b5d6ad6510
Closes the residual `novel_pair_under_seen_relation` pattern that neither `transitive_walk` nor `multi_relation_walk` could synthesise. - new `compose_relations(triples, head, frame, relation)` operator — pure lookup, returns both `R(head, ?)` and `R(frame, ?)` tails - new `FRAME_TRANSFER` intent + `_FRAME_TRANSFER_RE` regex tried before generic TRANSITIVE_QUERY so "in Y" isn't truncated; handles "X belong to in Y" → belongs_to normalisation - pipeline wiring: `_maybe_compose_relations`, `_fold_compose_into_surface`, `_serialize_compose` (folded into operator_invocation so trace_hash stays bit-identical across replay) - regression: inference_closure, multi_step_reasoning, cross_domain_transfer all still 100% on public + holdouts discourse_paragraph v2: - per-sentence grammar rubric (length, capitalization, subject alignment) gated on `require_per_sentence_grammar` - scaling cases at 10 / 20 / 50 sentences — 3/3 pass, 100% per-sentence - 3 runtime round-trip cases (`mode: runtime_roundtrip`) that prime vault, ask question, verify bit-identical across two fresh runtimes - new `per_sentence_grammar_pass_rate` lane metric Long-form replay benchmark (benchmarks/replay_vs_llm.py): - `replay_determinism_report(prompts, runs, priming)` — CORE-only - `compare_to_llm(prompts, llm_callable)` — BYO API client, no provider lock-in; reports per-prompt determinism on both sides - ships with default cognition-pack prompts; 100% bit-identical at runs=3 Lanes green: cognition 121/121, runtime 19/19, teaching 17/17, packs 6/6, compositionality 16/16 + 10/10, inference_closure 20/20 + 12/12, multi_step_reasoning 15/15 + 10/10, cross_domain_transfer 10/10 + 8/8, discourse_paragraph v1 12/12 + v2 6/6.
92 lines
3.3 KiB
Python
92 lines
3.3 KiB
Python
"""Unit tests for compose_relations operator and FRAME_TRANSFER intent.
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Covers the compositionality lane's `novel_pair_under_seen_relation`
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pattern: given R(A, a_val) and R(B, b_val), the probe "What does A R
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in B?" should yield both tails.
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"""
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from __future__ import annotations
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from generate.intent import IntentTag, classify_intent
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from generate.operators import FrameComposeResult, compose_relations
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class TestComposeRelations:
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def test_returns_both_tails_when_both_edges_present(self):
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triples = (
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("truth", "grounds", "judgment"),
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("knowledge", "grounds", "inference"),
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)
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result = compose_relations(
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triples, head="truth", frame="knowledge", relation="grounds"
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)
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assert result.subject_tail == "judgment"
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assert result.frame_tail == "inference"
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def test_returns_none_for_missing_edge(self):
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triples = (("truth", "grounds", "judgment"),)
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result = compose_relations(
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triples, head="truth", frame="knowledge", relation="grounds"
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)
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assert result.subject_tail == "judgment"
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assert result.frame_tail is None
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def test_case_insensitive_inputs(self):
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triples = (("Truth", "Grounds", "Judgment"),)
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result = compose_relations(
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triples, head="TRUTH", frame="knowledge", relation="GROUNDS"
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)
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assert result.head == "truth"
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assert result.subject_tail == "judgment"
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def test_first_write_wins_for_duplicate_heads(self):
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triples = (
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("truth", "grounds", "judgment"),
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("truth", "grounds", "second"),
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)
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result = compose_relations(
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triples, head="truth", frame="truth", relation="grounds"
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)
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assert result.subject_tail == "judgment"
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def test_pure_function_replay_deterministic(self):
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triples = (
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("truth", "grounds", "judgment"),
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("knowledge", "grounds", "inference"),
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)
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a = compose_relations(triples, "truth", "knowledge", "grounds")
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b = compose_relations(triples, "truth", "knowledge", "grounds")
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assert a == b
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def test_as_dict_is_json_safe(self):
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result = FrameComposeResult(
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head="truth",
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frame="knowledge",
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relation="grounds",
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subject_tail="judgment",
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frame_tail="inference",
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)
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d = result.as_dict()
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assert d["head"] == "truth"
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assert d["frame_tail"] == "inference"
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class TestFrameTransferIntent:
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def test_classifies_frame_transfer_form(self):
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intent = classify_intent("What does truth ground in knowledge?")
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assert intent.tag is IntentTag.FRAME_TRANSFER
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assert intent.subject == "truth"
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assert intent.relation == "grounds"
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assert intent.frame == "knowledge"
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def test_belong_to_in_form_normalises_to_belongs_to(self):
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intent = classify_intent("What does recognition belong to in naming?")
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assert intent.tag is IntentTag.FRAME_TRANSFER
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assert intent.subject == "recognition"
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assert intent.relation == "belongs_to"
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assert intent.frame == "naming"
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def test_does_not_match_single_entity_probe(self):
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intent = classify_intent("What does wisdom precede?")
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assert intent.tag is IntentTag.TRANSITIVE_QUERY
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assert intent.frame is None
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