core/tests/test_architectural_invariants.py

796 lines
30 KiB
Python

"""
tests/test_architectural_invariants.py
Machine-verified proofs of CORE's architectural claims.
This file tests the claims that distinguish CORE from standard transformer /
attention-based / vector-store architectures. Every test here is either:
(A) a mathematical invariant that must hold by construction, or
(B) a structural/type invariant that must hold by design.
If any test in this file fails, a load-bearing architectural claim of CORE
is broken and must be fixed before any other work proceeds.
Claim index
-----------
INV-01 Versor closure under sandwich product (algebraic closure)
INV-02 normalize_to_versor is called once and only at the gate
INV-03 versor_condition < 1e-5 after injection (gate post-condition)
INV-04 versor_apply is algebraically closed (no normalization needed)
INV-05 Holonomy encoding is deterministic (same input → same output)
INV-06 Null-cone membership is preserved under versor_apply
INV-07 D2-D4 frontends cannot claim AUTO_ACCEPT_ELIGIBLE (governance)
INV-08 pressure_id is content-addressed (same content → same id)
INV-09 semantic_key is claim-addressed (same claim, diff provenance → same key)
INV-10 Structural deduplication: duplicate pressure_id rejected
INV-11 Convergent evidence: same semantic_key from N sources → N-1 warnings
INV-12 ReviewDecision does not mutate original packet
INV-13 Segmenter is D0: identical input → identical output (determinism)
INV-14 Segmenter span byte offsets are valid and within source bounds
INV-15 ModalityPack gate_engaged requires checksum_verified
INV-16 ProjectionHead output is always (32,) float32
INV-17 gate_engaged=False structurally prevents projection
INV-18 Null multivector normalization raises (no silent NaN)
INV-19 SourceSpan byte order enforced at construction
INV-20 FieldState versor condition is preserved after versor_apply
"""
from __future__ import annotations
import hashlib
import json
from copy import deepcopy
from typing import Any
import numpy as np
import pytest
# ---------------------------------------------------------------------------
# Algebra imports
# ---------------------------------------------------------------------------
from algebra.versor import versor_apply, normalize_to_versor, versor_condition
from algebra.holonomy import holonomy_encode
from algebra.cl41 import geometric_product, reverse
# ---------------------------------------------------------------------------
# Ingest imports
# ---------------------------------------------------------------------------
from core_ingest.types import (
CandidateGeometricPressure,
DeterminismClass,
FrontendTrace,
GateDisposition,
Modality,
ReviewDecision,
ReviewLevel,
SourceSpan,
)
from core_ingest.compiler import IngestCompiler
from core_ingest.segmenter import StructuralSegmenter
# ---------------------------------------------------------------------------
# Sensorium imports
# ---------------------------------------------------------------------------
from sensorium.protocol import CL41_DIM, ModalityPack, ModalityVocabulary
from sensorium.registry import ModalityRegistry
from sensorium.adapters.text import TextProjectionHead, english_pack
# ---------------------------------------------------------------------------
# Field / gate imports
# ---------------------------------------------------------------------------
from ingest.gate import inject
# ===========================================================================
# Shared fixtures
# ===========================================================================
SOURCE = b"In the beginning God created the heavens and the earth."
SOURCE_SHA = hashlib.sha256(SOURCE).hexdigest()
def _span(start: int = 0, end: int = 20) -> SourceSpan:
return SourceSpan(
byte_start=start, byte_end=end, source_sha256=SOURCE_SHA, region="body"
)
def _frontend(det: DeterminismClass = DeterminismClass.D0) -> FrontendTrace:
return FrontendTrace(
instrument_id="StructuralSegmenter/prose/v1",
determinism=det,
version="1.0.0",
)
def _packet(
det: DeterminismClass = DeterminismClass.D0,
rl: ReviewLevel = ReviewLevel.AUTO_ACCEPT_ELIGIBLE,
lemma: str = "beginning",
s_off: int = 0,
e_off: int = 20,
) -> CandidateGeometricPressure:
return CandidateGeometricPressure(
kind="assertion",
modality=Modality.TEXT,
provenance=(_span(s_off, e_off),),
frontend=_frontend(det),
review_level=rl,
confidence=0.9,
uncertainty=0.1,
lemma=lemma,
payload_json=json.dumps({"text": SOURCE.decode()}),
)
def _unit_versor(blade: int = 0) -> np.ndarray:
"""A unit versor in Cl(4,1): 1.0 in blade `blade`, 0 elsewhere."""
v = np.zeros(32, dtype=np.float64)
v[blade] = 1.0
return v
# ===========================================================================
# INV-01 Versor closure under sandwich product
# ===========================================================================
class TestINV01VersorClosure:
"""
Claim: The sandwich product V * F * reverse(V) is algebraically closed
on the versor manifold. If V and F are versors, the result is a versor
— no normalization required.
This is the foundational claim that makes CORE's field evolution
correct-by-construction rather than correct-by-convention.
"""
def test_scalar_versor_preserves_condition(self):
V = _unit_versor(0) # scalar blade
F = _unit_versor(0)
result = versor_apply(V, F)
assert versor_condition(result) < 1e-5
def test_bivector_rotor_preserves_condition(self):
# A rotor in Cl(4,1): scalar + e12 bivector, normalized
V = np.zeros(32, dtype=np.float64)
V[0] = np.cos(np.pi / 8) # scalar part
V[5] = np.sin(np.pi / 8) # e12 bivector blade
V = normalize_to_versor(V)
F = _unit_versor(1) # e1 vector
result = versor_apply(V, F)
assert versor_condition(result) < 1e-5
def test_closure_holds_after_10_sequential_applications(self):
"""Closure must hold under iterated application — no drift."""
V = normalize_to_versor(_unit_versor(0))
F = normalize_to_versor(_unit_versor(1))
for _ in range(10):
F = versor_apply(V, F)
assert versor_condition(F) < 1e-4 # allow mild float accumulation
def test_closure_is_not_approximate_luck(self):
"""A non-versor does NOT pass the condition check."""
bad = np.ones(32, dtype=np.float64) * 0.1 # not a versor
assert versor_condition(bad) > 1e-3
# ===========================================================================
# INV-02 normalize_to_versor called once, at the gate only
# ===========================================================================
class TestINV02SingleNormalizationSite:
"""
Claim: normalize_to_versor() is the single normalization call in the
system and it is called at ingest/gate.py and nowhere else in the
production path.
Structural test: grep the source tree for normalize_to_versor calls
outside of ingest/gate.py and algebra/versor.py (definition).
"""
def test_normalize_not_called_outside_gate(self, tmp_path):
import ast
import os
allowed_files = {
os.path.join("algebra", "versor.py"), # definition
os.path.join("ingest", "gate.py"), # sole call site
os.path.join("tests", "test_architectural_invariants.py"), # this file
os.path.join("tests", "test_versor_closure.py"),
}
violations: list[str] = []
root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
for dirpath, _, filenames in os.walk(root):
for fname in filenames:
if not fname.endswith(".py"):
continue
full = os.path.join(dirpath, fname)
rel = os.path.relpath(full, root)
if rel in allowed_files:
continue
try:
src = open(full, encoding="utf-8").read()
tree = ast.parse(src, filename=rel)
except Exception:
continue
for node in ast.walk(tree):
if isinstance(node, ast.Call):
func = node.func
name = ""
if isinstance(func, ast.Name):
name = func.id
elif isinstance(func, ast.Attribute):
name = func.attr
if name == "normalize_to_versor":
violations.append(f"{rel}:{node.lineno}")
assert violations == [], (
"normalize_to_versor() called outside the allowed set:\n"
+ "\n".join(violations)
)
# ===========================================================================
# INV-03 Gate post-condition: versor_condition < 1e-5 after injection
# ===========================================================================
class TestINV03GatePostCondition:
"""
Claim: Every FieldState produced by ingest/gate.py satisfies
versor_condition(F) < 1e-5.
"""
def test_single_token_injection(self):
"""A minimal vocab stub satisfies the gate post-condition."""
class _Vocab:
def get_versor(self, t):
v = np.zeros(32, dtype=np.float64)
v[0] = 1.0
return v
state = inject(["logos"], _Vocab())
assert versor_condition(state.F) < 1e-5
def test_multi_token_injection(self):
class _Vocab:
def get_versor(self, t):
v = np.zeros(32, dtype=np.float64)
v[0] = 1.0
v[1] = 0.1 * hash(t) % 10 * 0.01 # small perturbation per token
v = v / np.sqrt(abs(v @ v) or 1.0)
return v
state = inject(["in", "the", "beginning"], _Vocab())
assert versor_condition(state.F) < 1e-5
# ===========================================================================
# INV-04 versor_apply is algebraically closed (no post-normalization)
# ===========================================================================
class TestINV04VersorApplyClosed:
"""
Claim: versor_apply does not call normalize_to_versor internally.
The closure property is algebraic, not enforced by renormalization.
"""
def test_no_normalization_in_versor_apply_source(self):
import inspect
src = inspect.getsource(versor_apply)
assert "normalize_to_versor" not in src, (
"versor_apply must not call normalize_to_versor. "
"Closure is algebraic, not enforced by renormalization."
)
def test_apply_result_passes_condition_without_renormalization(self):
V = normalize_to_versor(_unit_versor(0))
F = normalize_to_versor(_unit_versor(1))
result = versor_apply(V, F)
# No renormalization — must still pass
assert versor_condition(result) < 1e-5
# ===========================================================================
# INV-05 Holonomy encoding is deterministic
# ===========================================================================
class TestINV05HolonomyDeterminism:
"""
Claim: holonomy_encode() is a pure function — given identical inputs it
produces identical outputs. This is required for D0 classification of
the gate's encoding step.
"""
def test_same_versors_same_output(self):
versors = [_unit_versor(i % 5) for i in range(5)]
H1 = holonomy_encode(versors)
H2 = holonomy_encode(versors)
np.testing.assert_array_equal(H1, H2)
def test_different_order_different_output(self):
v1 = _unit_versor(0)
v2 = _unit_versor(1)
H_ab = holonomy_encode([v1, v2])
H_ba = holonomy_encode([v2, v1])
# Order sensitivity: holonomy is not commutative
assert not np.allclose(H_ab, H_ba), (
"Holonomy should be order-sensitive — the geometric product "
"is non-commutative in Cl(4,1)."
)
def test_determinism_across_100_calls(self):
versors = [normalize_to_versor(_unit_versor(i % 32)) for i in range(4)]
results = [holonomy_encode(versors) for _ in range(100)]
for r in results[1:]:
np.testing.assert_array_equal(r, results[0])
# ===========================================================================
# INV-06 Null-cone membership preserved under versor_apply
# ===========================================================================
class TestINV06NullConePreservation:
"""
Claim: versor_apply maps null vectors to null vectors.
A null vector x in Cl(4,1) satisfies x * x = 0 (up to float tolerance).
This ensures vocabulary tokens (null vectors) remain on the null cone
after field transitions.
"""
def _null_vector(self) -> np.ndarray:
"""Construct the canonical o (origin) null vector in CGA Cl(4,1)."""
# In CGA: o = (e_minus - e_plus) / 2 where e_minus^2=-1, e_plus^2=+1
# Using the Cl(4,1) blade indexing from algebra/cl41.py:
# blade 3 = e3, blade 4 = e4 (the extra CGA basis vectors)
# A simple null vector: e1 + e_inf where e_inf = e4+e3 (metric-dependent)
# For this test we construct numerically.
v = np.zeros(32, dtype=np.float64)
v[1] = 1.0 # e1
v[2] = 1.0 # e2
# Make null: x*x = 0 requires careful construction per the metric.
# Use a known null vector from the CGA embedding instead.
# e_o = 0.5*(e_minus - e_plus): in our 32-dim basis this is blade index 3+4
v = np.zeros(32, dtype=np.float64)
v[3] = 0.5 # e3 component
v[4] = -0.5 # e4 component (opposite sign for null condition in Cl(4,1))
return v
def test_null_vector_self_product_is_zero(self):
n = self._null_vector()
nn = geometric_product(n, n)
assert abs(scalar_part := nn[0]) < 1e-10, (
f"Null vector self-product scalar part = {scalar_part:.2e}, expected ~0"
)
def test_versor_apply_preserves_null_property(self):
n = self._null_vector()
V = normalize_to_versor(_unit_versor(0)) # identity-like rotor
result = versor_apply(V, n)
rr = geometric_product(result, result)
assert abs(rr[0]) < 1e-9, (
f"versor_apply broke null property: x*x scalar = {rr[0]:.2e}"
)
# ===========================================================================
# INV-07 Governance invariant: D2-D4 cannot claim AUTO_ACCEPT_ELIGIBLE
# ===========================================================================
class TestINV07GovernanceInvariant:
"""
Claim: The AUTO_ACCEPT_ELIGIBLE status is structurally unavailable to
D2-D4 frontends. This is enforced at packet construction time by the
type system, not by a runtime policy check.
"""
@pytest.mark.parametrize("det", [
DeterminismClass.D2,
DeterminismClass.D3,
DeterminismClass.D4,
])
def test_nondeterministic_frontend_cannot_claim_auto_accept(self, det):
with pytest.raises(ValueError, match="AUTO_ACCEPT_ELIGIBLE"):
_packet(det=det, rl=ReviewLevel.AUTO_ACCEPT_ELIGIBLE)
@pytest.mark.parametrize("det", [
DeterminismClass.D0,
DeterminismClass.D1,
])
def test_deterministic_frontend_can_claim_auto_accept(self, det):
p = _packet(det=det, rl=ReviewLevel.AUTO_ACCEPT_ELIGIBLE)
assert p.review_level == ReviewLevel.AUTO_ACCEPT_ELIGIBLE
# ===========================================================================
# INV-08 pressure_id is content-addressed
# ===========================================================================
class TestINV08PressureIdContentAddressed:
"""
Claim: Two packets with identical content have identical pressure_ids.
Two packets with any field difference have different pressure_ids.
"""
def test_identical_content_identical_id(self):
p1 = _packet()
p2 = _packet()
assert p1.pressure_id == p2.pressure_id
def test_different_provenance_different_id(self):
p1 = _packet(s_off=0, e_off=20)
p2 = _packet(s_off=10, e_off=30)
assert p1.pressure_id != p2.pressure_id
def test_different_lemma_different_id(self):
p1 = _packet(lemma="beginning")
p2 = _packet(lemma="earth")
assert p1.pressure_id != p2.pressure_id
# ===========================================================================
# INV-09 semantic_key is claim-addressed
# ===========================================================================
class TestINV09SemanticKeyClaimAddressed:
"""
Claim: Two packets asserting the same semantic claim share a semantic_key
regardless of provenance, instrument, or confidence values.
"""
def test_same_claim_different_provenance_same_key(self):
p1 = _packet(s_off=0, e_off=20)
p2 = _packet(s_off=50, e_off=70)
assert p1.semantic_key == p2.semantic_key
def test_different_claim_different_key(self):
p1 = _packet(lemma="beginning")
p2 = _packet(lemma="darkness")
assert p1.semantic_key != p2.semantic_key
def test_semantic_key_stable_across_constructions(self):
keys = {_packet(lemma="light").semantic_key for _ in range(50)}
assert len(keys) == 1, "semantic_key must be deterministic"
# ===========================================================================
# INV-10 Structural deduplication
# ===========================================================================
class TestINV10StructuralDeduplication:
"""
Claim: The IngestCompiler rejects the second submission of a packet with
an already-seen pressure_id within the same batch.
"""
def test_duplicate_rejected(self):
p = _packet()
compiler = IngestCompiler()
report, _ = compiler.compile([p, p])
assert len(report.accepted_ids) == 1
assert len(report.rejected_ids) == 1
dup_result = report.results[1]
assert dup_result.disposition == GateDisposition.REJECTED_PROVENANCE
assert "duplicate" in (dup_result.failure_reason or "")
# ===========================================================================
# INV-11 Convergent evidence detection
# ===========================================================================
class TestINV11ConvergentEvidence:
"""
Claim: When N packets share a semantic_key (same claim, N independent
provenance sources), packets 2..N receive a
'semantic_convergence:<k>_prior_sources' warning.
"""
def test_three_independent_sources_two_warnings(self):
p1 = _packet(s_off=0, e_off=20)
p2 = _packet(s_off=30, e_off=50)
p3 = _packet(s_off=60, e_off=80)
assert p1.semantic_key == p2.semantic_key == p3.semantic_key
compiler = IngestCompiler()
report, _ = compiler.compile([p1, p2, p3])
warned = [
r for r in report.results
if any("semantic_convergence" in w for w in r.warnings)
]
assert len(warned) == 2
# ===========================================================================
# INV-12 ReviewDecision does not mutate original packet
# ===========================================================================
class TestINV12ReviewDecisionImmutability:
"""
Claim: A ReviewDecision authorizes acceptance of a packet without
modifying the original packet. The packet's review_level remains
ARCHITECT_REVIEW_REQUIRED after the override.
"""
def test_packet_immutable_after_override(self):
p = _packet(det=DeterminismClass.D4, rl=ReviewLevel.ARCHITECT_REVIEW_REQUIRED)
original_rl = p.review_level
decision = ReviewDecision(
authorized_ids=frozenset({p.pressure_id}),
authorized_by="joshua.shay",
reason="Reviewed.",
)
compiler = IngestCompiler()
report, artifacts = compiler.compile([p], review_decision=decision)
# Accepted via override
assert p.pressure_id in report.accepted_ids
# Original packet is unchanged
assert p.review_level == original_rl
assert artifacts[0].packet is p
# ===========================================================================
# INV-13 Segmenter is D0: deterministic
# ===========================================================================
class TestINV13SegmenterDeterminism:
"""
Claim: StructuralSegmenter is a D0 instrument — identical source bytes
produce identical segments on every call, with no external state.
"""
@pytest.mark.parametrize("hint", ["prose", "scripture", "code", "math"])
def test_identical_input_identical_output(self, hint):
sources = {
"prose": b"# Title\n\nFirst paragraph.\n\nSecond.",
"scripture": b"Gen 1:1 In the beginning.\nGen 1:2 Formless.",
"code": b"```python\nprint('logos')\n```",
"math": rb"\[E = mc^2\]",
}
seg = StructuralSegmenter()
source = sources[hint]
results_a = seg.segment(source, modality_hint=hint)
results_b = seg.segment(source, modality_hint=hint)
assert len(results_a) == len(results_b)
for a, b in zip(results_a, results_b):
assert a.span.byte_start == b.span.byte_start
assert a.span.byte_end == b.span.byte_end
assert a.span.source_sha256 == b.span.source_sha256
assert a.text == b.text
def test_100_repeated_calls_identical(self):
seg = StructuralSegmenter()
source = b"# Logos\n\nIn the beginning was the Word."
first = seg.segment(source, modality_hint="prose")
for _ in range(99):
result = seg.segment(source, modality_hint="prose")
for a, b in zip(first, result):
assert a.span.byte_start == b.span.byte_start
assert a.text == b.text
# ===========================================================================
# INV-14 Segmenter byte offsets valid
# ===========================================================================
class TestINV14SegmenterByteOffsets:
"""
Claim: Every SourceSpan produced by the segmenter has:
- byte_start >= 0
- byte_end > byte_start
- byte_end <= len(source)
- source_sha256 == sha256(source)
"""
@pytest.mark.parametrize("hint,source", [
("prose", b"# Title\n\nBody text here."),
("scripture", b"Gen 1:1 Beginning.\nGen 1:2 Void."),
("code", b"```py\npass\n```"),
("math", rb"\[x^2\]"),
])
def test_offsets_valid(self, hint, source):
expected_sha = hashlib.sha256(source).hexdigest()
seg = StructuralSegmenter()
for s in seg.segment(source, modality_hint=hint):
assert s.span.byte_start >= 0
assert s.span.byte_end > s.span.byte_start
assert s.span.byte_end <= len(source)
assert s.span.source_sha256 == expected_sha
# ===========================================================================
# INV-15 ModalityPack gate invariant
# ===========================================================================
class TestINV15ModalityPackGateInvariant:
"""
Claim: gate_engaged=True cannot be set without checksum_verified=True.
Structural enforcement at construction time.
"""
def test_gate_engaged_without_checksum_raises(self):
vocab = ModalityVocabulary()
head = TextProjectionHead(vocab)
with pytest.raises(ValueError, match="checksum_verified"):
ModalityPack(
pack_id="test",
modality_type=sensorium_modality,
projection=head,
decoder=None,
vocabulary=vocab,
grammar_scaffold=None,
checksum_verified=False,
gate_engaged=True,
)
def test_gate_not_engaged_with_unverified_is_ok(self):
from sensorium.protocol import Modality as SModality
vocab = ModalityVocabulary()
pack = ModalityPack(
pack_id="ungated",
modality_type=SModality.TEXT,
vocabulary=vocab,
grammar_scaffold=None,
checksum_verified=False,
gate_engaged=False,
)
assert not pack.gate_engaged
# ---------------------------------------------------------------------------
# local alias to avoid import name collision
# ---------------------------------------------------------------------------
from sensorium.protocol import Modality as sensorium_modality # noqa: E402
# Reassign after class to satisfy the class body reference above:
TestINV15ModalityPackGateInvariant # force evaluation
# ===========================================================================
# INV-16 ProjectionHead output is (32,) float32
# ===========================================================================
class TestINV16ProjectionOutputShape:
"""
Claim: Every projection through the sensorium layer returns a (32,)
float32 array — the canonical Cl(4,1) multivector shape.
"""
def test_single_projection_shape_and_dtype(self):
from sensorium.protocol import ModalityVocabulary
vocab = ModalityVocabulary()
rotor = np.zeros(CL41_DIM, dtype=np.float32)
rotor[0] = 1.0
vocab.register("logos", rotor)
head = TextProjectionHead(vocab)
mv = head.project("logos")
assert mv.shape == (CL41_DIM,)
assert mv.dtype == np.float32
def test_oov_projection_shape_and_dtype(self):
head = TextProjectionHead(ModalityVocabulary())
mv = head.project("__oov__")
assert mv.shape == (CL41_DIM,)
assert mv.dtype == np.float32
def test_registry_project_enforces_shape(self):
vocab = ModalityVocabulary()
r = np.zeros(CL41_DIM, dtype=np.float32); r[0] = 1.0
vocab.register("word", r)
registry = ModalityRegistry()
registry.mount(english_pack(vocab))
mv = registry.project("en", "word")
assert mv.shape == (CL41_DIM,)
assert mv.dtype == np.float32
# ===========================================================================
# INV-17 gate_engaged=False blocks projection
# ===========================================================================
class TestINV17GateEngagedBlocksProjection:
"""
Claim: A ModalityPack with gate_engaged=False structurally prevents
projection through the registry. This enforces the Supervised Seeding
Epoch protocol — Hebrew and Koine Greek cannot be used for inference
until their seeding epoch completes.
"""
def test_hebrew_gate_off_blocks_project(self):
from sensorium.adapters.text import hebrew_pack
vocab = ModalityVocabulary()
r = np.zeros(CL41_DIM, dtype=np.float32); r[0] = 1.0
vocab.register("bereshit", r)
registry = ModalityRegistry()
registry.mount(hebrew_pack(vocab))
with pytest.raises(RuntimeError, match="gate is not engaged"):
registry.project("he", "bereshit")
def test_koine_greek_gate_off_blocks_project(self):
from sensorium.adapters.text import koine_greek_pack
vocab = ModalityVocabulary()
r = np.zeros(CL41_DIM, dtype=np.float32); r[0] = 1.0
vocab.register("logos", r)
registry = ModalityRegistry()
registry.mount(koine_greek_pack(vocab))
with pytest.raises(RuntimeError, match="gate is not engaged"):
registry.project("grc", "logos")
# ===========================================================================
# INV-18 Null multivector normalization raises
# ===========================================================================
class TestINV18NullNormalizationRaises:
"""
Claim: normalize_to_versor raises ValueError on a null multivector
(norm_squared ≈ 0). There is no silent NaN propagation.
NaN in the manifold would be structurally undetectable and
catastrophically wrong.
"""
def test_zero_vector_raises(self):
with pytest.raises(ValueError, match="null"):
normalize_to_versor(np.zeros(32, dtype=np.float64))
def test_near_zero_raises(self):
v = np.zeros(32, dtype=np.float64)
v[0] = 1e-15 # effectively zero after squaring
with pytest.raises(ValueError):
normalize_to_versor(v)
# ===========================================================================
# INV-19 SourceSpan byte order enforced
# ===========================================================================
class TestINV19SourceSpanByteOrder:
"""
Claim: SourceSpan enforces byte_end > byte_start at construction.
A reversed or zero-length span is structurally impossible.
"""
def test_reversed_offsets_raise(self):
with pytest.raises(ValueError):
SourceSpan(byte_start=50, byte_end=10, source_sha256="a" * 64)
def test_equal_offsets_raise(self):
with pytest.raises(ValueError):
SourceSpan(byte_start=10, byte_end=10, source_sha256="a" * 64)
def test_valid_span_constructs(self):
span = SourceSpan(byte_start=0, byte_end=1, source_sha256="a" * 64)
assert span.byte_end > span.byte_start
# ===========================================================================
# INV-20 FieldState versor condition preserved after versor_apply
# ===========================================================================
class TestINV20FieldStateVersorPreserved:
"""
Claim: Applying versor_apply to a valid FieldState's F array produces
a result that still satisfies versor_condition < 1e-5. The field
evolution never leaves the versor manifold.
"""
def test_field_stays_on_manifold_after_transition(self):
class _Vocab:
def get_versor(self, t):
v = np.zeros(32, dtype=np.float64); v[0] = 1.0; return v
state = inject(["logos"], _Vocab())
V = normalize_to_versor(_unit_versor(0))
F_new = versor_apply(V, state.F)
assert versor_condition(F_new) < 1e-5
def test_ten_successive_transitions_stay_on_manifold(self):
class _Vocab:
def get_versor(self, t):
v = np.zeros(32, dtype=np.float64); v[0] = 1.0; return v
state = inject(["word"], _Vocab())
F = state.F
V = normalize_to_versor(_unit_versor(0))
for _ in range(10):
F = versor_apply(V, F)
assert versor_condition(F) < 1e-4