Dontopedia

ResonantWireLM

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

ResonantWireLM has 212 facts recorded in Dontopedia across 21 references, with 16 live disagreements.

212 facts·172 predicates·21 sources·16 in dispute

Mostly:has hyperparameter(9), has readout dim(5), rdf:type(4)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (32)

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.

partOfModelPart of Model(12)

partOfPart of(2)

advocatesScalingAdvocates Scaling(1)

associatedWithResonantWireLmAssociated With Resonant Wire Lm(1)

describedPerBlockPerTokenDescribed Per Block Per Token(1)

doesNotApplyToDoes Not Apply to(1)

existsInContextOfExists in Context of(1)

existsInResonantwirelmExists in Resonantwirelm(1)

forModelFor Model(1)

hasModelHas Model(1)

includesResonantWireLMIncludes Resonant Wire Lm(1)

inquiresAboutMemoryRequirementsInquires About Memory Requirements(1)

isBottleneckIs Bottleneck(1)

isCheckpointForIs Checkpoint for(1)

isStandaloneIs Standalone(1)

isUnmeasuredIs Unmeasured(1)

offersToTrainOffers to Train(1)

postedModelInfoPosted Model Info(1)

specificToSpecific to(1)

statesThatStates That(1)

Other facts (206)

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.

206 facts
PredicateValueRef
Has HyperparameterG=8[15]
Has HyperparameterH=4[15]
Has HyperparameterL=6[15]
Has Hyperparameterheads=4[15]
Has Hyperparameterk_probes=4[15]
Has Hyperparameterconstellation_temp=0.254[17]
Has Hyperparameterreadout_dim=93[17]
Has Hyperparameterconstellation_temp=0.100[21]
Has Hyperparameterreadout_dim=93[21]
Has Readout Dim93[5]
Has Readout Dim93[9]
Has Readout Dim93[10]
Has Readout Dim64[13]
Has Readout Dim93[18]
Rdf:typeModel Architecture[15]
Rdf:typeModel Architecture[19]
Rdf:typeModel Architecture[20]
Rdf:typeModel[21]
Has Params33881[2]
Has Params27129[10]
Has Params49506[13]
Has Constellation Temp0.109[5]
Has Constellation Temp0.101[9]
Has Constellation Temp0.1[10]
Has Fundamentally Different Block Structure ThanHarmonicgpt[1]
Has Fundamentally Different Block Structure ThanWirelm[1]
Has Highest ThroughputAll Tested Models[2]
Has Highest Throughputtrue[15]
Loaded in Ms54[5]
Loaded in Ms64[9]
Has G8[5]
Has G8[9]
Has Param Count27057[5]
Has Param Count30297[9]
Has H2[5]
Has H2[9]
Has L12[5]
Has L24[9]
Has Heads4[5]
Has Heads4[9]
Has Num Heads2[6]
Has Num Heads4[13]
UsesHelmholtz Fiber[8]
UsesGivens Coupling[8]
Has CharacteristicPure Rotation[16]
Has CharacteristicGeometry[16]
Lacks ConceptSnr[16]
Lacks ConceptBandwidth[16]
Load Time56[17]
Load Time52[21]
Best Validation Step500[17]
Best Validation Step9500[21]
Has Memory Size0.11[18]
Has Memory Size0.11[21]
Contrasts WithWirelm Block Structure[1]
Should Be Wired IntoHarmonicmlx Trainer Py[1]
Commits to BeingValid Model Type[1]
Lacks Blocks WithFfn Last Metrics[1]
Has Own Block TypeTrue[1]
Depends onOwn Block Type[1]
Exists As Ready to Trainnull[2]
Requires Simple Training LoopStandard Trainer[2]
Is Smallnull[2]
Architecture Workstrue[2]
Commits to Learning Structurenull[2]
Learns Real Sequential StructureDataset[2]
Loaded From Checkpointcheckpoints/resonant_wire/best[2]
Lacks DmodelD Model[3]
Lacks Residual StreamResidual Stream[3]
Lacks Snr ConceptSnr[3]
Has Mode PowerMode Power[3]
ExistsEntity[3]
Commits to Physics AnalogsPhysical Interpretation[3]
Computes Dft Mode Power DistributionDft Mode Power[3]
Computes Graph HarmonicsGraph Harmonics[3]
Computes RR[3]
Is Pure Rotation and GeometryCurrent Implementation[3]
Presupposes RotationRotations[3]
Has RR[3]
Is Not Measuring PhysicsPhysics[3]
Described As Pure Rotation GeometryCurrent State[3]
Differs From Main TrainerMain Trainer[3]
Presupposes GeometryGeometry[3]
Has Rotor QuaternionsRotor Quaternions[3]
Lacks AttentionAttention Mechanism[3]
Lacks Bandwidth ConceptBandwidth[3]
Known to Compute RWe Compute[3]
Uses Adam OptimizerAdam[3]
Scales From Params8500000[4]
Achieves Bpb4.06[4]
Achieves Tok Per Sec41000[4]
Scales to Params27000[4]
Optimizes Bandwidth UsageFull Bandwidth[4]
Is HealthyTraining Dynamics[4]
References Mambatrue[5]
Achieved Best Val Bpb at Step3000[5]
Has Model NameResonantWireLM[5]
Generated GibberishModel Output Gibberish[5]
Achieved Best Val Bpb3.675[5]
Has Memory Mb0.11[5]

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.

contrastsWithblah/watt-activation/part-387
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null
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hasParamsblah/watt-activation/part-386
33881
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null
architectureWorksblah/watt-activation/part-386
true
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null
learnsRealSequentialStructureblah/watt-activation/part-386
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checkpoints/resonant_wire/best
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describedAsPureRotationGeometryblah/watt-activation/part-394
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differsFromMainTrainerblah/watt-activation/part-394
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scalesFromParamsblah/watt-activation/part-396
8500000
achievesBPBblah/watt-activation/part-396
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41000
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27000
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isHealthyblah/watt-activation/part-396
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hasReadoutDimblah/watt-activation/part-400
93
referencesMambablah/watt-activation/part-400
true
achievedBestValBPBAtStepblah/watt-activation/part-400
3000
hasModelNameblah/watt-activation/part-400
ResonantWireLM
loadedInMsblah/watt-activation/part-400
54
generatedGibberishblah/watt-activation/part-400
ex:model-output-gibberish
achievedBestValBPBblah/watt-activation/part-400
3.675
hasGblah/watt-activation/part-400
8
hasMemoryMBblah/watt-activation/part-400
0.11
hasParamCountblah/watt-activation/part-400
27057
smallModelblah/watt-activation/part-400
27057
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2
hasLblah/watt-activation/part-400
12
underwentGenerationblah/watt-activation/part-400
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hasHeadsblah/watt-activation/part-400
4
hasConstellationTempblah/watt-activation/part-400
0.109
presupposesExistenceOfBlocksblah/watt-activation/part-401
null
modelSizeInMBblah/watt-activation/part-401
0.11
handlesLongerSequencesWithoutArchChangeblah/watt-activation/part-401
null
groupHeadProductEqualsblah/watt-activation/part-401
16
supportsTrainingSeqRangeblah/watt-activation/part-401
1K-4K
requiresNoArchitectureChangesForLongSeqblah/watt-activation/part-401
null
implicatesScalabilityTo16kblah/watt-activation/part-401
null
hasNumLayersblah/watt-activation/part-401
12
hasNumHeadsblah/watt-activation/part-401
2
hasNumGroupsblah/watt-activation/part-401
8
activationsMemoryAtSeq4kBatchSize64blah/watt-activation/part-401
768
existsAsLanguageModelblah/watt-activation/part-401
null
capableOfSeq16kWith48mbblah/watt-activation/part-401
null
presupposesTokensInSequenceblah/watt-activation/part-401
null
currentlyUsesSequenceSizeblah/watt-activation/part-401
256
lowModelSizeAdvantageblah/watt-activation/part-401
null
memoryAndComputeScaleWithblah/watt-activation/part-401
ex:sequence-length
benefitsFromDepthScalingblah/watt-activation/part-430
null
hasConfirmedDepthScalingblah/watt-activation/part-430
true
usesblah/watt-activation/part-424
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hasblah/watt-activation/part-424
ex:different-dynamics
usesblah/watt-activation/part-424
ex:givens-coupling
avoidsDynH4Flawsblah/watt-activation/part-424
true
hasNoblah/watt-activation/part-424
ex:lohe-sync-step
isCustomModelblah/watt-activation/part-432
true
achievesStrongValidationblah/watt-activation/part-432
ex:bpb-3-0568
hasReadoutDimblah/watt-activation/part-432
93
hasConstellationTempblah/watt-activation/part-432
0.101
hasHeadsblah/watt-activation/part-432
4
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2
hasLblah/watt-activation/part-432
24
achievedBestValAtStepblah/watt-activation/part-432
10000
loadedInMsblah/watt-activation/part-432
64
hasBestValBPBblah/watt-activation/part-432
3.0568
hasParamCountblah/watt-activation/part-432
30297
hasSizeMBblah/watt-activation/part-432
0.12
hasGblah/watt-activation/part-432
8
bestValStepblah/watt-activation/part-431
9500
hasConfigHeadsblah/watt-activation/part-431
4
hasReadoutDimblah/watt-activation/part-431
93
loadTimeMsblah/watt-activation/part-431
52
referencesPriorWorkblah/watt-activation/part-431
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hasConfigGblah/watt-activation/part-431
8
hasConfigLblah/watt-activation/part-431
12
hasMemoryUsageblah/watt-activation/part-431
0.11MB
bestValBpbblah/watt-activation/part-431
3.1401
hasParamsblah/watt-activation/part-431
27129
hasConfigHblah/watt-activation/part-431
2
hasConstellationTempblah/watt-activation/part-431
0.1
usesCacheTypeblah/watt-activation/part-486
RotorCache + ReadoutCache
differsFromOthersInSpeedblah/watt-activation/part-486
ex:o1-per-token-models
achievesSpeedblah/watt-activation/part-486
1,750-9,300 tok/s
usesGenerationMethodblah/watt-activation/part-486
Token-by-token
supportsMultipleEncodingsblah/watt-activation/part-392
null
implementsRotorblah/watt-activation/part-392
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addsEncodingOptionblah/watt-activation/part-392
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existsAsEntityblah/watt-activation/part-420
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hasKprobesblah/watt-activation/part-420
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hasHyperparamLblah/watt-activation/part-420
12
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2
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32
hasNumHeadsblah/watt-activation/part-420
4
hasParamsblah/watt-activation/part-420
49506
hasSidecarLogblah/watt-activation/part-420
logs/phase_metrics_resonant_20260319_184655.jsonl
referencesClaudeblah/watt-activation/part-429
ex:claude-md
typeblah/watt-activation/384
ex:ModelArchitecture
labelblah/watt-activation/384
ResonantWireLM
paramCountblah/watt-activation/384
33K
readinessStatusblah/watt-activation/384
ready to train
trainingRequirementblah/watt-activation/384
simple training loop
usesStandardTrainerblah/watt-activation/384
false
paramCountExactblah/watt-activation/384
33881
hasHyperparameterblah/watt-activation/384
G=8
hasHyperparameterblah/watt-activation/384
H=4
hasHyperparameterblah/watt-activation/384
L=6
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heads=4
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k_probes=4
readoutDimblah/watt-activation/384
125
loadedFromblah/watt-activation/384
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learnsSequentialStructureblah/watt-activation/384
true
paramCountRoundedblah/watt-activation/384
34K
bpbComparedToUnigramblah/watt-activation/384
well below unigram H0=4.59
hasHighestThroughputblah/watt-activation/384
true
architectureStatusblah/watt-activation/384
works
modelSizeStatusblah/watt-activation/384
small
scalingLeverblah/watt-activation/384
Scaling up layers or groups
nextLeverblah/watt-activation/384
Scaling up layers or groups
labelblah/watt-activation/392
ResonantWireLM
hasCharacteristicblah/watt-activation/392
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partOfSequenceblah/watt-activation/394
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gParameterblah/watt-activation/394
8
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2
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27057
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readout_dim=93
bestValidationBPBblah/watt-activation/394
4.0621
bestValidationStepblah/watt-activation/394
500
labelblah/watt-activation/401
ResonantWireLM
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8
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2
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12
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4
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27093
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hasConstellationTemperatureblah/watt-activation/401
0.124
hasReadoutDimblah/watt-activation/401
93
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3.446
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0.2562
generationModeblah/watt-activation/401
byte-level
generationTemperatureblah/watt-activation/401
0.8
maxTokensblah/watt-activation/401
50
generatedTextblah/watt-activation/401
The frefithed wel mzis. The minge of thapty anpak fe i
prefillLatencyblah/watt-activation/401
129
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6547
generationSpeedblah/watt-activation/401
7.6
typeblah/watt-activation/422
ex:ModelArchitecture
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ResonantWireLM
hasDynamicsTypeblah/watt-activation/422
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ex:givens-coupling
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couplingMechanismIsblah/watt-activation/422
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labelblah/watt-activation/423
ResonantWireLM
doesNotUseblah/watt-activation/423
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ex:Model

References (21)

21 references
  1. [1]Part 3878 facts
    ctx:discord/blah/watt-activation/part-387
  2. [2]Part 3869 facts
    ctx:discord/blah/watt-activation/part-386
  3. [3]Part 39421 facts
    ctx:discord/blah/watt-activation/part-394
  4. [4]Part 3966 facts
    ctx:discord/blah/watt-activation/part-396
  5. [5]Part 40016 facts
    ctx:discord/blah/watt-activation/part-400
  6. [6]Part 40117 facts
    ctx:discord/blah/watt-activation/part-401
  7. [7]Part 4302 facts
    ctx:discord/blah/watt-activation/part-430
  8. [8]Part 4245 facts
    ctx:discord/blah/watt-activation/part-424
  9. [9]Part 43213 facts
    ctx:discord/blah/watt-activation/part-432
  10. [10]Part 43112 facts
    ctx:discord/blah/watt-activation/part-431
  11. [11]Part 4864 facts
    ctx:discord/blah/watt-activation/part-486
  12. [12]Part 3923 facts
    ctx:discord/blah/watt-activation/part-392
  13. [13]Part 4209 facts
    ctx:discord/blah/watt-activation/part-420
  14. [14]Part 4291 fact
    ctx:discord/blah/watt-activation/part-429
  15. [15]38422 facts
    ctx:discord/blah/watt-activation/384
    • full textwatt-activation-384
      text/plain3 KBdoc:agent/watt-activation-384/9e57a6f6-7f88-4fd5-81f0-454435de5e3e
      Show excerpt
      [2026-03-19 01:53] xenonfun: ⏺ Pure FFN comparison — clean, no contention, sequential: ``` ┌─────────────────────┬──────────┬───────┬──────────────────────┐ │ FFN Type │ BPB @500 │ tok/s │ Params (total model) │ ├──────────
  16. [16]3929 facts
    ctx:discord/blah/watt-activation/392
    • full textwatt-activation-392
      text/plain1 KBdoc:agent/watt-activation-392/ddbc1ecc-c3dc-4acc-b74a-05cca6e39186
      Show excerpt
      [2026-03-19 03:47] xenonfun: ``` ❯ do we not have concept of SNR and bandwidth in these or they pure rotation and geometry? ⏺ They're pure rotation and geometry right now — no SNR or bandwidth concepts. The ResonantWireLM has no attention,
  17. [17]39412 facts
    ctx:discord/blah/watt-activation/394
    • full textwatt-activation-394
      text/plain2 KBdoc:agent/watt-activation-394/027ab2ca-0cf4-4693-ba6c-1f9208e93d86
      Show excerpt
      [2026-03-19 03:59] xenonfun: ⏺ Look at those diagnostics: - r = 0.21 — beautifully low coherence, no over-sync. The BPSK encoding with 8 groups keeps things differentiated. - SNR = -13.4dB — deep in the noise regime, lots of room to bu
  18. [18]40120 facts
    ctx:discord/blah/watt-activation/401
    • full textwatt-activation-401
      text/plain3 KBdoc:agent/watt-activation-401/005682ee-5515-41ad-a48c-8b7300516ac0
      Show excerpt
      [2026-03-19 05:26] xenonfun: ⏺ Gated rotor at 2K steps: BPB 3.452. That's the best result from any ResonantWireLM variant. Updated comparison: ``` ┌───────────────────────┬────────┬───────┬───────┬──────┐ │ Model │ Param
  19. [19]4227 facts
    ctx:discord/blah/watt-activation/422
    • full textwatt-activation-422
      text/plain3 KBdoc:agent/watt-activation-422/a495a8d5-2cf4-41be-a842-8a6160f7c013
      Show excerpt
      [2026-03-20 01:24] xenonfun: Flaw 4: Optimizer micromanaging — INTENTIONALLY ADDRESSED (different fix) This one is nuanced. CLAUDE.md says: "Coupling gradient updates REMOVED: K and log_adjacency are structural constants, not learned." T
  20. [20]4233 facts
    ctx:discord/blah/watt-activation/423
    • full textwatt-activation-423
      text/plain2 KBdoc:agent/watt-activation-423/8045fe79-be14-40ef-8627-1f24b38b2ba2
      Show excerpt
      [2026-03-20 01:28] xenonfun: ``` ⏺ OK, so for H≠4 it dispatches to _spectral_lohe_sync in attention.py (line 162), and for H=4 it uses quaternion_lohe_sync. Our entity run uses H=2, so it wouldn't even hit the Lohe FFN path — ResonantWir
  21. [21]42913 facts
    ctx:discord/blah/watt-activation/429
    • full textwatt-activation-429
      text/plain3 KBdoc:agent/watt-activation-429/b18dc08f-4e77-4134-ab4f-183a2f544d75
      Show excerpt
      [2026-03-20 03:23] lisamegawatts: Done — 7 pages, clean compile. The paper is fundamentally rewritten: Old narrative: "Block-diagonal fusion beats FedAvg by +3.5pp" New narrative: "Block-diagonal alone is a capacity confound. The full evol

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