Results Ledger
SE Program Results Ledger
This is a durable registry, not marketing. It logs every experiment, grid-cell, method, and lever tried across the structural-entropy (SE) research program, with its outcome and the exact artifact the number lives in. The purpose is to avoid redundant re-runs: before starting any SE experiment, check here for what we already know.
Anti-fabrication rule: every result is quoted from an artifact (a result JSON/JSONL,
a paper .tex, or a FINDINGS log). Where a source has no extractable number, the row
says “not run / no artifact” rather than guessing.
Status legend: ✅ settled · ⚠️ low-n / preliminary · ❌ null / inconclusive / retracted · 🔄 running · ⬜ not run (no code / not executed).
Four threads: Benchmark reproductions · Graph-SE · DM-SISA · SeSE calibration.
Row counts: Benchmark 30, Graph-SE 21, DM-SISA 15 levers + 5 washed-out, SeSE 17.
All paths are absolute on the research box; JSON paths are stable, .tex/JSONL cite commit where relevant.
Thread 1 — Benchmark reproductions
Repo root: /home/ubuntu/structural-entropy-benchmark/. Registry of methods ×
repos: registry/methods.yaml. Result JSONs: results/<family>/<Method>__<Dataset>__<date>.json
(arrays = one entry per seed). Verdicts: FINDINGS_phase1.md, FINDINGS_new_tracks.md, PROGRESS.md.
Headline finding for the registry: on topology-only community detection, SE methods (deDoc, CoDeSEG) do not beat modularity / map-equation / spectral. SE helps only with node features (DeSE) or genuine block structure (SBM-Clean). Even with features, SE does not reliably beat plain Louvain. Do not re-chase “SE beats classical CD on plain graphs” — it is settled negative.
Community detection — results/community_detection/
| What was tried | Status | Result (honest) | Source |
|---|---|---|---|
| deDoc (SE-min, orig Java) × LFR μ0.1–0.6 | ✅ | Fails: ARI ≈ 0 at all μ; over-segments to ~993–998 comms (24 true), NMI ~0.62 | deDoc__LFR-mu0.{1..6}__2026-06-02.json |
| deDoc × SBM-Clean | ✅ | Recovers blocks: ARI 0.990, NMI 0.985, 4 comms | deDoc__SBM-Clean__2026-06-02.json |
| deDoc × SBM-Noisy | ✅ | Fails at threshold: ARI 0.0015, 146 comms | deDoc__SBM-Noisy__2026-06-02.json |
| deDoc × Karate | ✅ | Over-segments: ARI 0.0151, NMI 0.340, 31 comms | deDoc__Karate__2026-06-02.json |
| deDoc scalability N=50–10000 | ✅ | ~O(N^1.4), 74.2s @ N=10k; refutes “O(N³) infeasible” claim | deDoc__SBM-scalability__2026-06-02.json |
| CoDeSEG (SE-game, orig C++) × LFR μ0.1–0.6 | ✅ | ARI 1.00→0.02; over-segments (140→425 comms) | CoDeSEG__LFR-mu0.{1..6}__2026-06-02.json |
| CoDeSEG × SBM-Clean / Noisy / Karate | ✅ | Fails SBM (ARI 0.162 / 0.008); Karate ARI 0.185 | CoDeSEG__SBM-{Clean,Noisy}__…, CoDeSEG__Karate__2026-06-02.json |
| DeSE (deep SE + features, PyTorch, KDD25) × Cora | ✅ | 5-seed NMI mean ≈ 0.512, ARI ≈ 0.437 (acc/f1 have Munkres bug — use NMI/ARI) | DeSE__Cora__2026-06-02.json |
| DeSE × Citeseer | ✅ | 5-seed NMI ≈ 0.400, ARI ≈ 0.399 | DeSE__Citeseer__2026-06-02.json |
| DeSE × Photo | ⬜ | Not run — CPU-infeasible (>1h/seed), per FINDINGS_phase1 §5 | no JSON |
| LSENet (hyperbolic SE, ICML24) × Cora | ⚠️ | acc 0.631 / NMI 0.495 / ARI 0.418 (single best-by-NMI point, not 5-seed) | LSENet__Cora__2026-06-0{2,3,4}.json |
| LSENet × Citeseer | ⚠️ | Fails: NMI 0.067→0.094, ARI 0.070→0.113 (method-fragile) | LSENet__Citeseer__2026-06-0{2,3,4}.json |
| LSENet × Photo | ⚠️ | NMI 0.594→0.667, ARI 0.456→0.574 (config fix bumped it) | LSENet__Photo__2026-06-0{2,3,4}.json |
| Louvain / Leiden / Infomap / Spectral × LFR/SBM/Karate | ✅ | Baselines; Infomap collapses μ≥0.5 (ARI 0); Spectral best on Karate (ARI 0.882) | {Louvain,Leiden,Infomap,Spectral}__*__2026-06-02.json |
| Same 4 baselines × Cora/Citeseer/Photo (topology-only) | ✅ | Louvain Cora ARI 0.246/NMI 0.450; Photo 0.564/0.658; Spectral collapses (NMI ~0.04) | {…}__{Cora,Citeseer,Photo}__2026-06-03.json |
Note:
FINDINGS_phase1.mdquotes LSENet as “5-seed mean±std”; the JSON artifacts hold single best-by-NMI points — the ± is not reconstructible from these files._summary/accuracy.md+_summary/cross_objective.mdare derived tables, not primary sources.
Graph learning — results/graph_learning/
| What was tried | Status | Result (honest) | Source |
|---|---|---|---|
| SEP (SE graph pooling, ICML22, PyG2.5) × 7 TU datasets | ✅ | Reproduced 7/7. test_acc: PROTEINS 0.802, DD 0.810, NCI1 0.804, MUTAG 0.928, IMDB-B 0.781, IMDB-M 0.547, COLLAB 0.829 | SEP__{…}__2026-06-0{3,4}.json |
| SEGA (SE contrastive, ICML23) × PROTEINS, IMDB-B | ⚠️ | Single-run unsup SVM: PROTEINS accG 0.751, IMDB-B 0.709. MUTAG (~85-87% in PROGRESS) has no JSON | SEGA__{PROTEINS,IMDB-BINARY}__2026-06-04.json |
| SE-GSL (SE graph-structure learning, WWW23) × Cora | ✅ | test_acc 0.869±0.015 (GCN); GAT 0.880; SAGE 0.885. Citeseer/APPNP not present | SE-GSL__{Cora,gat_cora,sage_cora}__2026-06-0{3,4}.json |
| Baselines DiffPool / MinCutPool / DMoN (SEP comparison, 3 seeds) | ✅ | SEP > DiffPool/MinCut on PROTEINS/NCI1, ~tie MUTAG/IMDB-B | {DiffPool,MinCutPool,DMoN}__*__2026-06-0{5,6}.json |
Other task families
| What was tried | Status | Result (honest) | Source |
|---|---|---|---|
| SI2E (SE intrinsic reward, MiniGrid, PPO) × KeyCorridorS3R1 (easy) | ✅ | Reliably solves: success 0.90–0.91 (3 seeds) | SI2E__kc1-s{1,2,3}__2026-06-04.json |
| SI2E × DoorKey-8x8 (medium) | ❌ | Bimodal: success {0.94,0.94,0.94,0.25,0.07,0.00} — unstable | SI2E__{DoorKey-8x8,dk8-s*,si2e}__2026-06-0{3,4}.json |
| SI2E × RedBlueDoors | ❌ | Bimodal: {0.89, 0.06, 0.82}. (PROGRESS text says 0.76; JSON says 0.82 — trust JSON) | SI2E__rbd-s{1,2,3}__2026-06-04.json |
| SI2E × KeyCorridorS3R2 (hard) | ❌ | Fails: success 0.00 / 0.02 / 0.00 | SI2E__kc-s{1,2,3}__2026-06-04.json |
| Hierarchy quality (Dasgupta cost) — SE-agglom vs average/ward/Paris × 5 graphs | ✅ | SE wins 4/5: best on SBM-Clean/6blk, LFR-μ0.1/0.4; 2nd on Karate (avg wins) | hierarchy-quality__{Karate,SBM-Clean,SBM-6blk,LFR-mu0.1,LFR-mu0.4}__2026-06-04.json |
| SSE (semi-supervised SE, UCI, ratio 0.2, 10 reps) × 4 datasets | ✅ | acc: wine 0.930, breast-cancer 0.967, australian 0.739, heart 0.749 | SSE__{wine,breast-cancer,australian,heart}__2026-06-04.json |
| UnDBot (SE social-bot detection) × botwiki-2019 | ⚠️ | Single dataset/run: AUC 0.883, F1 0.513 (large sets OOM) | UnDBot__botwiki-2019__2026-06-04.json |
| SuperTAD (SE TAD-calling, Genome Biol 21, C++) × Hi-C GM12878/IMR90 | ⚠️ | “20 hierarchical TADs” reported prose-only — results/bioinformatics/ is EMPTY, no JSON backs it |
FINDINGS_new_tracks.md (Track 3), PROGRESS.md #10 |
Registry methods with no results (not run / no code)
| Method (family) | Status | Note | Source |
|---|---|---|---|
| deDoc2 (CD) | ⬜ | Repo verified, not run | registry/methods.yaml |
| SEP_HCSE / HCSE builder (GL) | ⬜ | Same repo as SEP; no separate JSON | registry/methods.yaml |
| SEAT, SSSE, HiTIN | ⬜ | Repos verified, not run | registry/methods.yaml |
| SIDM / SIRD (JMLR25, RL) | ⬜ | Recipe banked; heavy StarCraftII dep (PROGRESS #8 pending) | registry/methods.yaml, PROGRESS.md |
| COLLAB (SDM25) | ⬜ | Verified, not run | registry/methods.yaml |
| SeSE (LLM-UQ) | ⬜ | GPU-gated (≥24GB / gated LLaMa); see Thread 4 for the separate calibration study | FINDINGS_new_tracks.md (Track 2) |
| USER, HiPART (GL); SISA, SIRD (RL) | ⬜ | Repo to-locate (verified:false) — no code located |
registry/methods.yaml |
Thread 2 — Graph-SE (attention prior)
Repo: /home/ubuntu/SE-graph-learning/. Stage sweeps: experiments/d6_diffse/results/*.jsonl.
Master log: experiments/d6_diffse/FINDINGS.md. Paper: /home/ubuntu/se-attention-prior-paper/main.tex
(“The Multi-Level Edge: Structural Entropy as a Hierarchical Prior for Graph Attention”, compiled 2026-07-03).
Headline: differentiable multi-level soft-SE added as an attention-graph regularizer improves low-label node classification; flat SE only ties modularity/MinCut. The SE-specific lever is the multi-level encoding tree, confirmed against a capacity control and per-method-λ control. Not SOTA (below tuned GCNII/APPNP). Program is experimentally complete.
| Stage — what was tried | Status | Result (honest) | Source |
|---|---|---|---|
| S9 — k-robustness: reg{none,se,mod,mincut} × k{0..128} × 8 datasets × 7 seeds | ✅ | SE never collapses (worst-case-over-k ≥ −0.004 on all 8); mod collapses Citeseer (−0.020), MinCut Citeseer (−0.038). “SE beats both” fails (5/8); “bounded downside” holds | stage9_krobust_full.jsonl (1232 rows) |
| S10 — mega: {none,se,dropedge,selftrain,se+st} × label-rate × 8 × 7 | ✅ | SE > DropEdge 8/8; SE > self-train only 5/8 (self-train wins CoraFull/Photo/CS) | stage10_mega.jsonl (728) |
| S11 — λ sweep {0,0.01,0.03,0.1,0.3} | ✅ | Inverted-U, peak λ ≈ 0.03 | stage11_lambda.jsonl (480) |
| S12 — heterophily k-robustness × 5 datasets | ✅❌ | Settled NULL: SE gain Texas +0.012, Cornell −0.018, Wisconsin −0.050, cham −0.010, squirrel −0.004 → inert/harmful 4/5. Confirms homophily-only scope | stage12_hetero.jsonl (770) |
| S13 — multi-level vs flat SE vs mod vs mincut × 8 × 15 seeds | ✅ | se_ml − se_flat +2.0pp Wilcoxon p<1e-5 (n=120), 6/8; se_ml > mod +3.7pp p<1e-6 (7/8); > MinCut +1.4pp p=3e-6 (6/8). MinCut beats se_ml on CoraFull (honest exception) | stage13_ml.jsonl (600) |
| S14 — depth L{2,3,4} × 5 datasets × 10 seeds | ✅ | Inverted-U, L3 peak 4/5; pooled L3>L2 +2.6pp p=2e-4, L3>L4 +2.3pp p=2e-5 | stage14_depth.jsonl (150) |
| S15 — strong backbone (3-layer residual+JK GAT) × 4 × 10 | ✅ | se_ml > se_flat > none 4/4; se_ml>none +11.5pp p<1e-6; se_ml>se_flat +3.3pp p=3e-4 | stage15_strongbb.jsonl (120) |
| S16 — capacity control: ml_164 vs flat{4,16,64} × 4 × 10 | ✅ | ml wins 4/4 (Cora 0.713, Citeseer 0.584, CoraFull 0.278, Photo 0.808); flat_64 worst → hierarchy, not capacity, is the lever. Pooled +6.8pp p<1e-6 | stage16_capacity.jsonl (160) |
| S17 — planted-hierarchy depth{2,3} × L{2,3,4} × 30 seeds | ✅ | Weak signal: planted-3 peaks at L3 (0.457), L3>L2 p=0.0014, L3>L4 p=0.042; planted-2 ~flat (tie) | stage17_planted.jsonl (180) |
| S17b — planted HARD variant, same grid | ❌ | NULL: L irrelevant within each depth (all within ~0.001). Unreported negative control; paper uses S17 (has headroom). No committed .py |
stage17b_hard.jsonl (180) |
| S18 — per-method optimal-λ recheck × 4 × λ{4 vals} × 8 | ✅ | Even at each method’s own best-λ: se_ml wins Cora/Citeseer/Photo; MinCut wins CoraFull. Reg-strength confound does not explain the win. No committed .py |
stage18_lambda.jsonl (384) |
| D5 — SE-agglomerative hierarchical clustering × 34 graphs | ✅ | SE beats all baselines 33/34; SE vs Paris Wilcoxon p=1.16e-10, median +10.3%. Caveat: Dasgupta cost gameable → switched to NMI (SE 0.897 syn / 0.700 real) | experiments/d5_hierarchy/FINDINGS.md |
| D1 — SEP pooling ablation: SE tree vs shape-matched random | ✅❌ | NULL/negative: “structurally meaningful, practically irrelevant” — SE trees ≈ random-tree accuracy (PROTEINS .755 ≈ .757) despite 3-4× more cohesive clusters; cohesion-gate doesn’t rescue | experiments/d1_ablation/FINDINGS.md, README.md |
| BH-FDR correction across 24 se_ml-vs-baseline tests | ✅ | 15/24 survive p<0.05; failures = known nulls (DBLP, Pubmed/Computers vs se_flat, CoraFull vs MinCut) | HANDOFF_TO_FABLE5_2026-07-02.md |
| Transformer-transfer of the prior | ❌ | Negative (kept in paper Limitations) | se-attention-prior-paper/main.tex |
| SOTA comparison (vs tuned GCNII/APPNP, GRAND, C&S) | ❌ | Not SOTA — below tuned baselines in absolute accuracy (honest, in Limitations) | se-attention-prior-paper/main.tex L349 |
Provenance flags: S16/S17/S18 cell means were recomputed directly from the JSONL and match FINDINGS/paper to rounding. S17b and S18 JSONL exist without a committed script (backed up in commit
48a2f8b).
Thread 3 — DM-SISA (RL levers)
Repo: /home/ubuntu/dm-sisa/ (HEAD 5b85963). Levers dashboard: docs/LEVERS.md.
Paper: /home/ubuntu/dmsisa-paper/main.tex (HEAD ef06b95 = the correction commit).
Baseline to beat: SISA.
Overall verdict (corrected): the 4-way comparison (RAD-SAC / SISA / DM-SISA λ0.01 / DM-SISA λ0) is statistically inseparable at n=5 (same hardware+seeds). The contribution is formulation + efficiency, not a return beat. The earlier “over-regularization” claim is RETRACTED (2-seed artifact). The real, surviving result is ~16,000× cheaper abstraction (1.3 ms vs 21.4 s per call).
4-way late-phase return, n=5, RTX 3060 (main.tex Table tab:main, L166–186):
walker/walk RAD-SAC 652±94 · SISA 552±190 · DM-SISA λ0.01 774±63 · λ0 676±39;
cheetah/run 475±25 · 412±73 · 407±104 · 349±101. Only raw-significant contrast (λ0.01 vs SISA p=0.016)
fails 4-test correction (p=0.064) and leans on one SISA seed collapsing to 191.
Levers L1–L10 — docs/LEVERS.md
| Lever | What it is | Status | Result (honest) | Source |
|---|---|---|---|---|
| L1 | λ_SE = 0 (no aux penalty) | ✅ | Parity + more stable: walker 676±39 vs SISA 552±190 (p=0.22); ~5× lower variance; 16000× cheaper | docs/LEVERS.md |
| L2 | λ_SE = 0.01 (default) — does SE over-regularize? | ❌ RETRACTED | No effect: walker 774±63 ≥ λ0’s 676 at n=5; the 562 (n=2) was an artifact (p=0.10/0.55) | docs/LEVERS.md, paper L188–194 |
| L3 | λ_SE = 0.003 | ⚠️ | Small-λ hint: 735.8 (n=1) > λ0’s 676, but never reached rigorous n — qualitative only | docs/LEVERS.md |
| L4 | λ_SE = 0.001/0.002/0.005 (bracket optimum) | ⬜ | Planned, pending | docs/LEVERS.md |
| L5 | Policy-input injection (--dmsisa_inject) |
❌ (infra) | Built + trains clean, but every run crashed on flaky box — 0 completions. Infra, not method | docs/LEVERS.md |
| L6 | Depth: multi-level vs flat | ⚠️ | Running on single slow GPU; insufficient n | docs/LEVERS.md |
| L7 | Deeper 3-level hierarchy | ⬜ | Not run | docs/LEVERS.md |
| L8 | Community count k1/k2 sweep | ⬜ | Not run | docs/LEVERS.md |
| L9 | Aux-task coefficient tuning | ⬜ | Not run | docs/LEVERS.md |
| L10 | Distractor / hard-obs regime | ⬜ | Not run | docs/LEVERS.md |
| — | Efficiency (soft-SE vs discrete tree) | ✅ | ~16,000× faster per abstraction call: 1.32 ms vs 2.14×10⁴ ms | paper Abstract L34–36, tab:validation L140–142 |
| — | Correctness gate: one-hot soft-SE = discrete SE | ✅ | |Δ| = 1.15×10⁻⁷ (pass) | paper tab:validation |
| — | 4-way n=5 return comparison | ✅ | Statistically inseparable (see above) | paper tab:main L166–186 |
| — | Over-regularization claim | ❌ | WITHDRAWN | paper L188–194, commit ef06b95 |
| — | Stop condition | ✅ | “No lever produced a significant beat” (LEVERS.md closing note, 2026-07-05) | docs/LEVERS.md |
⚠️ Washed-out signals — DO NOT RE-CHASE
Five single-seed / low-n “wins” that regressed to parity under more seeds. These are settled dead ends; re-running them wastes GPU. (No single enumerated list existed in the artifacts — reconstructed and traced below.)
| # | Signal (as originally claimed) | Original | Washed out to | Source |
|---|---|---|---|---|
| S1 | Walker “beat” over SISA | Δ+71, 1-sided p=0.036 (n=3) | Δ+35, p=0.41 n.s. (n=4) → parity | LEVERS.md; commits 42fa252→c97f266 |
| S2 | λ0.01 over-regularizes | 562±41 (n=2), −22% vs λ0 | 774±63 (n=5) ≥ 676 — RETRACTED | LEVERS.md L2; paper ef06b95 L188–194 |
| S3 | DM-SISA(λ0) single-seed win over SISA | 711.4 (n=1) > 691.4 (n=1) | 676±39 vs 725±107 (n=5), p=0.55 n.s. | paper commits 7250357→bb926b0 |
| S4 | Naive DM-SISA return cost “−19%” | 558.8 (n=1) | Subsumed into retracted over-reg claim | paper commit 7250357 |
| S5 | λ0.01 vs SISA significance | raw p=0.016 | Fails 4-test correction p=0.064; leans on one collapsed seed (191) | paper ef06b95 L177–179 |
Thread 4 — SeSE (LLM calibration)
Repo: /home/ubuntu/sese-calibration-paper/. All numbers live in main.tex (Table tab:cells
L88–119; prose L73–142; abstract L19–38) — the repo has no result JSON/CSV; raw run data is not
committed, so main.tex is the durable source. Verdict history in commits 1dcbe9c, dd589c3, b17ff79.
Headline: the sign of the chain−answer AUROC gap (does chain-level SE beat answer-level SE?) is set by the model’s family / calibration, not competence. Only the badly-overconfident gemma-3-12b is a chain-SE winner. CORR = 0.75 (n=17, gemma-anchored) between overconfidence and the gap. The cross-family Mistral probe is a clean negative. Columns below: Acc / Overconf (peak−acc) / Gap (chain−answer AUROC; positive = chain-SE wins).
| Model × dataset | Status | Result (Acc / Overconf / Gap) | Source |
|---|---|---|---|
| Qwen2.5-0.5B × GSM8K | ✅ | 0.397 / +0.113 / −0.007 (answer-SE wins) | main.tex L94 |
| Qwen2.5-0.5B × SVAMP | ✅ | 0.508 / +0.124 / −0.066 | main.tex L95 |
| Qwen2.5-0.5B × AQuA | ✅ | 0.300 / +0.171 / −0.123 | main.tex L96 |
| Qwen2.5-1.5B × GSM8K | ✅ | 0.612 / +0.091 / −0.023 | main.tex L97 |
| Qwen2.5-1.5B × SVAMP | ✅ | 0.760 / +0.068 / −0.055 | main.tex L98 |
| Qwen2.5-3B × GSM8K | ✅ | 0.827 / +0.021 / −0.069 | main.tex L99 |
| Qwen2.5-3B × SVAMP | ✅ | 0.842 / +0.050 / −0.089 | main.tex L100 |
| Qwen2.5-7B × GSM8K | ✅ | 0.787 / +0.038 / −0.221 | main.tex L101 |
| Phi-3.5-mini × GSM8K | ✅ | 0.875 / +0.038 / −0.080 (answer-SE wins) | main.tex L102 |
| gemma-3-12b × GSM8K (s1) | ✅ | 0.410 / +0.400 / +0.122 (chain-SE wins) | main.tex L103 |
| gemma-3-12b × GSM8K (s2) | ✅ | 0.385 / +0.409 / +0.157 | main.tex L104 |
| gemma-3-12b × GSM8K (s3) | ✅ | 0.430 / +0.358 / +0.183 | main.tex L105 |
| gemma-3-12b × SVAMP | ✅ | 0.340 / +0.460 / +0.083 (chain-win robust across task) | main.tex L106 |
| Mistral-7B × AQuA (cross-family probe) | ⚠️ | overconf +0.19, gap −0.09 (answer-SE wins), n=2 → clean NEGATIVE; Mistral not in chain-winning regime | main.tex L132–139, commit 1dcbe9c |
| Mistral-7B × GSM8K | ❌ | EXCLUDED (parse artifact): 73% empty extracted answers → spurious; removed by >25%-empty validity filter | main.tex L108–109, L121–125 |
| CORR(overconfidence, gap) headline | ✅ | ρ = 0.75 (n=17), gemma-anchored, validity-filtered set | main.tex L31–32, L83, L139; commit 1dcbe9c |
| Degenerate-answer-graph mechanism | ❌ | Falsified: outlier’s answer diversity ≈ strong model’s (qualitative, no numeric artifact) | main.tex L78–81 |
Not run / open (no numbers — do not invent): full AQuA column for Qwen/Phi/gemma is 🔄 running
(main.tex L117); ECE / selective-generation curves are ⬜ not run (\TODO L141–142) — no ECE value
exists anywhere in the repo; additional overconfident models (gemma-2, Llama-3.2) ⬜ gated/inaccessible.
Internal-consistency flag: the initial 13-cell correlation is reported as 0.825 (L83, commit
dd589c3) but rounded to 0.83 in the abstract (L33, commit1dcbe9c); the current headline is the n=17 value 0.75.
What is already settled (do not redo)
- Topology-only community detection: SE (deDoc, CoDeSEG) does not beat modularity/map-eq/spectral. SE needs features or block structure. (Thread 1)
- DM-SISA return: no lever beats SISA; 4-way is statistically inseparable at n=5. The contribution is the 16,000× efficiency, not returns. The 5 washed-out signals above are dead ends. (Thread 3)
- DM-SISA over-regularization: retracted — do not report it. (Thread 3)
- Graph-SE flat objective: flat SE only ties modularity/MinCut; only multi-level SE is the real lever, and it is not SOTA. Heterophily is out of scope (inert/harmful). D1 pooling ablation is a settled NULL. (Thread 2)
- Graph-SE planted-depth (hard regime, S17b): NULL — the depth-peaks-at-L result does not survive; don’t cite it. (Thread 2)
- SeSE: chain-SE only wins for badly-overconfident models (gemma-3); cross-family Mistral is negative. (Thread 4)