Improve test verification for main crates

[marvin-hansen]

Improve tests and verification for:

• [ ]: deep_causality (deep_causality)
• [ ]: deep_causality_algorithms (deep_causality_algorithms)
• [ ]: deep_causality_cfd (deep_causality_cfd)
• [ ]: deep_causality_core (deep_causality_core)
• [ ]: deep_causality_data_structures (deep_causality_data_structures)
• [ ]: deep_causality_discovery (deep_causality_discovery)
• [ ]: deep_causality_ethos (deep_causality_ethos)
• [ ]: deep_causality_physics (deep_causality_physics)
• deep_causality_quantum (deep_causality_quantum) Closed in PR783
• [ ]: ultragraph (ultragraph)

[marvin-hansen]

Scope

The audit covers:

• deep_causality
• deep_causality_algorithms
• deep_causality_cfd
• deep_causality_core
• deep_causality_data_structures
• deep_causality_discovery
• deep_causality_ethos
• deep_causality_quantum
• ultragraph

Per-crate inventory

Percentages use each crate's explicit test-function count. The final five columns are raw,
overlapping classifications.

Crate	Test files	Tests	Single-input	Cherry-picked	Tautology	Circular	No assertion
deep_causality	160	1,137	1,041 (91.6%)	90 (7.9%)	21 (1.8%)	4 (0.4%)	2 (0.2%)
deep_causality_algorithms	37	334	254 (76.0%)	23 (6.9%)	3 (0.9%)	7 (2.1%)	0
deep_causality_cfd	123	938	649 (69.2%)	129 (13.8%)	22 (2.3%)	46 (4.9%)	0
deep_causality_core	31	252	243 (96.4%)	0	0	0	0
deep_causality_data_structures	10	81	66 (81.5%)	2 (2.5%)	0	0	0
deep_causality_discovery	52	194	178 (91.8%)	5 (2.6%)	3 (1.5%)	0	3 (1.5%)
deep_causality_ethos	17	106	105 (99.1%)	0	1 (0.9%)	0	0
deep_causality_quantum	45	462	371 (80.3%)	76 (16.5%)	9 (1.9%)	3 (0.6%)	0
ultragraph	15	129	98 (76.0%)	1 (0.8%)	1 (0.8%)	2 (1.6%)	0
Total	490	3,633	3,005 (82.7%)	326 (9.0%)	60 (1.7%)	62 (1.7%)	5 (0.1%)

Recommended order of fixes

This ordering covers the nine crates in this report. It prioritizes confirmed ineffective behavior
checks, consequence of a missed defect, and then raw mutation risk; it does not sort by scanner
percentage alone.

1. deep_causality_quantum — repair the eight output-blind kernel and wrapper success tests
   first. Exact values and identity laws are available, so these are high-confidence, bounded
   fixes in a correctness-sensitive crate.
2. deep_causality — make CSM action execution observable, assert relay targets and effects,
   repair the two assertion-free tests, and replace getter-presence checks with identity checks.
   This is the largest confirmed behavioral cluster and the public root crate.
3. deep_causality_cfd — fix the momentum wrapper and projection property checks, then use
   targeted mutation testing on coupling, blackout, corridor, force, and inverse kernels. Preserve
   the independent analytic and dense-reference controls.
4. deep_causality_algorithms — pin the automatic transform downgrade numerically, then run
   mutations over BRCD scoring and Gaussian-family code. The raw circular tests should remain when
   their closed forms are independent.
5. deep_causality_discovery — make the BOSS fallback pipeline assert the produced report and
   selected outcome. Retain the match-and-panic conversion tests or express them as matches! for
   scanner clarity.
6. deep_causality_ethos — make successful inheritance linking observable through graph or
   verdict behavior. This is one localized, inexpensive repair.
7. ultragraph — assert that topological sorting an empty graph returns an empty ordering, then
   mutation-test centrality normalization before changing its independently derived expectations.
8. deep_causality_data_structures — no confirmed hard defect; use focused index mutations as
   a verification pass after the higher-risk crates.
9. deep_causality_core — no hard candidate from this audit. Review only through mutation
   survivors; do not expand tests merely to reduce the single-input percentage.

[marvin-hansen]

The physics crate is particularly bad:

Class	Count	Share	What it means
single-input	1616	91.9%	One hand-chosen input. No loop, no table, no range.
cherry-picked	641	36.5%	One input, a magic literal expectation, and no stated provenance for it.
tautology	169	9.6%	No assertion says anything about the answer — typically assert!(x.is_ok()) and nothing else.
circular	57	3.2%	The expected value is recomputed in the test body from the same formula the kernel uses.
no-assertion	1	0.1%	No assertion, and no delegation to a helper that asserts.

The classes overlap: a cherry-picked test is also single-input by construction.