§4.6 — Knowledge localization and editing

B1–B2. ROME and MEMIT

• Key papers: ROME: Meng et al., NeurIPS 2022, https://arxiv.org/abs/2202.05262 (code https://github.com/kmeng01/rome — CUDA-hardcoded in places, no Apple Silicon support); MEMIT: Meng et al., ICLR 2023, https://arxiv.org/abs/2210.07229 (https://github.com/kmeng01/memit)
• Claims: causal tracing localizes fact recall to mid-layer MLPs at subject tokens; rank-one (ROME) / spread least-squares (MEMIT, 10k edits) weight updates rewrite facts.
• Cost: tracing minutes/fact on Mac at 7B; edits cheap, but covariance statistics need ~100k Wikipedia forward passes (precomputed stats exist only for GPT-2/GPT-J; 2025 reduction: https://arxiv.org/pdf/2506.04226).
• Epistemic status: Landmark; localization claim debunked-in-part (B8); editing mechanics established.

B3–B5. PMET, EMMET, AlphaEdit

• PMET: Li et al., AAAI 2024, https://arxiv.org/abs/2308.08742 — writes only FFN weights; its finding that MHSA weights need no update is itself interpretability evidence (aligned with B7).
• EMMET: Gupta et al., EMNLP Findings 2024, https://arxiv.org/abs/2403.14236 — unifies ROME/MEMIT as one preservation–memorization objective; useful theory for an MLX reimplementation.
• AlphaEdit: Fang et al., ICLR 2025 Outstanding Paper, https://arxiv.org/abs/2410.02355 (code https://github.com/jianghoucheng/alphaedit) — null-space projection onto preserved-knowledge keys; +36.7% over locate-then-edit baselines; mitigates sequential collapse; one-line projection + one SVD. 2026 reproducibility study broadly confirms but tempers (https://arxiv.org/pdf/2606.26783). Successor: UltraEdit (2025), https://arxiv.org/abs/2505.14679
• Epistemic status: Established SOTA line, still fresh.

B6. Knowledge neurons

• Key paper: Dai et al., ACL 2022, https://arxiv.org/abs/2104.08696
• Critiques: neurons express token/routing patterns rather than stored knowledge; language-independent and degenerate (redundant) knowledge neurons undermine one-fact-one-neuron (https://arxiv.org/html/2308.13198); attribution pitfalls (EMNLP 2024, https://arxiv.org/abs/2312.12141).
• Epistemic status: Debunked-in-part; historically important. Mac-feasible to reproduce.

B7. Storage vs retrieval — the current consensus scaffold

• Key papers: Geva et al., EMNLP 2023, https://arxiv.org/abs/2304.14767 (three-stage recall: early-MLP subject enrichment → relation propagation → attribute extraction by attention heads; causal via attention knockouts); attribute lens / linear relation decoding: Hernandez et al., ICLR 2024, https://arxiv.org/abs/2308.09124 (~48% of relations well-fit by a single affine map — linearity NOT universal); additive mechanisms: Chughtai, Cooney, Nanda 2024, https://arxiv.org/abs/2402.07321 (recall = sum of qualitatively different mechanisms — complicates clean localization).
• Consensus: MLPs store, attention retrieves — established; details (additivity, linearity coverage) contested.
• Cost: all Mac-feasible ≤7B.

B8. The localization-vs-editing critique — central calibration datum for Q1

• Key paper: Hase et al., NeurIPS 2023, https://arxiv.org/abs/2301.04213
• Substance: causal-tracing localization does not predict which layer edits best; edit success near-uniform across layers; tracing effects explain a tiny fraction of edit-success variance. "Edit success" cannot be used as evidence of localization — design Experiment C accordingly.
• Related: Rebuilding ROME (collapse partly implementation asymmetry, https://arxiv.org/abs/2403.07175); Should We Really Edit Language Models? (evaluation misleading, https://arxiv.org/pdf/2410.18785)
• Epistemic status: Established — a causal audit of a causal method. Reproduction Mac-feasible on GPT-2-XL.

B9. Ripple effects

• Key paper: Cohen et al., TACL 2024, https://arxiv.org/abs/2307.12976 — editors fail to propagate logical consequences (multi-hop, sibling, aggregation); in-context editing beats parametric editors. 2025–2026 follow-ups: ChainEdit, RippleCOT, https://arxiv.org/pdf/2606.01610
• Epistemic status: Established.

B10. Sequential-editing collapse and general-ability damage

• Key papers: Gupta et al. 2024, https://arxiv.org/abs/2401.07453 (gradual forgetting → abrupt collapse); Gu et al., EMNLP 2024, https://arxiv.org/abs/2401.04700 (few edits degrade reasoning/NLI/QA); 2025 energy-regularized line: https://arxiv.org/html/2510.01172v1
• Consequence: central caveat for any "editing as causal verification" protocol in modelmap.

B11. Multilingual knowledge sharing and cross-lingual editing

• Key papers: cross-lingual editing: https://arxiv.org/abs/2309.08952 ; language-independent neurons: https://arxiv.org/html/2308.13198 ; MPN: https://arxiv.org/abs/2401.03190 ; language-agnostic factual neurons: https://arxiv.org/abs/2406.16416 ; multilingual recall: https://arxiv.org/abs/2410.14387 ; multilingual pipeline replication (2025): https://arxiv.org/html/2505.20546v2 ; survey (EMNLP 2025): https://arxiv.org/html/2505.14393
• Substance: monolingual edits transfer poorly; a shared language-agnostic substrate exists (English-centric hub behavior common); Geva pipeline partially replicates multilingually with language-specific extraction stages. Directly matches modelmap's FR/EN promptset plans (Experiment B).
• Epistemic status: Sharing established qualitatively; degree and mechanism contested.

B12. Tooling and Apple Silicon reality check

• EasyEdit https://github.com/zjunlp/EasyEdit (ACL 2024) unifies ROME/MEMIT/PMET/AlphaEdit + KnowEdit benchmark; surveys: https://arxiv.org/abs/2310.16218 , https://arxiv.org/pdf/2401.01286
• All editing repos are CUDA-first; none advertises MPS support. The math is small-matrix linear algebra — an MLX/MPS reimplementation is tractable and itself a publishable modelmap artifact ("knowledge editing on consumer Macs"). The only expensive step is covariance precomputation (hours of forward passes at 7B on 64 GB).

Implications for modelmap

1. Never infer localization from edit success (Hase). Experiment C's correlational→causal survival rate must use patching/ablation, with editing as a separate axis.
2. The storage-vs-retrieval scaffold (B7) gives concrete map types: subject-enrichment maps (MLP), extraction maps (attention heads).
3. Multilingual localization atlas (FR/EN) has solid prior art to build on and clear open questions.
4. MLX knowledge-editing port = concrete tooling contribution.