Restructure: descriptive tier and experiment names, paper/manuscript

- paper/pnas -> paper/manuscript (venue-neutral)
- configs/layer1 -> configs/inheritance, src/knowledge -> src/inheritance
  (imported as `inheritance`), make layer1 -> make inheritance; layer2 alias dropped
- inheritance and trained-network bundles named after the manuscript figure
  they feed (fig2_grounding_sweep, figS3_rebaselining, ...), or descriptively
  where they feed none; configs keep their `experiment:` value so parquet
  hashes are unchanged, only output.dir moves
- figure scripts, SI figure sources, notebooks, REPRODUCING.md, README and the
  SI Methods/tables updated; make clean no longer deletes tracked manifests;
  reproduce.sh hashes the s{seed}/ layouts too

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm

tests/test_analysis.py

@@ -8,8 +8,8 @@ import numpy as np
 import pandas as pd
 import pytest
 
-from knowledge.analysis import reduce_to_stationary, critical_grounding
-from knowledge.metrics import tail_band_metrics
+from inheritance.analysis import reduce_to_stationary, critical_grounding
+from inheritance.metrics import tail_band_metrics
 
 
 def _H_eq(n, m, Hs):

tests/test_correctness.py

@@ -9,13 +9,13 @@ from __future__ import annotations
 import numpy as np
 import pytest
 
-from knowledge.config import LineageCfg
-from knowledge.metrics import forward_kl, heterozygosity, support_size, tail_mass
-from knowledge.step import allocate_m, apply_selection, generation_step, StepCtx, \
+from inheritance.config import LineageCfg
+from inheritance.metrics import forward_kl, heterozygosity, support_size, tail_mass
+from inheritance.step import allocate_m, apply_selection, generation_step, StepCtx, \
     structured_multinomial
-from knowledge.teachers import make_correlated_teachers, make_retention_matrix
-from knowledge.truth import make_true_distribution, uniform_init
-from knowledge.lineage import run_lineage
+from inheritance.teachers import make_correlated_teachers, make_retention_matrix
+from inheritance.truth import make_true_distribution, uniform_init
+from inheritance.lineage import run_lineage
 
 
 # ---- truth --------------------------------------------------------------------------

tests/test_dynamic_society.py

@@ -9,7 +9,7 @@ from __future__ import annotations
 
 import numpy as np
 
-from knowledge.dynamic_society import _conformity, _consensus, _novelty, run_dynamic_society
+from inheritance.dynamic_society import _conformity, _consensus, _novelty, run_dynamic_society
 
 
 def _run(arm_overrides: dict, seed: int = 0, gens: int = 50):

tests/test_genotype.py

@@ -10,13 +10,13 @@ from __future__ import annotations
 
 import numpy as np
 
-from knowledge.genotype import (
+from inheritance.genotype import (
     additive_fitness, bits_to_index, crossover, genotype_bits, hill_climb, linkage_equilibrium,
     locus_marginals, mutate, nk_fitness, recombine, recombine_teachers,
 )
-from knowledge.genotype_lineage import run_genotype_lineage
-from knowledge.society import make_specialist, run_directed_sex, run_recomb_landscape, run_society
-from knowledge.teachers import make_retention_matrix
+from inheritance.genotype_lineage import run_genotype_lineage
+from inheritance.society import make_specialist, run_directed_sex, run_recomb_landscape, run_society
+from inheritance.teachers import make_retention_matrix
 
 
 def test_genotype_bits_and_additive_fitness():

tests/test_kernel.py

@@ -10,9 +10,9 @@ from __future__ import annotations
 
 import numpy as np
 
-from knowledge.kernel import LearningKernelCfg, apply_kernel
-from knowledge.lineage import run_lineage
-from knowledge.metrics import heterozygosity
+from inheritance.kernel import LearningKernelCfg, apply_kernel
+from inheritance.lineage import run_lineage
+from inheritance.metrics import heterozygosity
 
 
 def test_apply_kernel_identity_is_noop():

tests/test_mating_system.py

@@ -10,8 +10,8 @@ from __future__ import annotations
 
 import numpy as np
 
-from knowledge.mating_system import _distinct_peaks, _diversity, run_mating_system
-from knowledge.genotype import nk_fitness
+from inheritance.mating_system import _distinct_peaks, _diversity, run_mating_system
+from inheritance.genotype import nk_fitness
 
 
 def _run(breadth: float, K: int = 6, seed: int = 0, gens: int = 40, N: int = 32, L: int = 10):

tests/test_mnist.py

@@ -14,7 +14,7 @@ import pytest
 
 pytest.importorskip("torchvision")
 
-from knowledge.metrics import forward_kl  # noqa: E402
+from inheritance.metrics import forward_kl  # noqa: E402
 from neural.config import MnistCfg, ModelCfg, OracleCfg  # noqa: E402
 from neural.mnist_data import (  # noqa: E402
     MnistSampler, assign_modes, fit_thickness_thresholds, make_mnist_truth, thickness_bin,

tests/test_neural_torch.py

@@ -12,7 +12,7 @@ import pytest
 
 pytest.importorskip("torch")
 
-from knowledge.metrics import forward_kl, heterozygosity  # noqa: E402
+from inheritance.metrics import forward_kl, heterozygosity  # noqa: E402
 from neural.config import ModelCfg, SyntheticCfg  # noqa: E402
 from neural.generation_loop import run_generative_lineage  # noqa: E402
 from neural.models import make_model  # noqa: E402

tests/test_neural_validation.py

@@ -4,7 +4,7 @@ With ``model.kind == "histogram"`` the neural generational step (train-on-parent
 + grounding) is *exactly* neutral Wright-Fisher drift with immigration. This module asserts
 that the neural runner reproduces the two closed forms Layer 1 is validated against
 (blueprint 2.4-1 neutral heterozygosity decay, 2.4-3 exact mutation-drift equilibrium) and
-that its mean H-trajectory tracks ``knowledge.lineage.run_lineage`` directly. If any of
+that its mean H-trajectory tracks ``inheritance.lineage.run_lineage`` directly. If any of
 these fail the neural plumbing is wrong — no real network should be trained until they pass.
 """
 
@@ -13,8 +13,8 @@ from __future__ import annotations
 import numpy as np
 import pytest
 
-from knowledge.lineage import run_lineage
-from knowledge.seeding import spawn_seeds
+from inheritance.lineage import run_lineage
+from inheritance.seeding import spawn_seeds
 from neural.generation_loop import run_generative_lineage
 from neural.synthetic import make_mode_truth
 from neural.config import SyntheticCfg

tests/test_scientific_validation.py

@@ -9,7 +9,7 @@ check (the simulator reproduces known population-genetics results) and a code ch
 (the implementation is correct). If any test here fails, the science is wrong, not
 just the code -- do not trust any downstream Layer-1 figure until these pass.
 
-The Layer-1 implementation in `src/knowledge/` is DONE, for validation purposes,
+The Layer-1 implementation in `src/inheritance/` is DONE, for validation purposes,
 when the conformance tests in Part 5 pass against the real package.
 
 HOW IT IS STRUCTURED
@@ -98,7 +98,7 @@ def theory_union_coverage_fraction(K_T: int, rho: float, q: float) -> float:
 # =====================================================================================
 #
 # This is the smallest correct implementation of the core Layer-1 dynamics. It exists
-# so the spine tests can run before src/knowledge/ is written, and so the exact
+# so the spine tests can run before src/inheritance/ is written, and so the exact
 # semantics the package must reproduce are unambiguous. The package will do far more
 # (config, logging, regions, selection, re-minting, per-region metrics); it must agree
 # with THIS on the analytic-check subset.
@@ -388,8 +388,8 @@ class TestMetricSanity:
 # from the minimal one built here, adapt the *construction* of cfg, never the tolerance.
 
 try:
-    import knowledge.metrics as knowledge_metrics
-    import knowledge.teachers as knowledge_teachers
+    import inheritance.metrics as knowledge_metrics
+    import inheritance.teachers as knowledge_teachers
     HAVE_KNOWLEDGE = True
 except Exception:  # package not built yet -> conformance layer skips, spine still runs
     knowledge_metrics = knowledge_teachers = None
@@ -440,7 +440,7 @@ class TestPackageDynamicsConform:
 
     def _run_lineage(self, cfg_overrides, seed):
         lineage = pytest.importorskip(
-            "knowledge.lineage", reason="Layer-1 package not implemented yet")
+            "inheritance.lineage", reason="Layer-1 package not implemented yet")
         base = {
             "truth": {"K": 50, "R": 1, "tail": "zipf", "zipf_s": 1.1,
                       "tail_frac": 0.5, "tail_threshold": 1e-3},

tests/test_speciation.py

@@ -5,7 +5,7 @@ from __future__ import annotations
 import numpy as np
 import pytest
 
-from knowledge.speciation import _bdm_point, _nk_point, run_speciation
+from inheritance.speciation import _bdm_point, _nk_point, run_speciation
 
 
 def test_bdm_parents_carry_no_incompatibility():