Exhaustive Circuit Mapping of a Single-Cell Foundation Model Reveals Massive Redundancy, Heavy-Tailed Hub Architecture, and Layer-Dependent Differentiation Control

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References (55)

[1]
Sparse autoencoders reveal organized biological knowledge but minimal regulatory logic in single-cell foundation models: a comparative atlas of Geneformer and scGPT
2026Ihor Kendiukhov
[2]
Systematic Evaluation of Single-Cell Foundation Model Interpretability Reveals Attention Captures Co-Expression Rather Than Unique Regulatory Signal
2026Ihor Kendiukhov
[3]
Scaling and evaluating sparse autoencoders
2024Leo Gao, Tom Dupr'e la Tour et al.
[4]
Mechanistic Interpretability for AI Safety - A Review
2024Leonard Bereska, E. Gavves
[5]
Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models
2024Samuel Marks, C. Rager et al.
[6]
scGPT: toward building a foundation model for single-cell multi-omics using generative AI
2024Haotian Cui, Chloe Wang et al.
[7]
Representation Engineering: A Top-Down Approach to AI Transparency
2023Andy Zou, Long Phan et al.
[8]
Sparse Autoencoders Find Highly Interpretable Features in Language Models
2023Hoagy Cunningham, Aidan Ewart et al.
[9]
Large Scale Foundation Model on Single-cell Transcriptomics
2023Minsheng Hao, Jing Gong et al.
[10]
Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
2023Kenneth Li, Oam Patel et al.
[11]
Transfer learning enables predictions in network biology
2023Christina V. Theodoris, Ling Xiao et al.
[12]
How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model
2023Michael Hanna, Ollie Liu et al.
[13]
Towards Automated Circuit Discovery for Mechanistic Interpretability
2023Arthur Conmy, Augustine N. Mavor-Parker et al.
[14]
The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest
2022Damian Szklarczyk, Rebecca Kirsch et al.
[15]
Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small
2022Kevin Wang, Alexandre Variengien et al.
[16]
scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data
2022Wenchuan Wang, Fan Yang et al.
[17]
Toy Models of Superposition
2022Nelson Elhage, Tristan Hume et al.
[18]
Locating and Editing Factual Associations in GPT
2022Kevin Meng, David Bau et al.
[19]
Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq
2021J. Replogle, R. Saunders et al.
[20]
The Tabula Sapiens: a multiple organ single cell transcriptomic atlas of humans
2021S. Quake

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"This paper explores mechanistic interpretability in biological foundation models through advanced experimental techniques."

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