Dontopedia

CNN Benchmark

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-06.)

CNN Benchmark has 49 facts recorded in Dontopedia across 11 references, with 5 live disagreements.

49 facts·32 predicates·11 sources·5 in dispute

Mostly:has result row(9), compares(4), involves(3)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (6)

Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.

canRunCan Run(1)

comparesAgainstCompares Against(1)

hasExperimentHas Experiment(1)

includeInclude(1)

isTrainCodeLmOnVqOutputsIs Train Code Lm on Vq Outputs(1)

notConfirmedByNot Confirmed by(1)

Other facts (48)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

48 facts
PredicateValueRef
Has Result RowResult Row 1[5]
Has Result RowResult Row 2[5]
Has Result RowResult Row 3[5]
Has Result RowResult Row 4[5]
Has Result RowResult Row 5[5]
Has Result RowResult Row 6[5]
Has Result RowResult Row 7[5]
Has Result RowResult Row 8[5]
Has Result RowResult Row 9[5]
ComparesV3 Model[3]
ComparesHelmholtz Model[3]
ComparesSymbiogenesis[11]
ComparesBaselines[11]
InvolvesMnist Dataset 5k 1k[11]
InvolvesFashion Mnist Dataset 1k 500[11]
InvolvesCifar 10 Dataset 2k 1k[11]
Has Test RunTest Run Adam[6]
Has Test RunTest Run Fast Muon[6]
Rdf:typeExperiment[8]
Rdf:typeExperiment[10]
Involves ModelHelmholtz[9]
Involves ModelV3[9]
Includes Concurrent Run Thru at Starttrue[1]
Partially Completetrue[1]
Might Need Fresh Runtrue[1]
Is Train Code Lm on Vq OutputsExperiment 1[2]
NamedDepth Efficiency — Complete[3]
FalsifiesHelmholtz Graceful Degradation Hypothesis[3]
Used Venom Amount1/4 gr[4]
Killed Guinea Pig in30 minutes[4]
References TablePaper Table 1[5]
Has Configurationiterations=60[5]
Uses Starting CheckpointCheckpoint 9198[6]
Has Baseline MetricBaseline Metric 1[6]
Has Nameanchor_kan+lohe_v3[7]
Has Statusrunning[7]
Has Step Number58[7]
Has Data Stageearly[7]
Estimated Completion Time20 min[7]
Has Key FindingsKey Findings Set[7]
Prompt Count5[8]
Outcomesame structure[8]
Resulting Statenearly identical outputs[8]
Classified Asbaseline[8]
Has TitleExp 1: Depth efficiency — H vs v3 at 6/4/3 layers × 3 seeds[9]
Has Layer Configurations6/4/3 layers[9]
Has Seed Count3[9]
Is Type ofCnn Benchmark[11]

Timeline

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

includesConcurrentRunThruAtStartblah/watt-activation/part-1
true
partiallyCompleteblah/watt-activation/part-1
true
mightNeedFreshRunblah/watt-activation/part-1
true
isTrainCodeLmOnVqOutputsblah/watt-activation/part-288
ex:experiment-1
namedblah/watt-activation/part-455
Depth Efficiency — Complete
comparesblah/watt-activation/part-455
ex:v3-model
comparesblah/watt-activation/part-455
ex:helmholtz-model
falsifiesblah/watt-activation/part-455
ex:helmholtz-graceful-degradation-hypothesis
usedVenomAmounttrove-cooktown/chinese-fishermen
1/4 gr
killedGuineaPigIntrove-cooktown/chinese-fishermen
30 minutes
labelblah/watt-activation/1
CNN Benchmark
referencesTableblah/watt-activation/1
ex:paper-table-1
hasResultRowblah/watt-activation/1
ex:result-row-1
hasResultRowblah/watt-activation/1
ex:result-row-2
hasResultRowblah/watt-activation/1
ex:result-row-3
hasResultRowblah/watt-activation/1
ex:result-row-4
hasResultRowblah/watt-activation/1
ex:result-row-5
hasResultRowblah/watt-activation/1
ex:result-row-6
hasResultRowblah/watt-activation/1
ex:result-row-7
hasResultRowblah/watt-activation/1
ex:result-row-8
hasResultRowblah/watt-activation/1
ex:result-row-9
hasConfigurationblah/watt-activation/1
iterations=60
usesStartingCheckpointblah/watt-activation/19
ex:checkpoint-9198
hasBaselineMetricblah/watt-activation/19
ex:baseline-metric-1
hasTestRunblah/watt-activation/19
ex:test-run-adam
hasTestRunblah/watt-activation/19
ex:test-run-fast-muon
hasNameblah/watt-activation/219
anchor_kan+lohe_v3
hasStatusblah/watt-activation/219
running
hasStepNumberblah/watt-activation/219
58
hasDataStageblah/watt-activation/219
early
estimatedCompletionTimeblah/watt-activation/219
20 min
hasKeyFindingsblah/watt-activation/219
ex:key-findings-set
typeblah/watt-activation/269
ex:Experiment
promptCountblah/watt-activation/269
5
outcomeblah/watt-activation/269
same structure
resultingStateblah/watt-activation/269
nearly identical outputs
classifiedAsblah/watt-activation/269
baseline
hasTitleblah/watt-activation/449
Exp 1: Depth efficiency — H vs v3 at 6/4/3 layers × 3 seeds
involvesModelblah/watt-activation/449
ex:helmholtz
involvesModelblah/watt-activation/449
ex:v3
hasLayerConfigurationsblah/watt-activation/449
6/4/3 layers
hasSeedCountblah/watt-activation/449
3
typeblah/watt-activation/453
ex:Experiment
isTypeOfblah/watt-activation/464
ex:cnn-benchmark
involvesblah/watt-activation/464
ex:mnist-dataset-5k-1k
involvesblah/watt-activation/464
ex:fashion-mnist-dataset-1k-500
involvesblah/watt-activation/464
ex:cifar-10-dataset-2k-1k
comparesblah/watt-activation/464
ex:symbiogenesis
comparesblah/watt-activation/464
ex:baselines

References (11)

11 references
  1. [1]Part 13 facts
    ctx:discord/blah/watt-activation/part-1
  2. [2]Part 2881 fact
    ctx:discord/blah/watt-activation/part-288
  3. [3]Part 4554 facts
    ctx:discord/blah/watt-activation/part-455
  4. ctx:genes/trove-cooktown/chinese-fishermen
  5. [5]112 facts
    ctx:discord/blah/watt-activation/1
    • full textwatt-activation-1
      text/plain3 KBdoc:agent/watt-activation-1/83ab6e73-1b84-4a84-b9fe-e21a39a0ff4c
      Show excerpt
      [2026-02-25 21:08] lisamegawatts: Tell Claude to use the gelation signal to avoid overfitting to training data, it is a reliable indicator and gives a distinct early signal that can be detected [2026-02-25 21:11] ajaxdavis: https://klipy.co
  6. [6]194 facts
    ctx:discord/blah/watt-activation/19
    • full textwatt-activation-19
      text/plain2 KBdoc:agent/watt-activation-19/e74bc25c-aab8-43ac-90e0-2f036b5a9627
      Show excerpt
      [2026-03-05 22:21] xenonfun: Both started from the same checkpoint, so same baseline: - Start checkpoint ./philosophy_model_fresh/checkpoint_iter_9198.npz - Baseline on same eval slice/settings: val_loss=5.355859, val_ppl=211.85 So
  7. [7]2196 facts
    ctx:discord/blah/watt-activation/219
    • full textwatt-activation-219
      text/plain3 KBdoc:agent/watt-activation-219/c4912ff6-d2ed-42a3-a8a7-43eb7014e9ec
      Show excerpt
      [2026-03-11 04:40] xenonfun: --- Three Things the β Signal Is Revealing 1. β_gate≈0.12 constant = the gate is not working. K=0.177 << K_c=1.33 means β≈25 throughout — we're so deep in the disordered phase that β never varies. To get
  8. [8]2695 facts
    ctx:discord/blah/watt-activation/269
    • full textwatt-activation-269
      text/plain2 KBdoc:agent/watt-activation-269/28541c3c-993b-46f3-bb42-9a8d45d63f59
      Show excerpt
      [2026-03-13 16:50] xenonfun: ⏺ Best val_loss: 0.299 — broke below 0.310 (previous best from diffusion_norm) and still falling at step 5000. New record. ``` ┌────────────────────────────────┬───────┬──────────┬─────────┐ │
  9. [9]4495 facts
    ctx:discord/blah/watt-activation/449
    • full textwatt-activation-449
      text/plain2 KBdoc:agent/watt-activation-449/f6da4410-b0fa-46d2-9cb5-99d205dd2e55
      Show excerpt
      [2026-03-21 03:48] xenonfun: ⏺ You're right. At step 950: ``` ┌──────────┬───────────┬──────────┐ │ Metric │ Helmholtz │ v3 │ ├──────────┼───────────┼──────────┤ │ BPB │ 2.257 │ 2.257 │ ├──────────┼───────────┼
  10. [10]4531 fact
    ctx:discord/blah/watt-activation/453
  11. [11]4646 facts
    ctx:discord/blah/watt-activation/464
    • full textwatt-activation-464
      text/plain3 KBdoc:agent/watt-activation-464/599938d0-3182-4b42-bb04-4488236f82bc
      Show excerpt
      [2026-03-21 18:08] xenonfun: ``` Key observations: - Rust achieves significantly higher accuracy (99.1% vs 89.5% best) — the GPU-accelerated training does more effective optimization per epoch - Gelation detected at the same step (12)

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