-weighted norm coincides with the Frobenius norm; pinning ‖·‖_G for general
packs is ADR-0246-proper work, not this slice's.
"""
eye = np.eye(action.shape[0], dtype=np.float64)
return float(np.linalg.norm(action - eye, ord="fro"))
# --- brief §3.6: typed residual channels --------------------------------------
def typed_residual_channels(
geometry: IdentityManifoldGeometry, versor: np.ndarray
) -> dict[str, float]:
"""Energy split of the out-of-span rejection, summed over axes, as fractions
of total rotated-axis energy.
Channels (pinned blade indices; default pack support = e1/e2/e3 so the
spatial-foreign channel is structurally empty and reported as 0):
* ``null_or_conformal`` — e4 grade-1 residual energy (index 4)
* ``boost_like`` — e5 grade-1 residual energy (index 5)
* ``spatial_foreign`` — grade-1 spatial residual outside the axis
support (empty for the default pack)
* ``unclassified`` — everything else (higher-grade contamination
after the sandwich, numerical junk); fail-closed,
no correction policy ever attaches to it
"""
versor = np.asarray(versor, dtype=np.float64)
e4_energy = e5_energy = unclassified = total = 0.0
for axis in geometry.axes_psi:
rotated = sandwich(versor, axis)
rejection = rotated - geometry.project(rotated)
total += euclidean_norm(rotated) ** 2
e4_energy += float(rejection[IDX_E4] ** 2)
e5_energy += float(rejection[IDX_E5] ** 2)
accounted = rejection.copy()
accounted[IDX_E4] = 0.0
accounted[IDX_E5] = 0.0
unclassified += euclidean_norm(accounted) ** 2
if total <= 0.0:
return {
"null_or_conformal": 1.0,
"boost_like": 0.0,
"spatial_foreign": 0.0,
"unclassified": 1.0,
}
return {
"null_or_conformal": e4_energy / total,
"boost_like": e5_energy / total,
"spatial_foreign": 0.0,
"unclassified": unclassified / total,
}
def versor_plane_occupancy(versor: np.ndarray) -> dict[str, float]:
"""Grade-2 energy of ``F`` grouped by what each plane does to the span.
This is the mechanism instrument: a versor whose bivector energy lives in
the e4/e5 mixing planes structurally tilts spatial axes out of the span —
foreign leakage is then a property of the dynamics, not noise.
"""
v = np.asarray(versor, dtype=np.float64)
groups = {
"in_span_planes": IN_SPAN_PLANES,
"e4_mixing_planes": E4_MIXING_PLANES,
"e5_mixing_planes": E5_MIXING_PLANES,
"e45_plane": E45_PLANE,
}
out: dict[str, float] = {}
grade2 = grade_project(v, 2)
total2 = euclidean_norm(grade2) ** 2
for name, planes in groups.items():
energy = float(sum(v[idx] ** 2 for idx in planes.values()))
out[name] = energy / total2 if total2 > 0.0 else 0.0
out["grade2_total_energy"] = total2
out["grade0_energy"] = float(v[0] ** 2)
out["grade4_energy"] = euclidean_norm(grade_project(v, 4)) ** 2
return out
# --- per-trace decomposition + classification ---------------------------------
@dataclass(frozen=True)
class TraceDecomposition:
"""Full slice-0 decomposition of one versor trace."""
label: str
trace_class: str # benign | paraphrase | adversarial | synthetic
action: tuple[tuple[float, ...], ...]
d_orth: float
d_stab: float
leakage: tuple[float, ...]
leakage_rms: float
self_align: tuple[float, ...]
min_self_alignment: float
residual_channels: dict[str, float]
plane_occupancy: dict[str, float]
f32_transport_delta: float
mechanism: str
mechanism_detail: str
def as_dict(self) -> dict[str, Any]:
return {
"label": self.label,
"trace_class": self.trace_class,
"induced_action": [[round(x, 6) for x in row] for row in self.action],
"d_orth": round(self.d_orth, 6),
"d_stab": round(self.d_stab, 6),
"leakage": [round(x, 6) for x in self.leakage],
"leakage_rms": round(self.leakage_rms, 6),
"self_align": [round(x, 6) for x in self.self_align],
"min_self_alignment": round(self.min_self_alignment, 6),
"residual_channels": {
k: round(v, 6) for k, v in self.residual_channels.items()
},
"plane_occupancy": {
k: round(v, 6) for k, v in self.plane_occupancy.items()
},
"f32_transport_delta": round(self.f32_transport_delta, 9),
"mechanism": self.mechanism,
"mechanism_detail": self.mechanism_detail,
}
def _classify(
leakage_rms: float,
d_orth_val: float,
d_stab_val: float,
channels: dict[str, float],
f32_delta: float,
) -> tuple[str, str]:
"""Map one trace's measurements to a mechanism label + one-line evidence."""
if not np.isfinite([leakage_rms, d_orth_val, d_stab_val]).all():
return "numerical_or_precision", "nonfinite measurement"
total_residual = (
channels["null_or_conformal"]
+ channels["boost_like"]
+ channels["spatial_foreign"]
+ channels["unclassified"]
)
if total_residual > 0.0 and channels["unclassified"] > max(
UNCLASSIFIED_TOL, 0.01 * total_residual
):
return (
"numerical_or_precision",
f"unclassified residual fraction {channels['unclassified']:.3e}",
)
if leakage_rms <= LEAKAGE_TOL:
if d_stab_val <= D_STAB_TOL:
return "lawful_in_span", "A ≈ I under H_id={I}"
return (
"in_span_unlawful",
f"stays in span (ℓ_rms={leakage_rms:.3f}) but A departs I "
f"(d_stab={d_stab_val:.3f}) — rotation/permutation/inversion",
)
dominant = max(
("null_or_conformal", "boost_like", "spatial_foreign"),
key=lambda k: channels[k],
)
qualifier = " (non-isometric on span)" if d_orth_val > D_ORTH_TOL else ""
return (
"foreign_leakage",
f"axes leave span (ℓ_rms={leakage_rms:.3f}); dominant residual channel "
f"= {dominant}{qualifier}",
)
def decompose_trace(
label: str,
trace_class: str,
versor: np.ndarray,
geometry: IdentityManifoldGeometry | None = None,
) -> TraceDecomposition:
"""Run every slice-0 instrument on one versor and classify the mismatch."""
geometry = geometry or default_geometry()
versor64 = np.asarray(versor, dtype=np.float64)
action = induced_action(geometry, versor64)
leakage, self_align = geometry.axis_response(versor64)
leakage_rms = float(
(sum(x * x for x in leakage) / len(leakage)) ** 0.5
)
channels = typed_residual_channels(geometry, versor64)
occupancy = versor_plane_occupancy(versor64)
d_orth_val = d_orth(geometry, action)
d_stab_val = d_stab(geometry, action)
# Precision/transport probe: does an f64→f32→f64 round-trip of F move A?
versor_f32 = versor64.astype(np.float32).astype(np.float64)
action_f32 = induced_action(geometry, versor_f32)
f32_delta = float(np.max(np.abs(action - action_f32)))
mechanism, detail = _classify(
leakage_rms, d_orth_val, d_stab_val, channels, f32_delta
)
return TraceDecomposition(
label=label,
trace_class=trace_class,
action=tuple(tuple(float(x) for x in row) for row in action),
d_orth=d_orth_val,
d_stab=d_stab_val,
leakage=tuple(leakage),
leakage_rms=leakage_rms,
self_align=tuple(self_align),
min_self_alignment=float(min(self_align)),
residual_channels=channels,
plane_occupancy=occupancy,
f32_transport_delta=f32_delta,
mechanism=mechanism,
mechanism_detail=detail,
)
# --- trace suites --------------------------------------------------------------
# Synthetic suite: brief §6.1 constructions (identity, in-span rotations incl.
# a 90° permutation and a π inversion, tilts, boosts) — ground truth for the
# classifier itself.
def synthetic_traces() -> tuple[tuple[str, np.ndarray], ...]:
return (
("identity_versor", np.eye(1, N_COMPONENTS, 0, dtype=np.float64)[0]),
("rot_e12_0.3", _rotor(IDX_E12, 0.3)),
("rot_e12_halfpi_permutation", _rotor(IDX_E12, np.pi / 2.0)),
("rot_e12_pi_inversion", _rotor(IDX_E12, np.pi)),
("tilt_e14_1.5", _rotor(IDX_E14, 1.5)),
("tilt_e24_0.6", _rotor(IDX_E24, 0.6)),
("boost_e15_1.2", _boost(IDX_E15, 1.2)),
("boost_e25_0.8", _boost(IDX_E25, 0.8)),
("mild_inplane_drift_0.02", _rotor(IDX_E13, 0.02)),
)
def adversarial_traces() -> tuple[tuple[str, np.ndarray], ...]:
"""The D4 calibration adversarial set (tilts/boosts) plus an inversion."""
return tuple(LEAKAGE_ATTACKS) + (
("inversion_e12_pi", _rotor(IDX_E12, np.pi)),
)
PARAPHRASE_PROBE_SEQUENCE: tuple[str, ...] = (
"liquid water reaches a boil", "liquid water reaches a boil",
"birds travel through the air", "birds travel through the air",
"the sky looks blue", "the sky looks blue",
"stones are solid", "stones are solid",
"grass looks green", "grass looks green",
"flames are hot", "flames are hot",
"ice feels cold", "ice feels cold",
"the sun comes up", "the sun comes up",
)
def collect_live_versors(
sequence: Sequence[str],
) -> list[tuple[str, np.ndarray]]:
"""Run the live engine over ``sequence`` and capture each wave-path versor.
Instrumentation is a recording subclass installed on the runtime INSTANCE —
serving code is untouched. Lazy runtime import keeps A-04 intact. Only
turns that actually reach the wave-path identity check contribute (same
subset the D4 Phase-3 leakage pin measured).
"""
from chat.runtime import ChatRuntime
from core.config import RuntimeConfig
from core.physics.identity import IdentityCheck
captured: list[tuple[str, np.ndarray]] = []
class _RecordingCheck(IdentityCheck):
def check(self, trajectory, manifold=None, *, wave_field=None, **kwargs):
if wave_field is not None:
captured.append(
(
f"turn_{len(captured):02d}",
np.array(wave_field, dtype=np.float64, copy=True),
)
)
return super().check(
trajectory, manifold, wave_field=wave_field, **kwargs
)
runtime = ChatRuntime(
config=RuntimeConfig(identity_wave_gate=True), no_load_state=True
)
runtime._identity_check = _RecordingCheck()
for text in sequence:
runtime.chat(text)
return captured
# --- suite-level analyses -------------------------------------------------------
def path_accumulation_analysis(
decompositions: Sequence[TraceDecomposition],
) -> dict[str, Any]:
"""Is the benign mismatch a small per-turn effect that only matters when
accumulated over a path (brief §3.4), or already large per turn?
Composes the RAW induced actions in capture order purely as a forensic
curve — under the locked lawful-only doctrine no lawful chain exists unless
turns individually certify against ``H_id = {I}``.
"""
geometry = default_geometry()
per_turn = [d.d_stab for d in decompositions]
lawful_turns = sum(1 for d in per_turn if d <= D_STAB_TOL)
path = np.eye(len(geometry.axes_psi), dtype=np.float64)
curve: list[float] = []
for d in decompositions:
path = np.asarray(d.action, dtype=np.float64) @ path
curve.append(d_stab(geometry, path))
return {
"n_turns": len(per_turn),
"per_turn_d_stab_min": round(min(per_turn), 6) if per_turn else 0.0,
"per_turn_d_stab_max": round(max(per_turn), 6) if per_turn else 0.0,
"per_turn_d_stab_mean": (
round(float(np.mean(per_turn)), 6) if per_turn else 0.0
),
"lawful_turn_count": lawful_turns,
"lawful_chain_exists": lawful_turns == len(per_turn) and bool(per_turn),
"raw_path_d_stab_curve": [round(x, 6) for x in curve],
"accumulation_is_the_mechanism": bool(
per_turn
and max(per_turn) <= D_STAB_TOL
and curve
and curve[-1] > D_STAB_TOL
),
}
def _spatial4_control_frames(
rng: np.random.Generator, count: int
) -> list[tuple[str, tuple[tuple[float, ...], ...]]]:
"""Deterministic random orthonormal 3-frames inside span(e1..e4).
Restricted to the positive-definite grade-1 block so the metric-restricted
Gram stays Euclidean and every control frame is exactly comparable to the
declared frame. Frames are returned as 3 rows of 4 spatial+e4 coefficients.
"""
frames = []
for i in range(count):
m = rng.standard_normal((4, 3))
q, _ = np.linalg.qr(m)
frames.append(
(
f"random_frame_{i:02d}",
tuple(tuple(float(x) for x in q[:, j]) for j in range(3)),
)
)
return frames
def _frame_geometry(rows4: Sequence[Sequence[float]]) -> IdentityManifoldGeometry:
"""Build a manifold geometry from 3 axes given as e1..e4 coefficients."""
axes = []
for row in rows4:
psi = np.zeros(N_COMPONENTS, dtype=np.float64)
for k, coeff in enumerate(row):
psi[1 + k] = float(coeff) # grade-1 slots e1..e4 at indices 1..4
axes.append(psi)
axes_t = tuple(axes)
gram = np.empty((3, 3), dtype=np.float64)
for i in range(3):
for j in range(3):
gram[i, j] = _inner0(axes_t[i], axes_t[j])
return IdentityManifoldGeometry(
axes_psi=axes_t, gram=gram, gram_inv=np.linalg.inv(gram)
)
def semantic_coupling_analysis(
versors: Sequence[tuple[str, np.ndarray]],
) -> dict[str, Any]:
"""Is the declared frame preferentially preserved by benign dynamics?
Compares benign leakage against the declared frame span(e1,e2,e3) with the
same versors' leakage against (a) the named alternative axis-aligned frames
inside span(e1..e4) and (b) a deterministic ensemble of random orthonormal
3-frames in span(e1..e4). If the declared frame's leakage sits inside the
random-frame distribution, the dynamics do not couple to the declared frame
at all — the mismatch is a semantic-coupling absence, not an attack signal.
"""
declared = default_geometry()
named_alternatives = {
"frame_e1e2e4": ((1, 0, 0, 0), (0, 1, 0, 0), (0, 0, 0, 1)),
"frame_e1e3e4": ((1, 0, 0, 0), (0, 0, 1, 0), (0, 0, 0, 1)),
"frame_e2e3e4": ((0, 1, 0, 0), (0, 0, 1, 0), (0, 0, 0, 1)),
}
rng = np.random.default_rng(CONTROL_FRAME_SEED)
random_frames = _spatial4_control_frames(rng, N_RANDOM_CONTROL_FRAMES)
def mean_leakage(geometry: IdentityManifoldGeometry) -> float:
vals = [geometry.leakage_rms(v) for _, v in versors]
return float(np.mean(vals)) if vals else 0.0
declared_mean = mean_leakage(declared)
named = {
name: round(mean_leakage(_frame_geometry(rows)), 6)
for name, rows in named_alternatives.items()
}
random_means = [
mean_leakage(_frame_geometry(rows)) for _, rows in random_frames
]
frac_random_better = (
float(np.mean([m < declared_mean for m in random_means]))
if random_means
else 0.0
)
# "Coupled" would mean the declared frame leaks dramatically less than
# essentially every control frame. "Uncoupled" = it sits inside the
# control distribution.
declared_is_special = bool(
random_means
and declared_mean < min(random_means)
and declared_mean < 0.5 * float(np.mean(random_means))
)
return {
"n_versors": len(versors),
"declared_frame_mean_leakage": round(declared_mean, 6),
"named_alternative_frame_mean_leakage": named,
"random_frame_count": len(random_means),
"random_frame_mean_leakage_min": (
round(min(random_means), 6) if random_means else 0.0
),
"random_frame_mean_leakage_mean": (
round(float(np.mean(random_means)), 6) if random_means else 0.0
),
"random_frame_mean_leakage_max": (
round(max(random_means), 6) if random_means else 0.0
),
"fraction_of_random_frames_leaking_less_than_declared": round(
frac_random_better, 6
),
"declared_frame_preferentially_preserved": declared_is_special,
}
# --- packet assembly ------------------------------------------------------------
def build_evidence_packet(
benign: Sequence[tuple[str, np.ndarray]],
paraphrase: Sequence[tuple[str, np.ndarray]],
) -> dict[str, Any]:
"""Assemble the full slice-0 evidence packet over all four trace classes."""
geometry = default_geometry()
suites: dict[str, list[TraceDecomposition]] = {
"synthetic": [
decompose_trace(label, "synthetic", v, geometry)
for label, v in synthetic_traces()
],
"adversarial": [
decompose_trace(label, "adversarial", v, geometry)
for label, v in adversarial_traces()
],
"benign": [
decompose_trace(label, "benign", v, geometry) for label, v in benign
],
"paraphrase": [
decompose_trace(label, "paraphrase", v, geometry)
for label, v in paraphrase
],
}
live = list(benign) + list(paraphrase)
mechanism_counts: dict[str, dict[str, int]] = {}
for suite_name, decomps in suites.items():
counts: dict[str, int] = {}
for d in decomps:
counts[d.mechanism] = counts.get(d.mechanism, 0) + 1
mechanism_counts[suite_name] = counts
max_f32_delta = max(
(d.f32_transport_delta for ds in suites.values() for d in ds),
default=0.0,
)
packet: dict[str, Any] = {
"schema_version": "adr_0246_slice0_diagnostic_v1",
"declared_frame": {
"axes": ["truthfulness=e1", "coherence=e2", "reverence=e3"],
"stabilizer": "H_id={I} (locked; unchanged)",
},
"diagnostic_tolerances": {
"leakage_tol": LEAKAGE_TOL,
"d_stab_tol": D_STAB_TOL,
"d_orth_tol": D_ORTH_TOL,
"unclassified_tol": UNCLASSIFIED_TOL,
"note": "report-local classification aids, not gate thresholds",
},
"suites": {
name: [d.as_dict() for d in decomps]
for name, decomps in suites.items()
},
"mechanism_counts": mechanism_counts,
"path_accumulation": path_accumulation_analysis(
suites["benign"] + suites["paraphrase"]
),
"semantic_coupling": semantic_coupling_analysis(live),
"precision_transport": {
"max_f32_roundtrip_action_delta": round(max_f32_delta, 9),
"significant": bool(max_f32_delta > 1e-4),
},
}
packet["verdict"] = _verdict(packet)
return packet
def _verdict(packet: dict[str, Any]) -> dict[str, Any]:
"""One-paragraph machine-checkable answer to the slice-0 question."""
benign_counts = {
**packet["mechanism_counts"].get("benign", {}),
}
for k, v in packet["mechanism_counts"].get("paraphrase", {}).items():
benign_counts[k] = benign_counts.get(k, 0) + v
dominant = max(benign_counts, key=benign_counts.get) if benign_counts else "none"
coupling = packet["semantic_coupling"]
accumulation = packet["path_accumulation"]
precision = packet["precision_transport"]
return {
"benign_mechanism_counts": benign_counts,
"dominant_benign_mechanism": dominant,
"precision_transport_is_the_cause": precision["significant"],
"path_accumulation_is_the_cause": accumulation[
"accumulation_is_the_mechanism"
],
"declared_frame_preferentially_preserved": coupling[
"declared_frame_preferentially_preserved"
],
"semantic_coupling_absent": not coupling[
"declared_frame_preferentially_preserved"
],
}

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"""Run the ADR-0246 slice-0 mismatch diagnostic and emit the evidence packet.
Usage: uv run python -m evals.adr_0246_mismatch_diagnostic [out.json]
Collects live benign + paraphrase versor traces from a fresh empty-vault
``ChatRuntime`` (instrumented instance-locally; serving code untouched), runs
the full decomposition over all four trace classes, and writes the JSON packet.
Diagnostic-only: no gate, threshold, axis, or flag changes.
"""
from __future__ import annotations
import json
import sys
from evals.adr_0246_mismatch_diagnostic import (
PARAPHRASE_PROBE_SEQUENCE,
build_evidence_packet,
collect_live_versors,
)
from evals.adr_0244_gamma_calibration import LIVE_PROBE_SEQUENCE
def main() -> int:
out_path = sys.argv[1] if len(sys.argv) > 1 else None
benign = collect_live_versors(LIVE_PROBE_SEQUENCE)
paraphrase = collect_live_versors(PARAPHRASE_PROBE_SEQUENCE)
packet = build_evidence_packet(benign, paraphrase)
text = json.dumps(packet, indent=2, sort_keys=True)
if out_path:
with open(out_path, "w", encoding="utf-8") as fh:
fh.write(text + "\n")
print(f"evidence packet written to {out_path}")
summary = {
"verdict": packet["verdict"],
"semantic_coupling": packet["semantic_coupling"],
"path_accumulation": {
k: v
for k, v in packet["path_accumulation"].items()
if k != "raw_path_d_stab_curve"
},
"precision_transport": packet["precision_transport"],
"mechanism_counts": packet["mechanism_counts"],
}
print(json.dumps(summary, indent=2, sort_keys=True))
return 0
if __name__ == "__main__":
raise SystemExit(main())

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"""ADR-0246 slice 0 — pins for the mismatch-diagnostic instruments.
Ground-truth expectations from the preflight brief §6.1: each synthetic
construction must land in the correct mechanism class, the induced action must
be exact on known rotors, typed residual channels must fire on the right blade,
and the diagnostic must remain eval-only (no serving imports, no flag changes).
"""
from __future__ import annotations
import numpy as np
import pytest
from algebra.cl41 import N_COMPONENTS, basis_vector
from evals.adr_0246_mismatch_diagnostic import (
IDX_E4,
IDX_E5,
IDX_E12,
IDX_E14,
IDX_E15,
build_evidence_packet,
d_orth,
d_stab,
decompose_trace,
default_geometry,
induced_action,
path_accumulation_analysis,
semantic_coupling_analysis,
synthetic_traces,
typed_residual_channels,
versor_plane_occupancy,
)
from evals.adr_0244_gamma_calibration import _boost, _rotor
@pytest.fixture(scope="module")
def geometry():
return default_geometry()
def test_blade_index_pins():
"""The channel map depends on the cl41 layout; pin it against the algebra."""
for k, expected_idx in ((0, 1), (1, 2), (2, 3), (3, IDX_E4), (4, IDX_E5)):
vec = basis_vector(k)
assert vec[expected_idx] == 1.0
assert np.count_nonzero(vec) == 1
def test_identity_versor_action_is_identity(geometry):
identity = np.zeros(N_COMPONENTS, dtype=np.float64)
identity[0] = 1.0
action = induced_action(geometry, identity)
assert np.allclose(action, np.eye(3), atol=1e-12)
assert d_orth(geometry, action) < 1e-12
assert d_stab(geometry, action) < 1e-12
def test_inplane_rotation_action_is_exact_rotation_matrix(geometry):
theta = 0.3
action = induced_action(geometry, _rotor(IDX_E12, theta))
expected = np.eye(3)
expected[0, 0] = expected[1, 1] = np.cos(theta)
# e12 rotor sandwich rotates the e1/e2 plane by theta.
expected[1, 0] = np.sin(theta)
expected[0, 1] = -np.sin(theta)
assert np.allclose(np.abs(action), np.abs(expected), atol=1e-6)
assert d_orth(geometry, action) < 1e-6 # rotation is a G-isometry
assert d_stab(geometry, action) > 0.05 # but NOT the identity action
def test_permutation_and_inversion_are_in_span_unlawful(geometry):
for versor in (_rotor(IDX_E12, np.pi / 2.0), _rotor(IDX_E12, np.pi)):
d = decompose_trace("t", "synthetic", versor, geometry)
assert d.leakage_rms < 1e-6 # invisible to leakage
assert d.d_stab > 0.05 # visible to the stabilizer distance
assert d.mechanism == "in_span_unlawful"
def test_tilt_fires_e4_channel_only(geometry):
channels = typed_residual_channels(geometry, _rotor(IDX_E14, 1.5))
assert channels["null_or_conformal"] > 0.1
assert channels["boost_like"] == pytest.approx(0.0, abs=1e-12)
assert channels["unclassified"] < 1e-9
d = decompose_trace("t", "synthetic", _rotor(IDX_E14, 1.5), geometry)
assert d.mechanism == "foreign_leakage"
assert "null_or_conformal" in d.mechanism_detail
def test_boost_fires_e5_channel_and_d_orth(geometry):
versor = _boost(IDX_E15, 1.2)
channels = typed_residual_channels(geometry, versor)
assert channels["boost_like"] > 0.1
assert channels["null_or_conformal"] == pytest.approx(0.0, abs=1e-12)
action = induced_action(geometry, versor)
assert d_orth(geometry, action) > 0.05 # boost is not a G-isometry
d = decompose_trace("t", "synthetic", versor, geometry)
assert d.mechanism == "foreign_leakage"
assert "boost_like" in d.mechanism_detail
def test_synthetic_suite_classification_ground_truth(geometry):
expected = {
"identity_versor": "lawful_in_span",
"rot_e12_0.3": "in_span_unlawful",
"rot_e12_halfpi_permutation": "in_span_unlawful",
"rot_e12_pi_inversion": "in_span_unlawful",
"tilt_e14_1.5": "foreign_leakage",
"tilt_e24_0.6": "foreign_leakage",
"boost_e15_1.2": "foreign_leakage",
"boost_e25_0.8": "foreign_leakage",
"mild_inplane_drift_0.02": "lawful_in_span",
}
for label, versor in synthetic_traces():
d = decompose_trace(label, "synthetic", versor, geometry)
assert d.mechanism == expected[label], label
def test_plane_occupancy_localizes_the_mechanism():
occ = versor_plane_occupancy(_rotor(IDX_E12, 0.5))
assert occ["in_span_planes"] == pytest.approx(1.0)
assert occ["e4_mixing_planes"] == 0.0
occ = versor_plane_occupancy(_rotor(IDX_E14, 0.5))
assert occ["e4_mixing_planes"] == pytest.approx(1.0)
occ = versor_plane_occupancy(_boost(IDX_E15, 0.5))
assert occ["e5_mixing_planes"] == pytest.approx(1.0)
def test_path_accumulation_detects_compounding_small_drift(geometry):
# 30 mild in-plane steps, each individually inside D_STAB_TOL, compound
# past it — exactly the brief §3.4 slow-drift failure mode.
steps = [
decompose_trace(f"s{i}", "synthetic", _rotor(IDX_E12, 0.02), geometry)
for i in range(30)
]
report = path_accumulation_analysis(steps)
assert report["lawful_chain_exists"] is True # each step ≈ I
assert report["per_turn_d_stab_max"] <= 0.05
assert report["raw_path_d_stab_curve"][-1] > 0.05
assert report["accumulation_is_the_mechanism"] is True
def test_path_accumulation_not_blamed_when_per_turn_already_large(geometry):
steps = [
decompose_trace(f"s{i}", "synthetic", _rotor(IDX_E14, 1.5), geometry)
for i in range(3)
]
report = path_accumulation_analysis(steps)
assert report["lawful_chain_exists"] is False
assert report["accumulation_is_the_mechanism"] is False
def test_semantic_coupling_detects_a_preserving_ensemble(geometry):
# Versors that DO preserve the declared frame: coupling analysis must
# report the frame as preferentially preserved (leakage 0 < any control).
versors = [(f"v{i}", _rotor(IDX_E12, 0.1 * (i + 1))) for i in range(5)]
report = semantic_coupling_analysis(versors)
assert report["declared_frame_mean_leakage"] < 1e-9
assert report["declared_frame_preferentially_preserved"] is True
def test_semantic_coupling_detects_an_uncoupled_ensemble(geometry):
# Versors tilting/boosting out of span: the declared frame should NOT
# stand out against the random-frame control ensemble.
versors = [
("t1", _rotor(IDX_E14, 1.5)),
("t2", _rotor(IDX_E14, 0.9)),
("b1", _boost(IDX_E15, 1.2)),
("b2", _boost(IDX_E15, 0.7)),
]
report = semantic_coupling_analysis(versors)
assert report["declared_frame_preferentially_preserved"] is False
def test_f32_transport_is_not_the_mechanism(geometry):
# The f64→f32→f64 round-trip of any reference versor moves the induced
# action by machine-epsilon scale — orders below the observed mismatch.
for label, versor in synthetic_traces():
d = decompose_trace(label, "synthetic", versor, geometry)
assert d.f32_transport_delta < 1e-4, label
def test_packet_verdict_shape_offline():
# Offline packet with synthetic stand-ins for the live suites: the packet
# must assemble, count mechanisms, and emit the verdict block.
benign = [("b0", _rotor(IDX_E14, 1.0)), ("b1", _boost(IDX_E15, 0.9))]
paraphrase = [("p0", _rotor(IDX_E14, 1.1))]
packet = build_evidence_packet(benign, paraphrase)
assert packet["schema_version"] == "adr_0246_slice0_diagnostic_v1"
verdict = packet["verdict"]
assert verdict["dominant_benign_mechanism"] == "foreign_leakage"
assert verdict["precision_transport_is_the_cause"] is False
assert set(packet["suites"]) == {
"synthetic",
"adversarial",
"benign",
"paraphrase",
}
def test_diagnostic_is_not_imported_by_serving():
"""A-04: chat/runtime.py must never import this eval package."""
with open("chat/runtime.py", encoding="utf-8") as fh:
source = fh.read()
assert "adr_0246_mismatch_diagnostic" not in source
def test_gate_flag_and_bound_untouched():
"""Slice 0 changes no gate surface: flag default off, bound value pinned."""
from core.config import RuntimeConfig
from core.physics import identity
assert RuntimeConfig().identity_wave_gate is False
assert identity._WAVE_LEAKAGE_BOUND == 0.2126624458513829