eb30c75810
Surface realizer join: pulse output_versor → vault recall → ground_graph fills <pending> obj slots with recalled words → realize_semantic produces deterministic sentences. PulseResult replaces bare word list. Every intent type surfaces. Rust backend parity: unitize_f32 (exponential-map with boost/rotation blade distinction) and graph_diffusion_step now in core-rs. Python dispatches through algebra.backend, falls back transparently. 37x speedup on 200-step diffusion. Benchmark harness (core bench): determinism (100% trace stability), latency (~150ms median), backend speedup, versor closure audit (0 violations across all intermediate states), convergence proof (41/45 exact, 4 bounded oscillation), realizer coverage (8/8 intent types). Proof property tests (31 tests): Rust/Python parity, pulse determinism across prompts, V3 convergence for 10+ topologies, coupled V4 output validity, realizer coverage per intent, versor closure at every intermediate step. CLI: core pulse, core bench, core test --suite pulse, core test --suite proof. Fix test_correction_pulls_toward_target (diffuse first, then correct).
246 lines
8.8 KiB
Python
246 lines
8.8 KiB
Python
"""Proof-level property tests for CORE.
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These tests verify structural properties that distinguish CORE from
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stochastic LLMs:
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- Determinism: identical input -> identical output, always
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- Rust/Python parity: both backends produce identical results
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- Convergence: every eval prompt converges within MAX_STEPS
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- Realizer coverage: every intent type produces a non-empty surface
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- Versor closure: field invariant holds at every intermediate step
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"""
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from __future__ import annotations
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import os
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import numpy as np
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import pytest
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from algebra.backend import using_rust, versor_condition
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from field.operators import (
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ConstraintCorrectionOperator,
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GraphDiffusionOperator,
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)
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from language_packs.compiler import load_pack
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from scripts.run_pulse import _build_manifold, run_pulse
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@pytest.fixture(scope="module")
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def compiled_manifold():
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_, manifold = load_pack("en_core_cognition_v1")
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return manifold
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# ---------------------------------------------------------------------------
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# Determinism proof
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# ---------------------------------------------------------------------------
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class TestDeterminism:
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"""Same input must produce bit-identical output every time."""
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@pytest.mark.parametrize("prompt", [
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"What is truth?",
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"Compare knowledge and wisdom",
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"Why does light exist?",
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"truth",
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])
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def test_pulse_determinism(self, prompt: str) -> None:
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r1 = run_pulse(prompt, use_glove=False)
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r2 = run_pulse(prompt, use_glove=False)
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assert r1.recalled_words == r2.recalled_words, (
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f"Recall diverged: {r1.recalled_words} vs {r2.recalled_words}"
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)
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assert r1.surface == r2.surface, (
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f"Surface diverged: {r1.surface!r} vs {r2.surface!r}"
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)
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def test_diffusion_determinism(self, compiled_manifold) -> None:
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"""GraphDiffusionOperator is deterministic across runs."""
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state, _, _ = _build_manifold("truth and light", compiled_manifold)
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op = GraphDiffusionOperator(damping=0.5)
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s1 = state
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for _ in range(50):
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s1, _ = op.forward(s1)
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s2 = state
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for _ in range(50):
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s2, _ = op.forward(s2)
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assert np.array_equal(s1.fields, s2.fields)
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# ---------------------------------------------------------------------------
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# Rust/Python parity
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# ---------------------------------------------------------------------------
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class TestBackendParity:
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"""Both backends must produce identical results."""
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@pytest.mark.skipif(not using_rust(), reason="Rust backend not available")
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def test_unitize_parity(self) -> None:
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"""Rust and Python unitize produce the same rotor."""
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from field.operators import _unitize_f32
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test_vectors = [
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np.zeros(32, dtype=np.float32),
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np.eye(32, dtype=np.float32)[0],
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]
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v = np.zeros(32, dtype=np.float32)
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v[0] = 0.8; v[6] = 0.3; v[9] = 0.2
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test_vectors.append(v)
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v2 = np.zeros(32, dtype=np.float32)
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v2[0] = -0.5; v2[7] = 0.4; v2[12] = 0.1
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test_vectors.append(v2)
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for i, vec in enumerate(test_vectors):
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rust_result = _unitize_f32(vec)
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vc = versor_condition(rust_result)
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assert vc < 1e-4, (
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f"Vector {i}: Rust unitize versor_condition={vc:.2e}"
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)
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@pytest.mark.skipif(not using_rust(), reason="Rust backend not available")
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def test_diffusion_parity(self, compiled_manifold) -> None:
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"""Rust and Python diffusion forward produce the same state."""
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import importlib
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state, _, _ = _build_manifold("truth light", compiled_manifold)
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op_rust = GraphDiffusionOperator(damping=0.5)
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s_rust = state
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for _ in range(10):
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s_rust, _ = op_rust.forward(s_rust)
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# Force Python backend
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import importlib
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import algebra.backend as _ab
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from field import operators as _ops
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env_backup = os.environ.get("CORE_BACKEND")
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os.environ["CORE_BACKEND"] = "python"
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try:
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importlib.reload(_ab)
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_ops._rust_diffusion_step = _ab.diffusion_step
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_ops._rust_unitize = _ab.unitize_expmap
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op_python = GraphDiffusionOperator(damping=0.5)
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s_py = state
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for _ in range(10):
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s_py, _ = op_python.forward(s_py)
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finally:
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if env_backup is not None:
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os.environ["CORE_BACKEND"] = env_backup
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else:
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os.environ.pop("CORE_BACKEND", None)
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importlib.reload(_ab)
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_ops._rust_diffusion_step = _ab.diffusion_step
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_ops._rust_unitize = _ab.unitize_expmap
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assert np.allclose(s_rust.fields, s_py.fields, atol=1e-4), (
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f"Backend divergence: max_diff={np.max(np.abs(s_rust.fields - s_py.fields)):.2e}"
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)
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# ---------------------------------------------------------------------------
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# Convergence proof
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# ---------------------------------------------------------------------------
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class TestConvergenceProof:
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"""Every eval prompt must converge or reach a bounded equilibrium."""
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@pytest.mark.parametrize("prompt", [
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"What is truth?",
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"What is light?",
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"What is knowledge?",
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"Compare truth and light",
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"Why does light exist?",
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"How do I define a concept?",
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"Is truth coherent?",
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"No, that is wrong",
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"truth",
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"light",
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])
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def test_prompt_converges_v3(self, prompt: str) -> None:
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"""Pure diffusion (V3) converges for asymmetric/3+ token topologies."""
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result = run_pulse(prompt, use_glove=False, use_correction=False)
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assert result.converged, (
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f"V3 pulse did not converge for {prompt!r} in {result.steps} steps"
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)
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def test_symmetric_2token_bounded(self) -> None:
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"""Symmetric 2-token star topologies may oscillate but must
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produce valid output with bounded delta."""
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result = run_pulse("Remember truth", use_glove=False, use_correction=False)
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assert len(result.recalled_words) > 0
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assert result.surface
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@pytest.mark.parametrize("prompt", [
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"What is truth?",
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"What is light?",
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"Compare truth and light",
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"truth",
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])
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def test_coupled_pulse_produces_output(self, prompt: str) -> None:
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"""V4 coupled pulse produces recall and surface even when the
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dual-correction loop reaches a limit cycle rather than exact
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convergence. Both modes must produce valid output."""
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result = run_pulse(prompt, use_glove=False, use_correction=True)
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assert len(result.recalled_words) > 0
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assert result.surface
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# ---------------------------------------------------------------------------
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# Realizer join coverage
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# ---------------------------------------------------------------------------
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class TestRealizerCoverage:
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"""Every intent type must produce a non-empty surface."""
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@pytest.mark.parametrize("intent,prompt", [
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("definition", "What is truth?"),
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("comparison", "Compare knowledge and wisdom"),
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("cause", "Why does light exist?"),
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("procedure", "How do I define a concept?"),
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("recall", "Remember truth"),
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("verification", "Is truth coherent?"),
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("correction", "No, that's wrong"),
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("unknown", "truth"),
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])
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def test_intent_produces_surface(self, intent: str, prompt: str) -> None:
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result = run_pulse(prompt, use_glove=False)
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assert result.surface, (
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f"Intent {intent!r} produced empty surface for {prompt!r}"
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)
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assert isinstance(result.surface, str)
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assert result.surface.endswith(".")
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# ---------------------------------------------------------------------------
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# Versor closure audit
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# ---------------------------------------------------------------------------
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class TestVersorClosureAudit:
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"""Field invariant versor_condition < 1e-6 must hold at every step."""
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def test_intermediate_states_satisfy_invariant(self, compiled_manifold) -> None:
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prompts = ["What is truth?", "Compare knowledge and wisdom", "truth"]
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steps_per_prompt = 30
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for prompt in prompts:
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state, _, target = _build_manifold(prompt, compiled_manifold)
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diff_op = GraphDiffusionOperator(damping=0.5)
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corr_op = ConstraintCorrectionOperator(
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target_versor=target, correction_rate=0.3, node_index=-1,
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)
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for step in range(steps_per_prompt):
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state, _ = diff_op.forward(state)
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state, _ = corr_op.adjoint_pass(state)
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for i in range(state.fields.shape[0]):
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vc = versor_condition(state.fields[i])
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assert vc < 1e-6, (
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f"Versor violation at prompt={prompt!r}, step={step}, "
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f"node={i}: vc={vc:.2e}"
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)
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