Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:42:13.959881Z
Paper Citation Record · LEDGER
As of 8 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 1 inbound Pith citation observation for arXiv:2505.17998.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:42:13.959881Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-10T00:50:49.134622Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-10T00:54:48.765802Z
58 of 58 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 54f9f796-dc94-44e3-bdfa-8935561b6600 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Intrinsic dimensionality explains the effectiveness of language model fine-tuning
Reference 1
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Observation 3dfaa656-3a0c-4931-aa98-3a8e1b81c8f8 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers How to escape sharp minima with random perturbations
Reference 2
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Observation 7033f00e-c35d-4c06-8285-009e92155297 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers CARMA: Enhanced Compositionality in LLMs via Advanced Regularisation and Mutual Information Alignment
Reference 3
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Observation 305c588a-0f63-4b28-85ac-9dcb6e6e3a06 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Intrinsic dimension of data representations in deep neural networks
Reference 4
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Observation 881781b3-0aea-4e6c-b148-87df5a54abf6 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Baker, Charles J
Reference 5
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Observation 40d9a486-8b2f-4393-8357-ec7e9a7e6e0f · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Mutual information neural estimation
Reference 6
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Observation f3d00bd0-af7a-4ae3-8b41-1b45d978c945 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Visualizing high-dimensional loss landscapes with hessian directions
Reference 7
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Observation fa8b2097-3db1-40a7-be6d-faec3b87d122 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Algorithms for manifold learning
Reference 8
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Observation d62fb9fa-6478-4d26-8251-47775b016c44 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Bridging information-theoretic and geometric compression in language models
Reference 9
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Observation 333f5674-2172-4247-814b-e2876f2a23e7 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Emergence of a high-dimensional abstraction phase in language transformers
Reference 10
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Observation 89bd24da-afbe-4f92-8a42-d52ef315f4e9 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Information-theoretic progress measures reveal grokking is an emergent phase transition
Reference 11
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Observation 86db478a-b96e-42f5-a5dd-a1be703e2546 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers The devil is in the detail: Simple tricks improve systematic generalization of transformers
Reference 12
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Observation 88972c94-ef66-4087-be9b-2c9fd136f58b · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Sparse Autoencoders Find Highly Interpretable Features in Language Models
Reference 13
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Observation b277f457-bf29-4123-a67d-9a56eab948d0 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Knowledge Neurons in Pretrained Transformers
Reference 14
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Observation eab9e30a-c869-4828-a8f1-99353d4c46d9 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers The generalized ratios intrinsic dimension estimator
Reference 15
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Observation 23f784e0-5248-430b-8c29-cf48d0cb68f6 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers A Complexity-Based Theory of Compositionality
Reference 16
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Observation 4205720b-396e-49f5-ac59-7ca5a8ecd6e9 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Estimating the intrinsic dimension of datasets by a minimal neighborhood information
Reference 17
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Observation 06a79309-8851-411c-b92e-49e741f4e1eb · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Not All Layers of LLMs Are Necessary During Inference
Reference 18
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Observation c87277f1-00bd-4cbe-9005-e08d89bdd060 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Fillmore
Reference 19
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Observation 198ee351-fda7-43a3-ad2e-8a013737af29 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Studying large language model generalization with influence functions
Reference 20
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Observation 5eb52840-a66f-4899-afa5-db4569a7ee96 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Visualizing and Understanding the Effectiveness of BERT
Reference 21
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Observation 96505720-da93-41be-ad6a-6367f2009d8c · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers An empirical analysis of compute-optimal large language model training
Reference 22
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Observation 36d38018-c528-49ad-8d71-e2bdae93d9fa · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Loss Landscape Degeneracy and Stagewise Development in Transformers
Reference 23
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Observation 874e74ec-647d-4688-bda9-819497f7c57d · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Compositionality decomposed: How do neural networks generalise? Journal of Artificial Intelligence Research, 67:757–795, 2020
Reference 24
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Observation a7b3033b-cf0f-4785-b843-37bdeb121de3 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Are Sparse Autoencoders Useful? A Case Study in Sparse Probing
Reference 25
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Observation 5f94e3bf-ee16-4919-9738-8ec136c7e6b7 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
Reference 26
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Observation 31c4a7cb-99ff-4176-8501-8a7731b70e39 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Flow of information in feed- forward denoising neural networks
Reference 27
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Observation b677f69b-6412-49d9-96d9-6ea98207608f · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Adam: A Method for Stochastic Optimization
Reference 28
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Observation 146740e2-e296-4078-b8df-c4eb80430911 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Reference 29
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Observation 67b0c5ea-cef1-4bcd-a9de-d67d49b0c7ec · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers An iteration method for the solution of the eigenvalue problem of linear differential and integral operators
Reference 30
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Observation 14b76094-7a1a-4fc4-8358-4a0693261c70 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Geometric Signatures of Compositionality Across a Language Model's Lifetime
Reference 31
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Observation c35ce934-db6b-4a40-a329-08db86ed596c · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Break it down: Evidence for structural compositionality in neural networks
Reference 32
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Observation 4513ccb3-e580-4b04-a255-c5cfd495d3ff · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Systematic generalization and emergent structures in transformers trained on structured tasks, 2023
Reference 33
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Observation 7996c54d-93ca-419b-8947-3ba9f2c917b4 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Multiscale geometric methods for data sets i: Multiscale svd, noise and curvature
Reference 34
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Observation ebaca91c-368d-4c05-9a66-f5ea048dd20e · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Deep double descent: Where bigger models and more data hurt.Journal of Statistical Mechanics: Theory and Experiment, 2021(12):124003, 2021
Reference 35
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Observation 15fc1d8c-bf23-4caa-b093-53bf2cf48cfe · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Progress mea- sures for grokking via mechanistic interpretability
Reference 36
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Observation 7c3a5340-ae92-410f-b65a-fe4eb9a0e841 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers In-context Learning and Induction Heads
Reference 37
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Observation aa574f1f-36db-4587-8b82-cb8a29e1fb72 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Lisa: Layerwise importance sampling for memory-efficient large language model fine-tuning
Reference 38
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Observation 7e689f40-08ba-4431-bf17-258a1205f9f5 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers The geometry of categorical and hierarchical concepts in large language models
Reference 39
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Observation 80f4fa42-1a0b-4e61-96e4-5058de3aa99a · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Zipf’s word frequency law in natural language: A critical review and future directions
Reference 40
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Observation eea18095-1869-4a43-9f65-819b6555e8aa · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets
Reference 41
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Observation 7ecc17e4-e366-49ff-b57f-ec5f6d294456 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers The shape of learning: Anisotropy and intrinsic dimen- sions in transformer-based models
Reference 42
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Observation 4ac94895-f962-4946-9816-dbedb7605b46 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers The effective rank: A measure of effective dimensionality
Reference 43
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Observation 745dfc1e-f9e1-474b-899f-a2e5f2b741dc · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers A deeper look at the hessian eigenspectrum of deep neural networks and its applications to regularization
Reference 44
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Observation 4b56f033-2846-4659-aa97-2cca2b620443 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Analysing mathematical reasoning abilities of neural models
Reference 45
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Observation ecbbe8e1-dcf4-42ef-b93e-837d5c879434 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Analytic insights into structure and rank of neural network hessian maps
Reference 46
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Observation d324e018-af54-4058-b8c0-3f9035691025 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Layer by Layer: Uncovering Hidden Representations in Language Models
Reference 47
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Observation 63958d1e-82cc-4f90-a625-ca31dca3a88a · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Negative results for saes on downstream tasks and deprioritising sae research (gdm mech interp team progress up- date #2), March 2025
Reference 48
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Observation 285c6b45-8d1c-4b61-929d-c7825db2a158 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers The geometry of hidden representations of large transformer models
Reference 49
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Observation e39d421b-cdf9-4308-bce5-7bb3400a8eb5 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Attention is all you need
Reference 50
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Observation 87ab987c-cfef-4b33-87d0-835ce5fa48b8 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Loss landscape geometry reveals stagewise development of transformers
Reference 51
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Observation a7ab022a-cea0-4cd2-9bb2-4c938f269b36 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Penalizing gradient norm for efficiently improving generalization in deep learning
Reference 52
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Observation 11f65c12-4f3b-4bb2-b1e4-561effb46326 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Algorithmic capabilities of random transformers
Reference 53
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Observation b3db6963-7903-421f-8a58-a703d02dd134 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Susskind, Samy Bengio, and Preetum Nakkiran
Reference 54
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Observation 780b74b9-3aa4-4f75-b002-2baf6afa248f · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers noun2 verb3 noun5
Reference 55
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Observation 6afe8182-68e1-4536-86f0-694a0d848d7e · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Unresolved cited work
Reference 57
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Observation c41654ee-fbc1-4c6a-8204-dbed1783a169 · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Unresolved cited work
Reference 2019
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Observation bc771ef0-d21e-41dd-9554-6974d468451a · outbound
TRACE for Tracking the Emergence of Semantic Representations in Transformers Unresolved cited work
Reference 2024
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Observation cb30c5bf-1c43-4288-b1ad-d731ef66f01e · inbound
Rethinking Intrinsic Dimension Estimation in Neural Representations TRACE for Tracking the Emergence of Semantic Representations in Transformers
Reference 27
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