Paper Citation Record · LEDGER

All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2109.04404.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2109.04404 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:31:40.596180Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T00:59:20.516644Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8a786ea9-17e9-4fb0-9379-41e4f378d389 · inbound

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale cites this paper.

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality

Reference 163

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T13:35:36.162383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e9b3e850-3216-4943-b654-adad5f725e24 · inbound

Eliciting Latent Predictions from Transformers with the Tuned Lens cites this paper.

Eliciting Latent Predictions from Transformers with the Tuned Lens All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality

Reference 85

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T16:54:37.525672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5557f1e0-0335-4c6f-9341-a2b4e341f9fb · inbound

Massive Activations in Large Language Models cites this paper.

Massive Activations in Large Language Models All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality

Reference 150

Resolution: verified exact; arxiv_id, observed 2026-05-16T07:02:54.053751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation badd403d-9992-4da0-893a-58c4a7e19180 · inbound

Efficient Pruning of Text-to-Image Models: Insights from Pruning Stable Diffusion cites this paper.

Efficient Pruning of Text-to-Image Models: Insights from Pruning Stable Diffusion All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-12T14:31:40.596180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66348516-2039-4b13-845f-2103de31276d · inbound

Leveraging Registers in Vision Transformers for Robust Adaptation cites this paper.

Leveraging Registers in Vision Transformers for Robust Adaptation All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T21:29:37.707870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96e0d664-60ec-4190-b371-349bb4dbb13b · inbound

Rethinking Word Similarity: Semantic Similarity through Classification Confusion cites this paper.

Rethinking Word Similarity: Semantic Similarity through Classification Confusion All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-08T18:21:44.351378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e75083c-fe7b-4fb3-b552-3f3259d7abca · inbound

HyperLens: Quantifying Cognitive Effort in LLMs with Fine-grained Confidence Trajectory cites this paper.

HyperLens: Quantifying Cognitive Effort in LLMs with Fine-grained Confidence Trajectory All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T19:36:09.869144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cc03183a-90b6-4f7c-a025-67d02084cc76 · inbound

A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models cites this paper.

A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T07:36:43.109379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9e9ea9ce-bdb4-4d1e-b55e-88fed69116df · inbound

A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models cites this paper.

A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T21:19:29.131868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6770713a-e23c-45c3-80ed-4cfed1e272dc · inbound

Size Doesn't Matter: Cosine-Scored Sparse Autoencoders cites this paper.

Size Doesn't Matter: Cosine-Scored Sparse Autoencoders All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-01T07:15:29.645191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fa7797c1-3e7b-4219-bff6-7634f5edfa14 · inbound

Massive Activations Are Architecturally Robust: A Controlled Scratch/Commitment Residual Stream Test cites this paper.

Massive Activations Are Architecturally Robust: A Controlled Scratch/Commitment Residual Stream Test All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:59:20.519308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dee5b13f-7326-4bb9-a742-9a0e250b63b4 · inbound

MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference cites this paper.

MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T17:12:27.357886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59411b51-d31a-434b-a786-2e787c3c21bd · inbound

Metaphor Tracer: A Theory-Informed Analysis of Hidden States cites this paper.

Metaphor Tracer: A Theory-Informed Analysis of Hidden States All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-31T07:30:05.520280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0285da40-5e7c-4ad3-8057-36ee2fd2ae64 · inbound

Divergent large language model predictions from convergent representations in ambiguous word pairs cites this paper.

Divergent large language model predictions from convergent representations in ambiguous word pairs All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-04T20:22:23.028058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.