fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery | Paper Citation Record

Observation 253820ba-3532-4d96-93e5-fd152fdc5943 · outbound

This paper cites Toy models of superposition.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Toy models of superposition

Reference 1

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Observation fecee89f-c3d2-4640-a749-44e2ca1da506 · outbound

This paper cites Available: https://transformer-circuits.pub/2022/toy_model/index.html.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Available: https://transformer-circuits.pub/2022/toy_model/index.html

Reference 2

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This paper cites Open Problems in Mechanistic Interpretability.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Open Problems in Mechanistic Interpretability

Reference 3

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This paper cites Towards monosemanticity: Decomposing language models with dictionary learning.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Towards monosemanticity: Decomposing language models with dictionary learning

Reference 4

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Observation 49e78471-30b8-42d4-8f74-989ad98d29b2 · outbound

This paper cites Sparse autoencoders find highly interpretable features in language models.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Sparse autoencoders find highly interpretable features in language models

Reference 5

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This paper cites Scaling monosemanticity: Extracting interpretable features from Claude 3 Sonnet.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Scaling monosemanticity: Extracting interpretable features from Claude 3 Sonnet

Reference 6

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This paper cites Scaling and evaluating sparse autoencoders.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Scaling and evaluating sparse autoencoders

Reference 7

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Observation 8960a5be-c63b-4150-a7de-69ab84b29ee9 · outbound

This paper cites Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders

Reference 8

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Observation a2c2c3c4-267f-4688-b229-6f51bcf08a88 · outbound

This paper cites BatchTopK sparse autoencoders.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery BatchTopK sparse autoencoders

Reference 9

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Observation 52f03a6a-16f3-4693-92a7-fc6c34faa581 · outbound

This paper cites PolySAE: Modeling Feature Interactions in Sparse Autoencoders via Polynomial Decoding.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery PolySAE: Modeling Feature Interactions in Sparse Autoencoders via Polynomial Decoding

Reference 10

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Observation cef04beb-1e84-4552-b0bb-869c574eed11 · outbound

This paper cites Knowledge neurons in pretrained transformers.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Knowledge neurons in pretrained transformers

Reference 11

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Observation 066135fd-959f-44d1-bc01-2c5a519b1699 · outbound

This paper cites Remarkable robustness of LLMs: Stages of inference?.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Remarkable robustness of LLMs: Stages of inference?

Reference 12

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Observation 896997a3-52e5-4aec-9a91-9cc366fcbf23 · outbound

This paper cites Available: https://openreview.net/forum?id=Wxh5Xz7NpJ.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Available: https://openreview.net/forum?id=Wxh5Xz7NpJ

Reference 13

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Observation 4a5e9c3b-b491-4b10-996a-8ea5d99086ec · outbound

This paper cites Transformer feed-forward layers build pre- dictions by promoting concepts in the vocabulary space.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Transformer feed-forward layers build pre- dictions by promoting concepts in the vocabulary space

Reference 14

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Observation 1f8ad2d9-9060-4004-bee0-5a9d3778b861 · outbound

This paper cites Universal Neurons in GPT2 Language Models.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Universal Neurons in GPT2 Language Models

Reference 15

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Observation 3fd8a49d-326f-4d73-ac9c-cd5ea0881ca6 · outbound

This paper cites Residual stream analysis with multi-layer SAEs.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Residual stream analysis with multi-layer SAEs

Reference 16

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Observation c86fa4ad-4b28-4d00-9c26-97d4f721a834 · outbound

This paper cites Mechanistic permutability: Match features across layers.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Mechanistic permutability: Match features across layers

Reference 17

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Observation c8e0c87f-87dc-44ce-9c51-84e0aba0c8fd · outbound

This paper cites Sparse crosscoders for cross-layer features and model diffing.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Sparse crosscoders for cross-layer features and model diffing

Reference 18

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Observation 3e8df65e-77e6-4c98-bd84-631a65e0b9e0 · outbound

This paper cites Circuit tracing: Revealing computational graphs in language models.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Circuit tracing: Revealing computational graphs in language models

Reference 19

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Observation 1366b5d9-3217-4cef-84e3-83d681083070 · outbound

This paper cites Overcoming sparsity artifacts in crosscoders to interpret chat-tuning.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Overcoming sparsity artifacts in crosscoders to interpret chat-tuning

Reference 20

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Observation b01a6e81-9f06-4428-862e-cf72c38bd04e · outbound

This paper cites Tensor methods in computer vision and deep learning.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Tensor methods in computer vision and deep learning

Reference 21

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Observation 9c5b06a8-bd2d-4126-bbbc-9b1837089908 · outbound

This paper cites Understanding polysemanticity in neural networks through coding theory.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Understanding polysemanticity in neural networks through coding theory

Reference 22

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Observation 0646545d-94d6-4b42-a359-d80ff963d788 · outbound

This paper cites Dropout: A simple way to prevent neural networks from overfitting.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Dropout: A simple way to prevent neural networks from overfitting

Reference 23

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Observation 28685ab8-ae67-4fad-8f63-967b795dde92 · outbound

This paper cites Towards automated circuit discovery for mechanistic interpretability.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Towards automated circuit discovery for mechanistic interpretability

Reference 24

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Observation 153ecff6-0e71-4cdc-81d3-57b5c7b6dbce · outbound

This paper cites How does gpt-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery How does gpt-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model

Reference 25

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Observation e3b28cab-66e1-407c-bde0-42ceaff508d8 · outbound

This paper cites Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models

Reference 26

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Observation 9ada2af3-b303-4132-8880-eb2aac3d577f · outbound

This paper cites Internal states before wait modulate reasoning patterns.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Internal states before wait modulate reasoning patterns

Reference 27

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Observation 4385c6b1-4782-4168-adb8-4a360f760c4e · outbound

This paper cites Foundation Models for Discovery and Exploration in Chemical Space.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Foundation Models for Discovery and Exploration in Chemical Space

Reference 28

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Observation 6ecc559f-a55a-4157-a049-0eb06d4c66b5 · outbound

This paper cites Towards Understanding Distilled Reasoning Models: A Representational Approach.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Towards Understanding Distilled Reasoning Models: A Representational Approach

Reference 29

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Observation b705a75c-ed14-4b9e-be4b-c15ee994ec58 · outbound

This paper cites Evolution of concepts in language model pre-training.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Evolution of concepts in language model pre-training

Reference 30

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Observation f0cbf1b8-2090-4796-ae64-eb437967c175 · outbound

This paper cites Crosscoding Through Time: Tracking Emergence & Consolidation Of Linguistic Representations Throughout LLM Pretraining.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Crosscoding Through Time: Tracking Emergence & Consolidation Of Linguistic Representations Throughout LLM Pretraining

Reference 31

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Observation 4f9d56d1-5a75-4dea-93b9-5d0dbe462606 · outbound

This paper cites The expression of a tensor or a polyadic as a sum of products.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery The expression of a tensor or a polyadic as a sum of products

Reference 32

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Observation 31d748d0-a14d-47b8-a655-bb02bf827bbb · outbound

This paper cites Tensor Ring Decomposition.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Tensor Ring Decomposition

Reference 33

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Observation d6ae2864-c637-49f0-a36d-a70e447b8491 · outbound

This paper cites Multilinear mixture of experts: Scalable expert specialization through factorization.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Multilinear mixture of experts: Scalable expert specialization through factorization

Reference 34

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Observation 63913227-da98-446b-815a-600c6b3a03d3 · outbound

This paper cites Towards interpretability without sacrifice: Faithful dense layer decomposition with mixture of decoders.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Towards interpretability without sacrifice: Faithful dense layer decomposition with mixture of decoders

Reference 35

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Observation fe258c11-9b9b-4d51-a43b-9ade1ab6f2cc · outbound

This paper cites Extracting and composing robust features with denoising autoencoders.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Extracting and composing robust features with denoising autoencoders

Reference 36

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Observation b44d0b37-ae34-420b-bc41-c6774aa94d6e · outbound

This paper cites Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion

Reference 37

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Observation 4cc19124-ccdf-4f3c-b69b-e4cb8a7dfc54 · outbound

This paper cites BERT: Pre-training of deep bidirec- tional transformers for language understanding.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery BERT: Pre-training of deep bidirec- tional transformers for language understanding

Reference 38

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Observation eea1d31e-f3c2-4d7c-a07c-dfa81ca64a8e · outbound

This paper cites Masked autoencoders are scalable vision learners.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Masked autoencoders are scalable vision learners

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:01:36.717680Z

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Observation 5e633153-44c0-49eb-aec2-ed30500ee915 · outbound

This paper cites Matching pursuits with time-frequency dictionaries.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Matching pursuits with time-frequency dictionaries

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:01:36.730855Z

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Observation b72c6692-61e5-43cc-a76e-ed24e6579eeb · outbound

This paper cites Multilinear multitask learning.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Multilinear multitask learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:01:36.734718Z

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Observation 47f5f985-d346-4692-9ef4-ba07d31c1b84 · outbound

This paper cites Deep Multi-task Representation Learning: A Tensor Factorisation Approach.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Deep Multi-task Representation Learning: A Tensor Factorisation Approach

Reference 42

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Observation 3a601e80-87a8-446e-8ccc-d443b229898d · outbound

This paper cites Training with noise is equivalent to Tikhonov regularization.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Training with noise is equivalent to Tikhonov regularization

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:01:36.743337Z

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Observation 5300b64b-e744-4c05-b035-765f4876e889 · outbound

This paper cites SAELens.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery SAELens

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:01:36.761373Z

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Observation b09a70aa-d880-46c9-9c72-5538b5b3adb4 · outbound

This paper cites Language models are unsu- pervised multitask learners.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Language models are unsu- pervised multitask learners

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:01:36.790629Z

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Observation a47f9176-8bc4-4972-9ddd-5b668b2edcbc · outbound

This paper cites Pythia: A suite for analyzing large language models across training and scaling.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Pythia: A suite for analyzing large language models across training and scaling

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:01:36.705672Z

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Observation 360a1eff-94d4-420b-8232-3f379e90467f · outbound

This paper cites Gemma 2: Improving open language models at a practical size.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Gemma 2: Improving open language models at a practical size

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:01:36.696893Z

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Observation 0f1c2edc-9d80-4092-a34e-014447fe5cbe · outbound

This paper cites OpenWebText corpus.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery OpenWebText corpus

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:51:24.093931Z

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Observation aa30a32a-d289-441c-96f9-ced7ffae6dda · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-05-12T05:51:24.065493Z

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Observation d2add16f-c67b-496e-81d1-2e00db7216c1 · outbound

This paper cites SAEBench: A comprehensive benchmark for sparse autoencoders in language model interpretability.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery SAEBench: A comprehensive benchmark for sparse autoencoders in language model interpretability

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:01:36.700902Z

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Observation dab1d863-37a1-4af0-a076-e6b56d02989b · outbound

This paper cites Bias in bios: A case study of semantic representation bias in a high-stakes setting.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Bias in bios: A case study of semantic representation bias in a high-stakes setting

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:01:36.692622Z

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Observation 46690fe3-f827-45b3-9a1f-f21f71c4fa3e · outbound

This paper cites Character-level convolutional networks for text classification.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Character-level convolutional networks for text classification

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:01:36.786276Z

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Observation b4cec38a-0024-41eb-9544-2f514a2580fe · outbound

This paper cites Europarl: A parallel corpus for statistical machine translation.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Europarl: A parallel corpus for statistical machine translation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:01:36.672506Z

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Observation c45b530a-7f86-41fb-ad23-22f75286fd4c · outbound

This paper cites Github code dataset.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Github code dataset

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:01:36.684170Z

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No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation febb6f8b-ff0f-49b2-a6ed-6b79c27d06c7 · outbound

This paper cites Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-05-12T05:51:24.021338Z

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Observation 25b70170-69b1-4520-97ef-a6690cbc33e1 · outbound

This paper cites Foundations of the PARAFAC procedure: Models and conditions for an “explanatory.

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery Foundations of the PARAFAC procedure: Models and conditions for an “explanatory

Reference 56

Resolution: malformed identifier; arxiv_id, observed 2026-05-12T05:51:24.153164Z

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