| Model Soups (averaging weights) | Established research | Wortsman et al. 2022; replicated in multiple follow-ups |
| Task Arithmetic (task vectors) | Established research | Ilharco et al. 2022; widely adopted in merging literature |
| TIES-Merging | Established research | Yadav et al. 2023; validated on multi-task benchmarks |
| DARE | Established research | Yu et al. 2023; independent replication confirmed |
| SLERP for model weights | Research hypothesis | Shoemake 1985 is established for rotations; application to high-dimensional weight vectors is hypothesized |
| Evolutionary Model Merge | Established research | Akiba et al. 2024; demonstrated on diverse model families |
| LoRA for task vector production | Established research | Hu et al. 2021; standard practice in PEFT |
| QLoRA for resource-constrained fine-tuning | Established research | Dettmers et al. 2023; replicated at scale |
| NSGA-II for merge optimization | Engineering assumption | Deb et al. 2002 is established; applicability to model merging search space is assumed |
| NSGA-III for many-objective merging | Research hypothesis | Deb & Jain 2014 is established; benefit for 5+ merge objectives is predicted |
| CMA-ES for merge hyperparameters | Research hypothesis | Hansen 2001 is established; continuous merge ratio optimization is untested in EMEP |
| Catastrophic forgetting in merged LLMs | Research hypothesis | McCloskey & Cohen 1989 is established for small networks; LLM-scale merge forgetting is under study |
| EWC for merge-time protection | Research hypothesis | Kirkpatrick et al. 2017 is established for sequential learning; merge application is speculative |
| Benchmark contamination detection | Established research | Sainz et al. 2023; methods are validated but not exhaustive |
| Hidden Test Set isolation prevents overfitting | Engineering assumption | Logical consequence of ML best practice; not formally proven for evolutionary merging |
| ed25519 signing for model provenance | Engineering assumption | Cryptographic primitive is established; supply chain application in ML is emerging |
| Safetensors security advantage over pickle | Established research | Format specification and security analysis are published |
| Offline-first enterprise mode | Future goal | Design target for Phase 7; not yet implemented or validated |
| Pareto front diversity guarantees | Research hypothesis | NSGA-II/III provide diversity mechanisms; guarantee for model merge space is unproven |
| Semantic versioning for models | Engineering assumption | Standard practice in software; formalized for ML artifacts in EMEP |