from __future__ import annotations import numpy as np from generate.articulation import ArticulationPlan from generate.surface import SentenceAssembler, SurfaceContext from session.correction import CorrectionPass from session.graph import SessionGraph from session.referents import ReferentRegistry from vault.decompose import default_gate def _v(slot: int, scale: float = 1.0) -> np.ndarray: arr = np.zeros(32, dtype=np.float32) arr[slot] = scale return arr def test_backward_walk_returns_true_graph_distances_for_branching_dag() -> None: graph = SessionGraph() graph.add_turn(0, _v(0), _v(0), ("a",), ("a",), "assert") graph.add_turn(1, _v(1), _v(1), ("b",), ("b",), "assert") graph.add_turn(2, _v(2), _v(2), ("c",), ("c",), "assert", backward_edges=[0, 1]) graph.add_turn(3, _v(3), _v(3), ("d",), ("d",), "assert", backward_edges=[2]) walked = graph.backward_walk(3) assert [(dist, node.turn_idx) for dist, node in walked] == [(1, 2), (2, 0), (2, 1)] def test_correction_pass_uses_graph_distance_not_bfs_ordinal() -> None: graph = SessionGraph() base = _v(0) graph.add_turn(0, base, base, ("a",), ("a",), "assert") graph.add_turn(1, base, base, ("b",), ("b",), "assert") graph.add_turn(2, base, base, ("c",), ("c",), "assert", backward_edges=[0, 1]) graph.add_turn(3, base, base, ("d",), ("d",), "assert", backward_edges=[2]) result = CorrectionPass(min_alignment=0.0).apply(graph, base, from_turn=3) by_turn = {record.turn_idx: record.graph_distance for record in result.records} assert by_turn[3] == 0 assert by_turn[2] == 1 assert by_turn[0] == 2 assert by_turn[1] == 2 def test_referent_registry_tracks_only_currently_consumed_sources() -> None: registry = ReferentRegistry() registry.register("light", _v(4), turn=7) assert registry.resolve(["what", "is", "it"]) == ["what", "is", "light"] assert registry.consumed_turns() == [7] assert registry.consumed_slots() == {"neut_sg": 7} assert registry.resolve(["light"]) == ["light"] assert registry.consumed_turns() == [] assert registry.consumed_slots() == {} def test_surface_coreference_keeps_question_pronoun_lowercase() -> None: plan = ArticulationPlan( subject="Light", predicate="form", object=None, surface="Light form", output_language="en", frame_id="test", ) ctx = SurfaceContext(active_referent_surface="Light", active_referent_slot="neut_sg") sentence = SentenceAssembler().assemble(plan, (), role="question", context=ctx) assert sentence.surface == "Given that Light, does it form?" def test_surface_elaboration_matches_rendered_elaboration_string() -> None: plan = ArticulationPlan( subject="light", predicate="reveals", object="truth", surface="light reveals truth", output_language="en", frame_id="test", ) ctx = SurfaceContext(valence_delta=-1.0) sentence = SentenceAssembler().assemble( plan, ("mercy", "justice"), role="elaborate", context=ctx, ) assert sentence.elaboration == "mercy but justice" assert "mercy but justice" in sentence.surface def test_running_dialogue_blade_stays_nonzero_after_three_turns() -> None: """The running blade must not collapse to zero through grade explosion.""" from algebra.rotor import make_rotor_from_angle from generate.proposition import Proposition def _prop(slot: int) -> Proposition: relation = make_rotor_from_angle(0.1 * (slot + 1), bivector_idx=6) return Proposition( subject="a", predicate="b", object_=None, surface="a b", frame_id="test", subject_versor=_v(0), predicate_versor=_v(1), object_versor=None, relation=relation, ) from session.context import SessionContext from language_packs import load_pack _, vocab = load_pack("en_core_cognition_v1") ctx = SessionContext(vocab) for i in range(5): ctx.record_dialogue(_prop(i)) blade = ctx.running_dialogue_blade assert blade is not None assert float(np.linalg.norm(blade)) > 1e-6, ( "running_dialogue_blade collapsed to zero after multiple turns" ) def test_unknown_gate_fires_on_empty_vault_without_self_storage() -> None: class EmptyVault: def __len__(self) -> int: return 0 decision = default_gate.check(0.0, vault=EmptyVault(), query=_v(0)) assert decision.fire is True assert decision.source == "empty_vault"