fix(config): Align configuration API with canonical ModelConfig fields and document checkpoint provenance

docs: Hardening claims, provenance tags, and research-grade documentation alignment

feat: Harden research system with hierarchical KV, phase profiling, matched budgets, and counterfactual validation

feat(benchmarks): Add comprehensive baseline comparison system with 14 architectures, 17 metrics, Pareto frontiers, and provenance tags

feat(research): Turn Q-TensorFormer into a verified research system with LFS figure assets

feat(benchmarks): add comprehensive 11-model baseline comparison study, interactive dashboard table, and publication-grade docs

Implement top-tier research features: PID Dual Controller, GQA, Roofline Analyzer, Early-Exit, Meyer-Wallach Entanglement, and Interactive Visual Dashboard

Transform Q-TensorFormer into an Information-Value Adaptive Resource Allocation Architecture

Upload src/__init__.py

Upload src/energy_v4.py

Upload src/qkan.py

v3.0.0: Source files