second review round: tempered claims, robust statistics, corrected technical statements

Analyses (figures/stats_llm_epistasis.py, committed + reproducible):
condition-clustered bootstrap CIs (functional measures exclude zero:
dis_raw [+0.04,+0.69], conf-weighted [+0.02,+0.68]; gradient alignment
[-0.59,-0.06]; geometry straddles zero), PAIRED predictor contrasts (not
individually significant — stated), leave-one-condition-out held-out
prediction (functional replicates, geometry ~0, performance baseline
unstable), three outcome references (ordering sensitive to reference —
reported, with the mechanism), between/within-axis decomposition
(within-conflict identification impossible by design; the compat axis
identifies), and seed-level paired reliability (routing/directed beat
soup 3/3 seeds incl. one catastrophic soup failure; CI-width fragility
claim withdrawn).

Renames and corrections: "decisive experiment" -> "controlled predictive
test"; "operational epistasis" -> "confidence-weighted functional
conflict (proposed proxy)"; "functional by construction" -> "controls a
major source of coordinate mismatch / conflict-associated" (module,
configs, READMEs, figures); SI proposition's "chord" defined precisely
(endpoint-loss interpolation, invariant) vs the path (not invariant) +
no-global-optimality caveat (removable = lower bound, residual = upper);
snowball count != performance cliff distinction added; claims table
gains four rows (grid finding / weighting NOT supported / functional-vs-
all-geometry not established / operator choice open); §1 ladder states
the prediction rung as a bounded small-model result.

paper/response-to-review-2.md: point-by-point, opening with the
bookkeeping correction (E13b/c were in the reviewed draft — revised
interpretation, not new results). READMEs rewritten around the four
analyses with the chronology (prospective/adaptive/post-hoc) disclosed.
151 tests green.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v

paper/si-notes.md

@@ -12,10 +12,17 @@ condition, the cyclically-relabelled classes; `μ(S) ≈ conflict_frac` up to cl
 networks, positive per-unit rescalings — the full unit symmetry group of a plain ReLU MLP) satisfies
 `T(B)(x) = B(x)` for all `x` by construction.
 
-**Proposition 1 (endpoint invariance).** For every function-preserving `T`, the endpoint functions —
-and hence the endpoint losses/errors and the linear chord between them — are identical for the pair
-`(A, T(B))` and the pair `(A, B)`. Alignment can only re-coordinate the *interpolation path*, never
-the endpoints or the chord. *(Immediate from the definition of function-preserving.)*
+**Proposition 1 (endpoint invariance — with the term "chord" defined precisely).** Here "chord"
+means the α-linear interpolation **of the endpoint loss values**, `(1−α)·L(A) + α·L(B)` — the
+baseline in the barrier definition, a function of the endpoints only — NOT the weight-space
+interpolation path. For every function-preserving `T`, the endpoint functions, hence the endpoint
+losses and this chord, are identical for `(A, T(B))` and `(A, B)`. The **interpolation path itself is
+generally NOT invariant** — losses along `(1−α)·A + α·T(B)` change with `T`, which is precisely why
+alignment can lower a barrier. *(Immediate from the definition of function-preserving.)* Scope
+caveat: our aligner provably recovers a permuted-and-rescaled copy exactly — an important special
+case — but this does not establish global optimality of the alignment over the symmetry group for
+independently trained networks; the decomposition's "removable" share is therefore a lower bound, and
+the "residual" an upper bound, on their true values.
 
 **Proposition 2 (no merged model can serve both parents).** Let `h` be *any* single classifier (in
 particular, any interpolated/merged model, under any alignment). On every `x ∈ S`, `f_A(x) ≠ f_B(x)`,