spb/modelmap Public License

1---2project: modelmap3document: Bibliography4author: Simon-Pierre Boucher5contact: contact@spboucher.ai6website: https://modelmap.io7created: 2026-08-128modified: 2026-08-129status: draft10---1112# Bibliography1314Every consulted source, with URL and access date. Populated during Phase 1 (charter §4).15Entries: `- Authors — Title (year). URL — accessed YYYY-MM-DD. [theme tags]`16Duplicates across themes are listed once, under their primary theme. Entries flagged17(spot-check) carry stable canonical IDs cited from memory by the survey pass and should18be re-verified before citation in a published document.1920## §4.1 — Observational probing2122- Alain & Bengio — Understanding intermediate layers using linear classifier probes (2016). https://arxiv.org/abs/1610.01644 — accessed 2026-08-12. [probing]23- Belinkov — Probing Classifiers: Promises, Shortcomings, and Advances (2021/2022). https://arxiv.org/abs/2102.12452 ; https://aclanthology.org/2022.cl-1.7/ — accessed 2026-08-12. [probing, survey] (spot-check)24- Hewitt & Liang — Designing and Interpreting Probes with Control Tasks (2019). https://arxiv.org/abs/1909.03368 — accessed 2026-08-12. [probing, controls] (spot-check)25- Voita & Titov — Information-Theoretic Probing with Minimum Description Length (2020). https://arxiv.org/pdf/2003.12298 — accessed 2026-08-12. [probing, controls]26- nostalgebraist — interpreting GPT: the logit lens (2020). https://www.lesswrong.com/posts/AcKRB8wDpdaN6v6ru/interpreting-gpt-the-logit-lens — accessed 2026-08-12. [logit-lens]27- Belrose et al. — Eliciting Latent Predictions from Transformers with the Tuned Lens (2023). https://arxiv.org/abs/2303.08112 ; https://github.com/AlignmentResearch/tuned-lens — accessed 2026-08-12. [tuned-lens]28- Chuang et al. — DoLa: Decoding by Contrasting Layers (2023). https://arxiv.org/abs/2309.03883 — accessed 2026-08-12. [early-decoding]29- Pal et al. — Future Lens: Anticipating Subsequent Tokens from a Single Hidden State (2023). https://www.alphaxiv.org/overview/2311.04897v1 ; https://future.baulab.info/ — accessed 2026-08-12. [lens-successors]30- Ghandeharioun et al. — Patchscopes (2024). https://arxiv.org/abs/2401.06102 ; https://pair-code.github.io/interpretability/patchscopes/ — accessed 2026-08-12. [lens-successors]31- SimLens for Early Exit (2025). https://arxiv.org/pdf/2507.17618 — accessed 2026-08-12. [lens-successors, 2025]32- Zou et al. — Representation Engineering: A Top-Down Approach to AI Transparency (2023). https://arxiv.org/abs/2310.01405 ; https://github.com/andyzoujm/representation-engineering — accessed 2026-08-12. [RepE]33- Representation Engineering: Survey and Research Challenges (2025). https://arxiv.org/pdf/2502.17601 — accessed 2026-08-12. [RepE, critique]34- Vogel — repeng (control-vector library). https://github.com/vgel/repeng ; llama.cpp PR https://github.com/ggml-org/llama.cpp/pull/5970 — accessed 2026-08-12. [tooling, steering, GGUF]35- Ravfogel et al. — Null It Out: INLP (ACL 2020). https://aclanthology.org/2020.acl-main.647.pdf — accessed 2026-08-12. [concept-erasure]36- Elazar et al. — Amnesic Probing (TACL 2021). https://aclanthology.org/2021.tacl-1.10/ — accessed 2026-08-12. [concept-erasure]37- Belrose et al. — LEACE: Perfect linear concept erasure in closed form (2023). https://arxiv.org/abs/2306.03819 ; https://github.com/EleutherAI/concept-erasure ; https://blog.eleuther.ai/oracle-leace/ — accessed 2026-08-12. [concept-erasure]38- Nonlinear Concept Erasure: a Density Matching Approach (2025). https://arxiv.org/abs/2507.12341 — accessed 2026-08-12. [concept-erasure, 2025]39- Improving Causal Interventions in Amnesic Probing with Mean Projection or LEACE (2025). https://arxiv.org/html/2506.11673 — accessed 2026-08-12. [concept-erasure, critique]4041## §4.2 — Causal / interventional methods4243- Vig et al. — Investigating Gender Bias in LMs Using Causal Mediation Analysis (NeurIPS 2020). https://papers.nips.cc/paper/2020/hash/92650b2e92217715fe312e6fa7b90d82-Abstract.html — accessed 2026-08-12. [patching-origins]44- Zhang & Nanda — Towards Best Practices of Activation Patching (ICLR 2024). https://arxiv.org/abs/2309.16042 — accessed 2026-08-12. [patching, methodology]45- Heimersheim & Nanda — How to use and interpret activation patching (2024). https://arxiv.org/pdf/2404.15255 — accessed 2026-08-12. [patching, methodology]46- Nanda — How to Think About Activation Patching. https://www.alignmentforum.org/posts/xh85KbTFhbCz7taD4/how-to-think-about-activation-patching — accessed 2026-08-12. [patching]47- Goldowsky-Dill et al. — Localizing Model Behavior with Path Patching (2023). https://arxiv.org/abs/2304.05969 — accessed 2026-08-12. [path-patching]48- Nanda — Attribution Patching: Activation Patching At Industrial Scale (2023). https://www.neelnanda.io/mechanistic-interpretability/attribution-patching — accessed 2026-08-12. [attribution-patching]49- Syed, Rager, Conmy — Attribution Patching Outperforms Automated Circuit Discovery (BlackboxNLP 2024). https://aclanthology.org/2024.blackboxnlp-1.25.pdf ; https://arxiv.org/abs/2310.10348 — accessed 2026-08-12. [EAP]50- Kramár, Lieberum, Shah, Nanda — AtP*: efficient localization of LLM behaviour (2024). https://arxiv.org/abs/2403.00745 ; https://github.com/koayon/atp_star — accessed 2026-08-12. [attribution-patching]51- When Attribution Patching Lies (2026). https://arxiv.org/pdf/2606.09899 — accessed 2026-08-12. [attribution-patching, critique, 2026]52- RelP: Relevance Patching (2025). https://arxiv.org/html/2508.21258v1 — accessed 2026-08-12. [attribution-patching, 2025]53- Position-aware Automatic Circuit Discovery (2025). https://arxiv.org/pdf/2502.04577 — accessed 2026-08-12. [circuits, 2025]54- APP: Accelerated Path Patching (2025). https://arxiv.org/html/2511.05442 — accessed 2026-08-12. [path-patching, 2025]55- Chan et al. (Redwood) — Causal Scrubbing (2022). https://www.alignmentforum.org/posts/JvZhhzycHu2Yd57RN/causal-scrubbing-a-method-for-rigorously-testing ; appendix https://www.alignmentforum.org/posts/kcZZAsEjwrbczxN2i/causal-scrubbing-appendix — accessed 2026-08-12. [causal-scrubbing]56- Practical Pitfalls of Causal Scrubbing (2023). https://www.alignmentforum.org/posts/DFarDnQjMnjsKvW8s/practical-pitfalls-of-causal-scrubbing — accessed 2026-08-12. [causal-scrubbing, critique]57- Geiger et al. — Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations (DAS, CLeaR 2024). https://arxiv.org/abs/2303.02536 — accessed 2026-08-12. [DAS, IIA]58- Wu et al. — Interpretability at Scale: Boundless DAS in Alpaca (2023). https://arxiv.org/abs/2305.08809 — accessed 2026-08-12. [DAS]59- Wu et al. — pyvene (NAACL 2024). https://arxiv.org/abs/2403.07809 ; https://github.com/stanfordnlp/pyvene — accessed 2026-08-12. [tooling, DAS]60- Turner et al. — Steering Language Models With Activation Engineering (ActAdd, 2023). https://arxiv.org/abs/2308.10248 — accessed 2026-08-12. [steering]61- Rimsky et al. — Steering Llama 2 via Contrastive Activation Addition (ACL 2024). https://aclanthology.org/2024.acl-long.828/ ; https://arxiv.org/abs/2312.06681 — accessed 2026-08-12. [steering]62- Li et al. — Inference-Time Intervention (NeurIPS 2023). https://arxiv.org/pdf/2306.03341 — accessed 2026-08-12. [steering]63- Tan et al. — Analysing the Generalisation and Reliability of Steering Vectors (NeurIPS 2024). https://arxiv.org/abs/2407.12404 — accessed 2026-08-12. [steering, critique]64- Steering off Course: Reliability Challenges in Steering LMs (ACL 2025). https://aclanthology.org/2025.acl-long.974/ ; https://arxiv.org/html/2504.04635v1 — accessed 2026-08-12. [steering, critique, 2025]65- Understanding Unreliability of Steering Vectors (2026). https://arxiv.org/pdf/2602.17881 — accessed 2026-08-12. [steering, 2026]66- On the Non-Identifiability of Steering Vectors (2026). https://arxiv.org/pdf/2602.06801 — accessed 2026-08-12. [steering, 2026]67- The Curse of Multiple Mediators: Hidden Interaction Effects in Activation Patching (2026). https://arxiv.org/html/2606.27510v1 — accessed 2026-08-12. [patching, critique, 2026]68- Bereska & Gavves — Mechanistic Interpretability for AI Safety: A Review (2024). https://arxiv.org/pdf/2404.14082 — accessed 2026-08-12. [survey]69- Fiotto-Kaufman et al. — NNsight and NDIF (ICLR 2025). https://arxiv.org/abs/2407.14561 ; https://github.com/ndif-team/nnsight ; https://ndif.us/ — accessed 2026-08-12. [tooling, remote-execution]70- Bau — baukit. https://github.com/davidbau/baukit — accessed 2026-08-12. [tooling]71- TransformerLens — Getting Started (MPS opt-in note). https://transformerlensorg.github.io/TransformerLens/content/getting_started.html ; https://github.com/TransformerLensOrg/TransformerLens — accessed 2026-08-12. [tooling, Apple-Silicon]72- IOI circuit in Mamba (2024). https://arxiv.org/html/2407.14008v1 — accessed 2026-08-12. [circuits, replication]7374## §4.3 — Feature decomposition7576- Elhage et al. — Toy Models of Superposition (2022). https://transformer-circuits.pub/2022/toy_model/index.html — accessed 2026-08-12. [superposition]77- Yun, Chen, Olshausen, LeCun — Transformer visualization via dictionary learning (2021). https://arxiv.org/abs/2103.15949 — accessed 2026-08-12. [dictionary-learning]78- Sharkey, Braun, Millidge — Interim report: taking features out of superposition with SAEs (2022). https://www.lesswrong.com/posts/z6QQJbtpkEAX3Aojj/interim-research-report-taking-features-out-of-superposition — accessed 2026-08-12. [SAE-precursor]79- Cunningham et al. — Sparse Autoencoders Find Highly Interpretable Features in LMs (2023). https://arxiv.org/abs/2309.08600 — accessed 2026-08-12. [SAE]80- Bricken et al. — Towards Monosemanticity (2023). https://transformer-circuits.pub/2023/monosemantic-features/index.html — accessed 2026-08-12. [SAE, feature-splitting]81- Templeton et al. — Scaling Monosemanticity (2024). https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html — accessed 2026-08-12. [SAE-scaling, pipeline, feature-browser]82- Rajamanoharan et al. — Gated Sparse Autoencoders (2024). https://arxiv.org/abs/2404.16014 — accessed 2026-08-12. [SAE-variant]83- Gao et al. — Scaling and evaluating sparse autoencoders (TopK, 2024). https://arxiv.org/abs/2406.04093 — accessed 2026-08-12. [SAE-variant]84- Rajamanoharan et al. — JumpReLU Sparse Autoencoders (2024). https://arxiv.org/abs/2407.14435 — accessed 2026-08-12. [SAE-variant]85- Bussmann, Leask, Nanda — BatchTopK Sparse Autoencoders (2024). https://arxiv.org/abs/2412.06410 — accessed 2026-08-12. [SAE-variant]86- Bussmann et al. — Matryoshka Sparse Autoencoders (2025). https://arxiv.org/abs/2503.17547 — accessed 2026-08-12. [SAE-variant, absorption]87- Lindsey et al. — Sparse Crosscoders for Cross-Layer Features and Model Diffing (2024). https://transformer-circuits.pub/2024/crosscoders/index.html — accessed 2026-08-12. [crosscoders]88- Dunefsky, Chlenski, Nanda — Transcoders Find Interpretable LLM Feature Circuits (2024). https://arxiv.org/abs/2406.11944 — accessed 2026-08-12. [transcoders]89- Paulo, Shabalin, Belrose — Transcoders Beat Sparse Autoencoders for Interpretability (2025). https://arxiv.org/abs/2501.18823 — accessed 2026-08-12. [skip-transcoders]90- Chanin et al. — A is for Absorption (2024). https://arxiv.org/abs/2409.14507 — accessed 2026-08-12. [feature-absorption]91- Engels et al. — Not All Language Model Features Are Linear (2024). https://arxiv.org/abs/2405.14860 — accessed 2026-08-12. [feature-geometry]92- Bills et al. — Language models can explain neurons in language models (2023). https://openaipublic.blob.core.windows.net/neuron-explainer/paper/index.html — accessed 2026-08-12. [auto-interp]93- Paulo et al. — Automatically Interpreting Millions of Features (2024). https://arxiv.org/abs/2410.13928 — accessed 2026-08-12. [auto-interp]94- Huang et al. — Rigorously Assessing Natural Language Explanations of Neurons (2023). https://arxiv.org/abs/2309.10312 — accessed 2026-08-12. [auto-interp-critique]95- Karvonen et al. — SAEBench (2025). https://arxiv.org/abs/2503.09532 ; https://www.neuronpedia.org/sae-bench/info ; https://github.com/adamkarvonen/SAEBench — accessed 2026-08-12. [SAE-evaluation]96- Chanin et al. — Are Sparse Autoencoder Benchmarks Reliable? (2026). https://arxiv.org/abs/2605.18229 — accessed 2026-08-12. [evaluation-critique, 2026]97- Smith et al. (GDM) — Negative Results for SAEs On Downstream Tasks and Deprioritising SAE Research (2025). https://deepmindsafetyresearch.medium.com/negative-results-for-sparse-autoencoders-on-downstream-tasks-and-deprioritising-sae-research-6cadcfc125b9 — accessed 2026-08-12. [SAE-debate]98- Kantamneni et al. — Are Sparse Autoencoders Useful? A Case Study in Sparse Probing (2025). https://arxiv.org/abs/2502.16681 — accessed 2026-08-12. [SAE-debate]99- Wu et al. — AxBench (2025). https://arxiv.org/abs/2501.17148 — accessed 2026-08-12. [steering, SAE-debate]100- Kissane et al. — SAEs are highly dataset dependent (2024). https://www.alignmentforum.org/posts/rtp6n7Z23uJpEH7od/saes-are-highly-dataset-dependent-a-case-study-on-the — accessed 2026-08-12. [dataset-dependence]101- Paulo, Belrose — SAEs Trained on the Same Data Learn Different Features (2025). https://arxiv.org/abs/2501.16615 — accessed 2026-08-12. [seed-dependence, replication]102- Movva et al. — Use SAEs to Discover Unknown Concepts, Not to Act on Known Concepts (2025). https://arxiv.org/abs/2506.23845 — accessed 2026-08-12. [SAE-debate]103- Lieberum et al. — Gemma Scope (2024). https://arxiv.org/abs/2408.05147 ; https://huggingface.co/google/gemma-scope — accessed 2026-08-12. [open-SAEs, artifacts]104- He et al. — Llama Scope (2024). https://arxiv.org/abs/2410.20526 — accessed 2026-08-12 (spot-check). [open-SAEs]105- Huang et al. — RAVEL (ACL 2024). https://arxiv.org/abs/2402.17700 ; https://github.com/explanare/ravel — accessed 2026-08-12. [evaluation, causal]106- CE-Bench (2025). https://arxiv.org/abs/2509.00691 — accessed 2026-08-12. [SAE-evaluation, 2025]107- SynthSAEBench (2026). https://arxiv.org/abs/2602.14687 — accessed 2026-08-12. [SAE-evaluation, synthetic, 2026]108- SAELens (Bloom, Tigges, Chanin et al.). https://github.com/jbloomAus/SAELens ; https://github.com/decoderesearch/SAELens — accessed 2026-08-12. [tooling, MPS]109- dictionary_learning (Marks, Mueller). https://github.com/saprmarks/dictionary_learning — accessed 2026-08-12. [tooling]110- EleutherAI — sparsify. https://github.com/EleutherAI/sparsify — accessed 2026-08-12. [tooling, streaming]111- EleutherAI — delphi (auto-interp). https://github.com/EleutherAI/delphi — accessed 2026-08-12. [tooling]112- EleutherAI — clt-training. https://github.com/EleutherAI/clt-training — accessed 2026-08-12. [tooling, CLT]113- ckkissane — crosscoder-model-diff-replication. https://github.com/ckkissane/crosscoder-model-diff-replication — accessed 2026-08-12. [tooling]114115## §4.4 — Circuits and computational graphs116117- Olsson et al. — In-context Learning and Induction Heads (2022). https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html ; https://arxiv.org/abs/2209.11895 — accessed 2026-08-12. [induction-heads, universality]118- Wang et al. — Interpretability in the Wild: IOI circuit (2022). https://arxiv.org/abs/2211.00593 — accessed 2026-08-12. [IOI, circuits, path-patching]119- McDougall et al. — Copy Suppression (2023). https://arxiv.org/abs/2310.04625 — accessed 2026-08-12. [head-taxonomy]120- Gould et al. — Successor Heads (2023). https://arxiv.org/abs/2312.09230 — accessed 2026-08-12. [head-taxonomy, universality]121- McGrath et al. — The Hydra Effect (2023). https://arxiv.org/abs/2307.15771 — accessed 2026-08-12. [self-repair, ablation-critique]122- Geva et al. — Transformer Feed-Forward Layers Are Key-Value Memories (2021). https://arxiv.org/abs/2012.14913 — accessed 2026-08-12. [MLP-KV]123- Geva et al. — FFN Layers Build Predictions by Promoting Concepts in Vocabulary Space (2022). https://arxiv.org/abs/2203.14680 — accessed 2026-08-12. [MLP-KV]124- Ameisen, Lindsey et al. — Circuit Tracing: Revealing Computational Graphs (2025). https://transformer-circuits.pub/2025/attribution-graphs/methods.html — accessed 2026-08-12. [attribution-graphs]125- Lindsey et al. — On the Biology of a Large Language Model (2025). https://transformer-circuits.pub/2025/attribution-graphs/biology.html — accessed 2026-08-12. [attribution-graphs]126- Anthropic — Tracing Attention Computation Through Feature Interactions (2025). https://transformer-circuits.pub/2025/attention-qk/index.html — accessed 2026-08-12. [QK-attribution]127- Anthropic — Open-sourcing circuit-tracing tools (2025). https://www.anthropic.com/research/open-source-circuit-tracing — accessed 2026-08-12. [circuit-tracer]128- Hanna, Piotrowski et al. — circuit-tracer (2025). https://github.com/safety-research/circuit-tracer ; https://github.com/decoderesearch/circuit-tracer ; https://aclanthology.org/2025.blackboxnlp-1.14.pdf — accessed 2026-08-12. [tooling]129- Conmy et al. — Towards Automated Circuit Discovery (ACDC, NeurIPS 2023). https://arxiv.org/abs/2304.14997 ; https://github.com/ArthurConmy/Automatic-Circuit-Discovery — accessed 2026-08-12. [circuit-discovery]130- Hanna, Pezzelle, Belinkov — Have Faith in Faithfulness (EAP-IG, COLM 2024). https://arxiv.org/abs/2403.17806 ; https://github.com/hannamw/EAP-IG — accessed 2026-08-12. [EAP-IG, faithfulness]131- Bhaskar et al. — Finding Transformer Circuits with Edge Pruning (NeurIPS 2024). https://arxiv.org/abs/2406.16778 ; https://github.com/princeton-nlp/Edge-Pruning — accessed 2026-08-12. [edge-pruning]132- Miller, Chughtai, Saunders — Transformer Circuit Faithfulness Metrics are not Robust (2024). https://arxiv.org/abs/2407.08734 — accessed 2026-08-12. [faithfulness-critique]133- Shi et al. — Hypothesis Testing the Circuit Hypothesis (NeurIPS 2024). https://arxiv.org/abs/2410.13032 ; https://github.com/blei-lab/circuitry — accessed 2026-08-12. [faithfulness, statistics]134- Marks et al. — Sparse Feature Circuits (ICLR 2025). https://arxiv.org/abs/2403.19647 ; https://github.com/saprmarks/feature-circuits — accessed 2026-08-12. [feature-circuits, SHIFT]135- Gao, Rajaram et al. (OpenAI) — Weight-sparse transformers have interpretable circuits (2025). https://arxiv.org/abs/2511.13653 ; https://github.com/openai/circuit_sparsity — accessed 2026-08-12. [weight-sparsity]136- Interpretable individual parameters follow-up (2026). https://arxiv.org/abs/2607.02964 — accessed 2026-08-12. [weight-sparsity, 2026]137- ADAG — Automatically Describing Attribution Graphs (2026). https://arxiv.org/abs/2604.07615 — accessed 2026-08-12. [automation, 2026]138- Circuit Insights: Towards Interpretability Beyond Activations (2025). https://arxiv.org/abs/2510.14936 — accessed 2026-08-12. [weight-based, 2025]139- Anthropic — Circuits Updates June 2026. https://transformer-circuits.pub/2026/june-update/index.html — accessed 2026-08-12. [2026, safety-auditing]140- Neuronpedia — Circuits Research Landscape (Aug 2025). https://www.neuronpedia.org/graph/info — accessed 2026-08-12. [community, attribution-graphs]141- UFO-101 — auto-circuit (multi-ablation library). https://github.com/UFO-101/auto-circuit — accessed 2026-08-12. [tooling, ablations]142- anthropics — attribution-graphs-frontend. https://github.com/anthropics/attribution-graphs-frontend — accessed 2026-08-12. [tooling, viz]143- mlxterp — Mechanistic Interpretability Library for Apple Silicon (COAI). https://github.com/coairesearch/mlxterp — accessed 2026-08-12. [tooling, MLX, Apple-Silicon]144145## §4.5 — Weight-space cartography146147- Sharma, Ash, Misra — The Truth is in There: LASER (ICLR 2024). https://arxiv.org/abs/2312.13558 ; https://github.com/pratyushasharma/laser — accessed 2026-08-12. [weight-SVD, intervention]148- Martin & Mahoney — Heavy-Tailed Universality Predicts Trends in Test Accuracies (2019). https://arxiv.org/abs/1901.08278 — accessed 2026-08-12. [weight-only, spectra]149- Martin, Peng, Mahoney — Predicting NN quality without training/testing data (2020). https://arxiv.org/abs/2002.06716 — accessed 2026-08-12. [weight-only, pre-screen-prior-art]150- WeightWatcher. https://github.com/CalculatedContent/WeightWatcher — accessed 2026-08-12. [tooling, Mac-native]151- Martin & Mahoney — Post-mortem: Simpson's paradox in weight metrics (2021). https://arxiv.org/abs/2106.00734 — accessed 2026-08-12. [critique]152- Dettmers et al. — LLM.int8() (2022). https://arxiv.org/abs/2208.07339 — accessed 2026-08-12. [outliers]153- Sun, Chen, Kolter, Liu — Massive Activations in LLMs (2024). https://arxiv.org/abs/2402.17762 — accessed 2026-08-12. [outliers, sinks]154- Xiao et al. — Attention Sinks / StreamingLLM (2023). https://arxiv.org/abs/2309.17453 — accessed 2026-08-12. [sinks]155- Gu et al. — When Attention Sink Emerges (2024). https://arxiv.org/abs/2410.10781 — accessed 2026-08-12. [sinks]156- Yu et al. — The Super Weight in LLMs (2024). https://arxiv.org/abs/2411.07191 ; https://machinelearning.apple.com/research/the-super-weight — accessed 2026-08-12. [weight-visible-outliers, causal]157- Attention Sink in Transformers: A Survey (2026). https://arxiv.org/pdf/2604.10098 — accessed 2026-08-12. [sinks, 2026]158- A Single Layer to Explain Them All: Massive Activations (2026). https://arxiv.org/html/2605.08504 — accessed 2026-08-12. [outliers, 2026]159- Kornblith et al. — CKA (2019). https://arxiv.org/abs/1905.00414 — accessed 2026-08-12 (spot-check). [similarity]160- Raghu et al. — SVCCA (2017). https://arxiv.org/abs/1706.05806 — accessed 2026-08-12 (spot-check). [similarity]161- Davari et al. — Reliability of CKA (ICLR 2023). https://arxiv.org/abs/2210.16156 — accessed 2026-08-12. [CKA-pathologies]162- Ding, Denain, Steinhardt — Grounding Representation Similarity with Statistical Testing (2021). https://arxiv.org/abs/2108.01661 — accessed 2026-08-12. [similarity-critique]163- Projection kernel between attention-head weight subspaces (2026). https://arxiv.org/pdf/2601.10266 — accessed 2026-08-12. [weight-only-similarity, 2026]164- Gromov et al. — The Unreasonable Ineffectiveness of the Deeper Layers (2024). https://arxiv.org/abs/2403.17887 — accessed 2026-08-12. [layer-redundancy]165- Men et al. — ShortGPT (2024). https://arxiv.org/abs/2403.03853 — accessed 2026-08-12. [layer-redundancy]166- Lenc & Vedaldi — Equivariance and equivalence (2015). https://arxiv.org/abs/1411.5908 — accessed 2026-08-12. [stitching]167- Bansal, Nakkiran, Barak — Revisiting Model Stitching (2021). https://arxiv.org/abs/2106.07682 — accessed 2026-08-12. [stitching]168- Ainsworth et al. — Git Re-Basin (ICLR 2023). https://arxiv.org/abs/2209.04836 ; https://github.com/samuela/git-re-basin — accessed 2026-08-12. [permutation-alignment]169- Jordan et al. — REPAIR (2022). https://arxiv.org/abs/2211.08403 — accessed 2026-08-12. [permutation-alignment]170- Peña et al. — Re-basin via implicit Sinkhorn differentiation (2022). https://arxiv.org/abs/2212.12042 — accessed 2026-08-12. [permutation-alignment]171- Sharma et al. — Simultaneous linear connectivity modulo permutation (2024). https://arxiv.org/abs/2404.06498 — accessed 2026-08-12. 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