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

src/neural/experiment.py

@@ -1,6 +1,6 @@
 """Neural (Layer 1.5) experiment runner: sweep a grid x replicates, write artifacts.
 
-Mirrors ``knowledge.experiment`` and reuses its sweep-expansion primitives
+Mirrors ``inheritance.experiment`` and reuses its sweep-expansion primitives
 (``_apply_param`` — including the ``g -> m`` conversion — and ``_set_by_path``), its
 provenance helpers, and its output contract (``save_artifacts``). Only the per-run call and
 the config key set differ: a neural run trains generative models rather than resampling a
@@ -20,8 +20,8 @@ from typing import Any
 import pandas as pd
 import yaml
 
-from knowledge.experiment import _apply_param, save_artifacts
-from knowledge.seeding import spawn_seeds
+from inheritance.experiment import _apply_param, save_artifacts
+from inheritance.seeding import spawn_seeds
 
 from .generation_loop import run_generative_lineage
 
@@ -38,7 +38,7 @@ _EXTRA_LIBS = ("torch", "torchvision")
 def expand_sweeps(cfg: dict) -> list[tuple[dict, dict]]:
     """Expand the sweep grid into (label, resolved_neural_cfg) pairs.
 
-    Identical semantics to ``knowledge.experiment.expand_sweeps`` (Cartesian product of the
+    Identical semantics to ``inheritance.experiment.expand_sweeps`` (Cartesian product of the
     declared ``{param, values}`` entries, reusing ``_apply_param`` for the ``g -> m`` and
     ``arm`` special cases) but assembling the base from the neural config groups.
 
@@ -129,7 +129,7 @@ def run_mnist_experiment(cfg: dict) -> tuple[pd.DataFrame, dict]:
     Returns:
         tuple[pd.DataFrame, dict]: Long-form results and the oracle-provenance manifest dict.
     """
-    from knowledge.config import _sub
+    from inheritance.config import _sub
 
     from .config import MnistCfg, OracleCfg
     from .mnist_data import assign_modes, load_mnist, make_mnist_truth, MnistSampler