specialists: switch training to PyTorch, add capacity sweep infrastructure
- train_specialist_pt.py: pure PyTorch trainer replacing Fabricate binary. No SQLite/WAL — saves checkpoint.pt + history.json only. Fabricate was causing D-state IO blocking on the HDD due to continuous WAL writes. - eval_specialist_pt.py: PyTorch eval with confusion matrix, threshold analysis, guardrail assessment, and deployment verdict. - capacity_sweep.py: updated to call PyTorch scripts; TIERS now include heads field (4/6/8) since PyTorch supports multi-head unlike Fabricate. - prereq_op_binary_split.py: evaluate_combined rewritten in PyTorch. - Performance fix in train_specialist_pt.py: load training data onto GPU once and sample via torch.randint instead of DataLoader (eliminates Python/CPU overhead on tiny datasets). Fix applied, not yet timed. Next session: time the fix, then run sweep_all_gates.sh --pilot-only. See HANDOFF-2026-03-31.md for full context. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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2 0 `whetstone_enqueue_pair_upgrade` MCP tool
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3 0 Deprecation/succession lifecycle framework
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3 0 Security scan orchestrator for generated targets
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1 0 Phase 12c Integration Tests
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2 0 Reviewer quorum policy model for critical approvals
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1 0 Phase 38a Integration
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1 0 Phase 23b Integration + Sprint Summary
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0 0 Reprojection safety policy
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2 0 Stable corpus certification suite
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0 0 Timing regression runner integration
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0 0 `whetstone_get_tool_permissions` MCP tool
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3 0 Cross-language rollout state snapshot model
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1 0 Compatibility policy profiles by interface criticality
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3 0 `whetstone_publish_roadmap_epoch` MCP tool
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2 0 Security Report Generation
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2 0 Compatibility/deprecation report artifact
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1 0 Human Review Interface via MCP
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2 0 HiveMind job publisher from editor
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1 0 Parser/AST adapter interface schema
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0 0 Sprint 76 integration summary + regression
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1 0 Scenario — Multi-Model Orchestration
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0 0 Autonomous loop controller with “green or explicit blocked” contract
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2 0 Security-Aware Routing
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0 0 Verifier trust weighting policy engine
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