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

docs(metrics): add quantitative percentage improvement breakdown (%) vs baselines and dashboard KPI cards

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

Enhance README.md with comprehensive LaTeX equations and theoretical CMDP formulation

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

Update README.md

update

Upload README.md

Upload benchmark_v4.py

Upload tests/test_v4.py

Upload requirements.txt

Upload _version.py

Upload README.md

Upload src/__init__.py

Upload src/energy_v4.py

Upload src/qkan.py

Add plain-English explanation and 8 detailed real-world application examples

Complete model card rewrite: brief overview + comprehensive technical documentation

Update MODEL_CARD: Add K2 Think v2 integration section, explainable AI features

Update ML Intern artifact metadata

Upload README.md with huggingface_hub

Upload benchmark_fast.py with huggingface_hub

Upload k2_explain.py with huggingface_hub

Upload benchmark_results.json with huggingface_hub

v3.0.0: Scripts

v3.0.0: Configs

v3.0.0: Tests

v3.0.0: Source files

Update ML Intern artifact metadata

Upload requirements.txt

Upload setup.py

Upload MODEL_CARD.md

Upload CITATION.cff

Upload LICENSE

Upload README.md

Upload README.md

Upload q_tensor_former_v2.py

Upload q_tensor_former.py

Upload README.md

Upload _version.py with huggingface_hub

Update ML Intern artifact metadata

Upload README.md with huggingface_hub

Upload qtensorformer_small.pt with huggingface_hub

Update ML Intern artifact metadata

initial commit