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Paper Citation Record · LEDGER

Applying sparse autoencoders to unlearn knowledge in language models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2410.19278.

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

pith.paper-citation-record.v1
2410.19278 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:17:15.579347Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 637f78f4-c60e-4005-aabb-8915d3d176fb · inbound

Feature Extraction and Steering for Enhanced Chain-of-Thought Reasoning in Language Models cites this paper.

Feature Extraction and Steering for Enhanced Chain-of-Thought Reasoning in Language Models Applying sparse autoencoders to unlearn knowledge in language models

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-07T15:17:15.579347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:17:15.579347Z digest=sha256:6ee2e6c29e5cd0c12643d597271893deb2b72bdf2eeec16b332879b7a077a565

Observation aff77032-7b38-4df5-8127-889103f6c7a1 · inbound

Position: Use Sparse Autoencoders to Discover Unknowns cites this paper.

Position: Use Sparse Autoencoders to Discover Unknowns Applying sparse autoencoders to unlearn knowledge in language models

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T21:34:33.012045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:34:33.012045Z digest=sha256:806068ac37e53036d6d8dae9ea6b89802067ec15cd813ca4b638aad2963e451f

Observation 627938d6-e79e-4b04-9299-f5e9e42df1d5 · inbound

Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation cites this paper.

Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation Applying sparse autoencoders to unlearn knowledge in language models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-05T20:31:31.321750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:31:31.321750Z digest=sha256:4b030b6e3f0a92217a80fcb4fd095b1dc72738d0d4eef3dce0c05affaa32b59d

Observation bcf2f84e-2779-4434-80cd-b7b28c4e069f · inbound

Sealing The Backdoor: Unlearning Adversarial Text Triggers In Diffusion Models Using Knowledge Distillation cites this paper.

Sealing The Backdoor: Unlearning Adversarial Text Triggers In Diffusion Models Using Knowledge Distillation Applying sparse autoencoders to unlearn knowledge in language models

Reference 100

Resolution
unresolved
no resolver link, observed 2026-08-05T18:47:45.364388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:47:45.364388Z digest=sha256:41d76da192dbbdb6a984438ccd5b1cfe88388717c388ec93353d6d4d18c5ec10

Observation 7bea2e3a-3798-435e-a716-93aae16b0917 · inbound

AI as a Tool for Simulation-Based Experiments in Literary Studies cites this paper.

AI as a Tool for Simulation-Based Experiments in Literary Studies Applying sparse autoencoders to unlearn knowledge in language models

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-06-28T15:02:19.052770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-28T14:54:14.909995Z digest=sha256:b27af9655c144378c724bf5b9d2ba0bf59bac32002540b13f0b9a2f985241570

Observation 43b1a271-7eeb-446a-b84f-5c1cbfbde733 · inbound

Safe-RULE: Safe Reinforcement UnLEarning cites this paper.

Safe-RULE: Safe Reinforcement UnLEarning Applying sparse autoencoders to unlearn knowledge in language models

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-07-03T00:07:28.415595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-27T17:28:09.686163Z digest=sha256:df73b26e848806c451128feab3cddd621227f370121f1bae40266ddf1bceec8e