User pearl (2026-04-24): the DQN already has every primitive TLOB needs
(gpu_attention, batched_forward/backward, GpuLinear, cuBLASLt handles,
cuda_autograd). Port TLOB's Q/K/V + attention as a composition of
existing primitives. Random-init, trainable end-to-end from the DQN's
reward signal. Uniform with Mamba2, IQL, atoms/γ/τ/ε — all of which
already follow this pattern.
Eliminates:
- ONNX Runtime dependency (was already dead — stripped from ml-supervised)
- Separate supervised pretraining pipeline
- "Freeze vs fine-tune" false dichotomy
- Pretrained-checkpoint-file-not-found failure mode
Prerequisites for the cuBLAS-native design (all satisfied):
- gpu_attention.rs exists
- GpuLinear trainable layer exists
- cuda_autograd over cuBLAS exists
- MBP-10 data already in the DQN data pipeline
What was "BLOCKED on prerequisites" in the Task 6C audit referred to
the OLD pretrained-ONNX design. The cuBLAS-native design has all
prerequisites satisfied — Task 6C can proceed under the new scope.
Pearl captured in memory: pearl_tlob_no_pretraining.md. Generalises to
any future attention/state-space/Neural ODE module: port to cuBLAS,
random init, let the DQN teach it.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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