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Paper Citation Record · LEDGER

Understanding polysemanticity in neural networks through coding theory

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2401.17975.

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

pith.paper-citation-record.v1
2401.17975 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:14:11.783109Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T04:12:30.755621Z

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 93258bb2-0e02-453b-92c5-90f3c641b28b · inbound

SAFR: Neuron Redistribution for Interpretability cites this paper.

SAFR: Neuron Redistribution for Interpretability Understanding polysemanticity in neural networks through coding theory

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-10T16:14:11.783109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T16:14:11.783109Z digest=sha256:395a096132dbdccbd44bdbb4522ac7f68884a22340d93f89903931ebf2eb1797

Observation e024d7d1-7da1-4336-ac31-61e59c51c31f · inbound

Neurons Speak in Ranges: Breaking Free from Discrete Neuronal Attribution cites this paper.

Neurons Speak in Ranges: Breaking Free from Discrete Neuronal Attribution Understanding polysemanticity in neural networks through coding theory

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-23T04:12:30.759111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-23T04:09:40.210836Z digest=sha256:7de466d63eaede9bfd97f32ad81443133d503db0c6a4d7c5b497ed4645d9d031

Observation 298c474c-7e91-404f-89ee-10af19a0a875 · inbound

Mammo-SAE: Interpreting Breast Cancer Concept Learning with Sparse Autoencoders cites this paper.

Mammo-SAE: Interpreting Breast Cancer Concept Learning with Sparse Autoencoders Understanding polysemanticity in neural networks through coding theory

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T15:42:41.165164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:42:41.165164Z digest=sha256:adb009638a6997e87701f53c0a1547bb5dc9a5cde3bf628eef2310792e9119fd

Observation d29db268-316e-4ad0-9a8e-c6b1a4e7b574 · inbound

Representational Curvature Modulates Behavioral Uncertainty in Large Language Models cites this paper.

Representational Curvature Modulates Behavioral Uncertainty in Large Language Models Understanding polysemanticity in neural networks through coding theory

Reference 3

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T21:56:17.452354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-08T03:46:29.265708Z digest=sha256:b8cc7b7cced5579252b2cbf3644872beac278ac0c601d639c53fec98df2bac70

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

fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery cites this paper.

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

Reference 22

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T05:51:24.031218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-12T04:54:41.225199Z digest=sha256:87f081959df97ff4562b5225ef3406d0544084cf90ce8d72b116a8d25e702aa2