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Paper Citation Record · LEDGER

Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2210.09545.

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

pith.paper-citation-record.v1
2210.09545 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:59:34.563333Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T08:04:28.747447Z

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 f769a2be-802e-49d2-a148-cb62a1571138 · inbound

GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts cites this paper.

GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 74

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T06:25:21.156152Z

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=2026-05-15T06:25:20.966510Z digest=sha256:9d3b03889903ae9219da66c5204f7fe772074e488b2a27959edf5d69584a7be6

Observation 66ceb03c-a844-4b5a-8429-cb423a7a5078 · inbound

A Systematic Review of Poisoning Attacks Against Large Language Models cites this paper.

A Systematic Review of Poisoning Attacks Against Large Language Models Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-07T05:59:34.563333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:34.563333Z digest=sha256:78ae42f11d62e297759fc3891bfb5c090763bd933ba1a0717db907599863e69d

Observation 18ef200f-a70b-47a0-9df2-6a583814239e · inbound

Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques cites this paper.

Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 181

Resolution
unresolved
no resolver link, observed 2026-08-06T16:24:30.487336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:24:30.487336Z digest=sha256:453f3056eca551ef2e8da7e93111246982b565154d3338d3c76aca968dec009a

Observation 1dc43788-7721-4823-9d0f-612704c4b474 · inbound

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution cites this paper.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-05T14:42:31.551978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:42:31.551978Z digest=sha256:a0dbb624153e70d2198e7ebce55ca66d87e0665343a67d2acefbc0693113a1df

Observation 62a5fc8b-95a8-434c-a918-a514f3b50bbe · inbound

Uncovering and Aligning Anomalous Attention Heads to Defend Against NLP Backdoor Attacks cites this paper.

Uncovering and Aligning Anomalous Attention Heads to Defend Against NLP Backdoor Attacks Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-17T22:02:08.627638Z

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=2026-05-17T22:01:09.858921Z digest=sha256:411dd22f0cb26a530d4dcac62d4797129dc21fae0d24d79d0af464511af390de

Observation 680ba610-dad7-4b01-9447-52f6936411cf · inbound

Patronus: Identifying and Mitigating Transferable Backdoors in Pre-trained Language Models cites this paper.

Patronus: Identifying and Mitigating Transferable Backdoors in Pre-trained Language Models Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-03T18:08:07.581777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T18:08:07.581777Z digest=sha256:03a7275af7a1eb412420eb0cbab2bd479a83d945ce73be125d729f8b3c170bbd

Observation 26e22186-fe39-4dba-b99b-97c81c1c6e82 · inbound

BackFlush: Knowledge-Free Backdoor Detection and Elimination with Watermark Preservation in Large Language Models cites this paper.

BackFlush: Knowledge-Free Backdoor Detection and Elimination with Watermark Preservation in Large Language Models Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 28

Resolution
metadata mismatch
arxiv_id, observed 2026-05-14T21:02:58.931015Z

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=2026-05-14T21:01:10.756844Z digest=sha256:ec5aafc39ac84d7724c7ce2689eade406724686413f8956b75272f4a1d015273

Observation 80a1cb2f-a269-4774-828b-dbbc367d7bb9 · inbound

Breaking the Rounding Trap: Securing LLMs against Quantization-Conditioned Backdoors cites this paper.

Breaking the Rounding Trap: Securing LLMs against Quantization-Conditioned Backdoors Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-06-30T08:04:28.749090Z

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=2026-06-30T07:47:18.350953Z digest=sha256:af3a42848235cf4ca426e15860a70ac2f9238873fc239ddfcfdb790a40102041

Observation 31690c6c-4c21-4b05-a511-bc5304e931fd · inbound

Breaking the Rounding Trap: Securing LLMs against Quantization-Conditioned Backdoors cites this paper.

Breaking the Rounding Trap: Securing LLMs against Quantization-Conditioned Backdoors Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-04T04:39:06.938899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T04:39:06.938899Z digest=sha256:c89a7e285176bccd0a13eb31e71c5fc48a868078500e53d133a43ab26b4506b7