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Phase 4i: strong IRED graph reasoner still ties softmax — the flip is task-specific, not accuracy-gated - #5

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Phase 4i: strong IRED graph reasoner still ties softmax — the flip is task-specific, not accuracy-gated#5
dkjo8 merged 1 commit into
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@dkjo8 dkjo8 commented Jul 27, 2026

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Refutes the natural explanation for the graph tie (a weak IRED reasoner) by scaling it up — an honest negative result.

Test

The Phase-4h IRED graph reasoner capped at ID ~0.58 and only tied softmax. Hypothesis: with a stronger reasoner it would flip (like arithmetic). We scaled the model (hidden 256 / latent 48, more training) to ID ~0.83 — as strong as arithmetic, low variance.

Result (5 seeds, strong IRED graph)

vs scalar energy vs best softmax
strong IRED graph (ID 0.83) +0.092±0.029, 5/5 +0.001±0.003, 0/5 (clean ties)

Geometry still beats scalar energy 5/5 but still only ties softmax — so the graph tie is not a weak-reasoner artifact. The arithmetic flip (4/5) is genuinely task-specific, not a function of accuracy nor a general property of learned landscapes: on graph a well-calibrated softmax already captures what descent geometry would add.

Paper (abstract/experiments/conclusion) + docs updated to state this boundary plainly; the open question becomes when descent geometry adds over softmax. 92 tests green, ruff clean; paper rebuilds. Default reasoner unchanged; T1 stays canonical basin-center.

🤖 Generated with Claude Code

…k-specific, not accuracy-gated

Tests the "the graph tie is just a weak reasoner" hypothesis by scaling the IRED graph model
(hidden 256 / latent 48) to ID ~0.83 — as strong as arithmetic, low variance.

- configs/experiments/graph_ired.toml: stronger model (h256/l48, e110, n_train 12000), n_eval 700
  (a distinct (K, n_test) group from the Phase-4h weak run).

RESULT (5 seeds, strong IRED graph, ID 0.831±0.012): geometry still beats scalar energy 5/5
(dAURC +0.092±0.029) but only TIES the best softmax baseline (dAURC +0.001±0.003, 0/5 seeds clear 0).
So the graph tie is NOT a weak-reasoner artifact — the hypothesis is refuted. The arithmetic flip is
genuinely task-specific: on graph a well-calibrated softmax already captures what descent geometry
would add, even under a strong learned landscape. Paper (abstract/experiments/conclusion) + docs
updated to state this boundary plainly; the open question becomes *when* geometry adds over softmax.
92 tests green, ruff clean; paper rebuilds.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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dkjo8 merged commit bf960a7 into main Jul 27, 2026
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dkjo8 deleted the phase-4i-ired-graph-strong branch July 27, 2026 17:36
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