Matched curation runs
Same model, different selected subsets, multiple budgets and seeds.
Compute partnership
Compute for falsifiable work, not vague model training.
PlainHand’s experimental matrix uses repeated matched runs, controlled corruptions, portability tests, and reproducibility artifacts.
WORKLOAD MATRIX DESIGNEDExperimental workloads
What the compute would test.
Modality-fusion matrix
Coherence-guided, learned attention, fixed fusion, shuffled gate, and single modalities.
Corruption robustness
Blur, occlusion, dropout, delay, drift, clipping, and missing modalities.
Layered evaluation
Whole-action only, part or sensor only, and independent combined scoring.
Hierarchy experiments
Conventional model, explicit fixed hierarchy, and adaptive hierarchy.
Agent structure search
Bounded topology, grouping, and weighting proposals under matched rules.
Two-direction learning
World-model-first baseline versus interaction-first policy and recovered prediction.
Portability
A second compatible architecture; OpenVLA only after its compatibility gate.
Run-plan module
Quantities follow the real experiment plan.
GPU-hours
To be specifiedStorage + transfer
To be specifiedSeeds + budgets
To be specifiedCompute partner contact
Match resources to the experimental program.
Share the program, infrastructure, or resource type you can offer. PlainHand will follow up with the compatible workload and evidence plan.
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