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Paper Citation Record · LEDGER

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing

As of 18 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 1 inbound Pith citation observation for arXiv:2502.00602.

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

pith.paper-citation-record.v1
2502.00602 v2

Coverage vector

measured 64 of 64 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T18:26:56.446574Z

measured 65 of 65 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:34:43.970506Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T12:34:47.754484Z

Reference resolution

64 of 64 outbound references displayed

  • verified exact3
  • verified fuzzy8
  • unresolved53
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ddea3436-633f-4e0e-b2a6-df699d930684 · outbound

This paper cites A neural probabilistic language model.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing A neural probabilistic language model

Reference 1

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source=arxiv_source observed=2026-08-09T18:26:55.925116Z digest=sha256:d9d964f840119884e5392481631b662e4c6717134064f6488a47dc53569b19bb

Observation 078661a8-bfce-4497-950d-a85935eda788 · outbound

This paper cites Pattern recognition and machine learning, volume 4.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Pattern recognition and machine learning, volume 4

Reference 2

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source=arxiv_source observed=2026-08-09T18:26:55.932845Z digest=sha256:58cd1275bc85ad6d077dbfd9a6f610dd2fea45832215783e8ade926e48dd4d69

Observation ba286dd8-5a3d-467a-bc63-c0a8ce602f07 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing On the Opportunities and Risks of Foundation Models

Reference 3

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source=arxiv_source observed=2026-08-09T18:26:55.941132Z digest=sha256:56c5d20e3cbe3646a6a4bcece4d2475aa81c54247a9b6201dfa792721663fe98

Observation 81756c13-7e9c-40ed-bd22-e1085a7f593e · outbound

This paper cites Language models are few-shot learners.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Language models are few-shot learners

Reference 4

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source=arxiv_source observed=2026-08-09T18:26:55.951441Z digest=sha256:1d21273f04359594478c687e446e61d17a435e1b524289e545363839a0560fa1

Observation 29e9174a-e7ae-4a99-99e8-a35fa1d0e0b4 · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 5

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source=arxiv_source observed=2026-08-09T18:26:55.957742Z digest=sha256:4259f314ba0c8fadd71fc43425b9108c876bb6da373eb3197ca9dc67fbde4fdc

Observation f3eeec36-cf1f-4a5b-ae2f-b358c1d751f0 · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 6

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source=arxiv_source observed=2026-08-09T18:26:55.964047Z digest=sha256:0f7650ceaec4725b64fde18190c3ac0529c7f68d984a578e23a2abbb6c83d602

Observation 7ae62e6f-339a-4f62-94d4-4e52a7bcd39a · outbound

This paper cites Evaluating the ripple effects of knowledge editing in language models.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Evaluating the ripple effects of knowledge editing in language models

Reference 7

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raw_fallback, observed 2026-08-09T18:26:58.211400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-09T18:26:55.974422Z digest=sha256:84db649d4e0ff250a43f5971924b05492ac1bb0d618130b54d6b6b545f0edc35

Observation 1464037b-690c-4166-b4d9-0b2bbea1218a · outbound

This paper cites Elements of information theory.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Elements of information theory

Reference 8

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source=arxiv_source observed=2026-08-09T18:26:55.982618Z digest=sha256:96ce91a70734b2996670cd76a999398b9694f3a4e8520ee7f0e96b8f54d0e3ef

Observation a3bce83e-7c9f-4d1a-9f4e-0968102593df · outbound

This paper cites Knowledge Neurons in Pretrained Transformers.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Knowledge Neurons in Pretrained Transformers

Reference 9

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source=arxiv_source observed=2026-08-09T18:26:55.989243Z digest=sha256:660cf3286d27ae874767d0619d791338c50009b691b666d9970e3fc0e4511e09

Observation 84ceff7d-93fd-4436-9d34-709d63edfe5e · outbound

This paper cites Editing Factual Knowledge in Language Models.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Editing Factual Knowledge in Language Models

Reference 10

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source=arxiv_source observed=2026-08-09T18:26:55.998480Z digest=sha256:61b01f3cc1e723f4d041df0d4cc6db17078fd06cd6cf186f50e5c99de277260e

Observation da02cd86-2722-4146-88f3-690889a32de8 · outbound

This paper cites Calibrating Factual Knowledge in Pretrained Language Models.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Calibrating Factual Knowledge in Pretrained Language Models

Reference 11

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source=arxiv_source observed=2026-08-09T18:26:56.009198Z digest=sha256:d7cc38ad5314db8b13d848ce2e106f26a9e9cc9187c27411864258d3b2873c39

Observation 73079c1e-86f0-41f8-b8c7-81342ca4022f · outbound

This paper cites A Survey on In-context Learning.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing A Survey on In-context Learning

Reference 12

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source=arxiv_source observed=2026-08-09T18:26:56.015569Z digest=sha256:6f2c00d7b077608d7a40cebc280e8be5738a588c76842de42a16cf18e2220480

Observation 7f8a3aab-b197-4cdd-931f-178b98ece498 · outbound

This paper cites The Llama 3 Herd of Models.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing The Llama 3 Herd of Models

Reference 13

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source=arxiv_source observed=2026-08-09T18:26:56.021780Z digest=sha256:5f5dcb0f181b21f119846229408c3cd1b5de65f4f6432f628395e1f3bc2460ab

Observation cb2914bf-a048-4f64-87ac-d609bee73200 · outbound

This paper cites ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection

Reference 14

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source=arxiv_source observed=2026-08-09T18:26:56.028321Z digest=sha256:a8e89e4d79e382daf4b042ed19893655e671a0060268a93694e20171a9e6fb5f

Observation 6655497d-0ecd-452c-b6fd-66c1fef5200d · outbound

This paper cites Aging with grace: Lifelong model editing with discrete key-value adaptors.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Aging with grace: Lifelong model editing with discrete key-value adaptors

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-09T18:26:56.037739Z digest=sha256:1f020eeb87144e1693f061550afdf61f697fbecfb6ab06137b4f25a4ebfd06ad

Observation fb92d708-92b7-4287-9518-96b09df24047 · outbound

This paper cites Truncation Sampling as Language Model Desmoothing.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Truncation Sampling as Language Model Desmoothing

Reference 16

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source=arxiv_source observed=2026-08-09T18:26:56.047686Z digest=sha256:8c78e17578d2138a27504cd2353598b5e921705af9a511c83b5965c79af5e09d

Observation 2204d177-fce7-443a-b638-7d6af2a8474d · outbound

This paper cites Long short-term memory.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Long short-term memory

Reference 17

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source=arxiv_source observed=2026-08-09T18:26:56.054201Z digest=sha256:6455658515bc73b696104798ee82cda4539cda52d4d571ca9d30b75cab033d21

Observation 5f4547cb-941a-4dbc-a830-c37a5db7b4a2 · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 18

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source=arxiv_source observed=2026-08-09T18:26:56.061090Z digest=sha256:ff976428eedf685f165eed1c316cbde3b2e668d07c80e99a2de248eea0508048

Observation 2b3f2b99-773d-4efa-bd06-94c05804d655 · outbound

This paper cites Transformer-Patcher: One Mistake worth One Neuron.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Transformer-Patcher: One Mistake worth One Neuron

Reference 19

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source=arxiv_source observed=2026-08-09T18:26:56.068084Z digest=sha256:12b5b0741f38416d9b3746f7d82c0d0dc105f47846fc2f3a0d3a97d48a833b6f

Observation 4b2ee85d-a69d-42a5-b0ae-e3c27455eb83 · outbound

This paper cites Survey of hallucination in natural language generation.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Survey of hallucination in natural language generation

Reference 20

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source=arxiv_source observed=2026-08-09T18:26:56.074346Z digest=sha256:202c4cb0ed6453e996d36ac300dbb3364e0cdd67ad32a38563defe399d8cb1ff

Observation 4b9ffcaa-33ef-41a6-88db-92a5551479ab · outbound

This paper cites Learning to Edit: Aligning LLMs with Knowledge Editing.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Learning to Edit: Aligning LLMs with Knowledge Editing

Reference 21

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source=arxiv_source observed=2026-08-09T18:26:56.080612Z digest=sha256:b542dec5795611644ed73138fee38706ef861b8bed59742a8925b4b4bd2893cb

Observation ff2b0342-b4ae-4b42-8ede-1a26b2746cdc · outbound

This paper cites Understanding black-box predictions via influence functions.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Understanding black-box predictions via influence functions

Reference 22

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source=arxiv_source observed=2026-08-09T18:26:56.088618Z digest=sha256:4c67638890f668a5b0bc1532c5b22967321c9108c0043d52ee683fdb2b8f8720

Observation 288acc03-3efc-43fe-a696-161161195d9d · outbound

This paper cites Large language models are zero-shot reasoners.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Large language models are zero-shot reasoners

Reference 23

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source=arxiv_source observed=2026-08-09T18:26:56.098715Z digest=sha256:bf9b1e48b21363430272853a5ce7e715eba04be2ee43047f614436864e658b21

Observation e3096a34-2940-4966-8719-5665c0c30af3 · outbound

This paper cites Professor Forcing: A New Algorithm for Training Recurrent Networks.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Professor Forcing: A New Algorithm for Training Recurrent Networks

Reference 24

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source=arxiv_source observed=2026-08-09T18:26:56.104597Z digest=sha256:934075e7c17801771759bfc959f7b8659cb714af499f310e77658ea4a716273d

Observation 3c0423eb-b3ae-4650-9025-d2c75da7f966 · outbound

This paper cites Adaptive Label Smoothing with Self-Knowledge in Natural Language Generation.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Adaptive Label Smoothing with Self-Knowledge in Natural Language Generation

Reference 25

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local_arxiv, observed 2026-08-09T18:26:57.292112Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-09T18:26:56.111113Z digest=sha256:52afb8e523df7dadbfb64e497a3878f4a0a9b41c15805107da11fbd4d1b2ebd2

Observation 18a498a9-1b16-4f90-8455-48dbcefaecdb · outbound

This paper cites BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Reference 26

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source=arxiv_source observed=2026-08-09T18:26:56.117440Z digest=sha256:c0b4f89479fb1074c1f2a5efcb4484a131f22b1bd5040bcb5da23e78cf8e81a3

Observation 291f52ab-e6df-46de-a3a3-c9871687ee6d · outbound

This paper cites Locating and editing factual associations in gpt.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Locating and editing factual associations in gpt

Reference 27

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source=arxiv_source observed=2026-08-09T18:26:56.124484Z digest=sha256:0d2712a1dd22c0a2199ed692c82918aed80a5f0a4b37e2f8601dc00b8baa015f

Observation 64bae0d1-053e-4957-9f69-bcdd2271f538 · outbound

This paper cites Mass-Editing Memory in a Transformer.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Mass-Editing Memory in a Transformer

Reference 28

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source=arxiv_source observed=2026-08-09T18:26:56.132279Z digest=sha256:94af731b8ade5dd1f0a8bdd3e68bc5b8fe1e4a5aaf100d371de511b0bf346456

Observation aefd6953-e1e7-4aee-9947-c8ccff503e5c · outbound

This paper cites Fast Model Editing at Scale.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Fast Model Editing at Scale

Reference 29

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T18:26:56.139474Z digest=sha256:74f150e48c93784e508dd2d835d7dd69aa1b11cd4b994637364b6c43f1786598

Observation ad76e627-7940-4aba-9845-9ca38931f369 · outbound

This paper cites Memory-based model editing at scale.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Memory-based model editing at scale

Reference 30

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verified fuzzy
raw_fallback, observed 2026-08-09T18:26:58.063878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-09T18:26:56.146356Z digest=sha256:2bceb7308fbbbb8fd5f7b4c4df31f8a271f87e1fce0e068afe60282655baacbf

Observation 3aec5a26-c1d7-4138-89b5-8bc7eb60ac79 · outbound

This paper cites When does label smoothing help? Advances in neural information processing systems, 32, 2019.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing When does label smoothing help? Advances in neural information processing systems, 32, 2019

Reference 31

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raw_fallback, observed 2026-08-09T18:26:58.039521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-09T18:26:56.155439Z digest=sha256:67dfdd25a261e05e3feda035f974b0748965f287b912c048625e2aa055003d61

Observation f9d0b7c9-78d6-4610-9c6e-2a69649b669f · outbound

This paper cites Training language models to follow instructions with human feedback.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Training language models to follow instructions with human feedback

Reference 32

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source=arxiv_source observed=2026-08-09T18:26:56.162743Z digest=sha256:7a59a7ecf52523d8c65265bc77e741bb88e543009ba98b61177394148862f327

Observation f2878f8a-c7a3-423e-866a-899e9e9d7c51 · outbound

This paper cites Language models are unsupervised multitask learners.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Language models are unsupervised multitask learners

Reference 33

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source=arxiv_source observed=2026-08-09T18:26:56.170394Z digest=sha256:2f1dc644f6e087fca120376add27501638641b8d47b2d3341be3967bd138b71d

Observation 7f8155a4-cc06-493f-bcf4-6ec5dfc957a1 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Direct preference optimization: Your language model is secretly a reward model

Reference 34

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source=arxiv_source observed=2026-08-09T18:26:56.187968Z digest=sha256:1ac5826ae351600381dd9b1c5a5efe02e6c4540fc4b9eb1bc79b34bc6d4845ee

Observation a93c5575-3a6e-4c6b-81c6-b1832fe3b571 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 35

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source=arxiv_source observed=2026-08-09T18:26:56.197022Z digest=sha256:37cf9b68d758310f87a5177f5214321b7c5855ce1b1104f3a17621217ee1d78e

Observation ffda8ba5-100f-4c9d-ae08-1b83f1ac6123 · outbound

This paper cites Knowledge Editing in Language Models via Adapted Direct Preference Optimization.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Knowledge Editing in Language Models via Adapted Direct Preference Optimization

Reference 36

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source=arxiv_source observed=2026-08-09T18:26:56.205565Z digest=sha256:06799d49e106b57bd6851ff65bc4e1dc71b957be95e3b379c4ad933d1a7252dd

Observation c86e1b7e-70b0-44c1-84e5-5b4aa4ce6073 · outbound

This paper cites Do Massively Pretrained Language Models Make Better Storytellers?.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Do Massively Pretrained Language Models Make Better Storytellers?

Reference 37

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verified exact
local_arxiv, observed 2026-08-09T18:26:57.133668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-09T18:26:56.213066Z digest=sha256:48d7431e24f7872aea5f7d7168ef396591f269b1936c600460414acdbadd0d0f

Observation a31c7010-11aa-4f31-af75-1e33f3c6f5dc · outbound

This paper cites Sequence to Sequence Learning with Neural Networks.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Sequence to Sequence Learning with Neural Networks

Reference 38

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T18:26:56.219166Z digest=sha256:03b2b2d577ccc241271148fdae605229cf60caffb21fe1af573084462bba2b2e

Observation 44abd8b6-acdd-4147-8261-12f0214518f9 · outbound

This paper cites Rethinking the inception architecture for computer vision.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Rethinking the inception architecture for computer vision

Reference 39

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verified fuzzy
raw_fallback, observed 2026-08-09T18:26:57.825154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-09T18:26:56.226329Z digest=sha256:b1df24d25850b49e69a1f052e7069e5868e3b76274a329f8e3630a340ada5667

Observation 72ffb4d0-3558-4913-bfb7-165291b11dda · outbound

This paper cites Top-$n\sigma$: Not All Logits Are You Need.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Top-$n\sigma$: Not All Logits Are You Need

Reference 40

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source=arxiv_source observed=2026-08-09T18:26:56.233285Z digest=sha256:521a2dc2addcec9dcb307537eaf53dda74afa59d4b7522ab8fbab1aee5ccc8bd

Observation 7ef1a219-a826-41f6-b39f-721b7b634f61 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 41

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T18:26:56.241970Z digest=sha256:1b668433873fe8f3a57400b66dd9db69b054014beb41d4181048b0eef4386494

Observation f088568f-0dda-40dc-b9a6-a38ca9946bba · outbound

This paper cites Attention is all you need.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Attention is all you need

Reference 42

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no resolver link, observed 2026-08-09T18:26:56.251845Z

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source=arxiv_source observed=2026-08-09T18:26:56.251845Z digest=sha256:61b216b28ccdb51681d2c57dc0e40e7a8537629e823c4ddb120450de2faf38a6

Observation 3dd24a00-f392-4c90-a37f-a66f048efb4d · outbound

This paper cites Beyond reverse KL : Generalizing direct preference optimization with diverse divergence constraints.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Beyond reverse KL : Generalizing direct preference optimization with diverse divergence constraints

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T18:26:57.781716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-09T18:26:56.260103Z digest=sha256:e721dc3985944bb295b765a957ba5ce7a41a1f76951cc73fabcf6034512efa9f

Observation d3673c36-b155-43a3-a7ac-b846dc343f53 · outbound

This paper cites Making Large Language Models Better Reasoners with Alignment.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Making Large Language Models Better Reasoners with Alignment

Reference 44

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no resolver link, observed 2026-08-09T18:26:56.266635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T18:26:56.266635Z digest=sha256:bf61ed763d3eee7692adf7e218218183ba9239e00bbecf7d00b07f67c18cab80

Observation dbdbd37d-a875-4f37-8fdf-149b2572d569 · outbound

This paper cites WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models

Reference 45

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no resolver link, observed 2026-08-09T18:26:56.274579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T18:26:56.274579Z digest=sha256:93419b0ba03936dac25e5786bd683599250216ee2aefdfa1911435c49d74beb9

Observation b9d15102-87f5-49f6-8f4c-1e81953a15ec · outbound

This paper cites EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models

Reference 46

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no resolver link, observed 2026-08-09T18:26:56.284030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T18:26:56.284030Z digest=sha256:7b3c05bfbc1df00f2e24133861ae259718e024ffa8d1ec6ec9527ab6a23d0693

Observation b63fbf01-e451-4302-a1ec-6ce0cd311446 · outbound

This paper cites Knowledge Editing for Large Language Models: A Survey.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Knowledge Editing for Large Language Models: A Survey

Reference 47

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no resolver link, observed 2026-08-09T18:26:56.291805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T18:26:56.291805Z digest=sha256:c6124116d76c500c1da02002260b556d1511537ced3a1b7e47841c5267f9fd65

Observation 035aec74-33e3-4e82-a1e8-d60a72466475 · outbound

This paper cites DeepEdit: Knowledge Editing as Decoding with Constraints.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing DeepEdit: Knowledge Editing as Decoding with Constraints

Reference 48

Resolution
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no resolver link, observed 2026-08-09T18:26:56.299568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T18:26:56.299568Z digest=sha256:b30d88d186491d2cd7e5d9626d7ac2c5a5dd786a329684412474f0350c2cca68

Observation fc2dde73-c9f1-4776-ae94-ac73ede65bdb · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Chain-of-thought prompting elicits reasoning in large language models

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-09T18:26:56.307652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T18:26:56.307652Z digest=sha256:3ecc47d934ba7de28f9875cc50fde14be4be06048b3b48d4bf76cd3f531fd233

Observation 1a4c97d8-9de6-4984-8dca-5f243266f245 · outbound

This paper cites Stable Knowledge Editing in Large Language Models.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Stable Knowledge Editing in Large Language Models

Reference 50

Resolution
verified exact
local_arxiv, observed 2026-08-09T18:26:56.868753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-09T18:26:56.317453Z digest=sha256:eaaeeb54f06b4f121b1a436062ba58f28ee62fd0df51710de76263245c9118b5

Observation 97747534-0bae-46b2-a44d-5a944e427d08 · outbound

This paper cites DocTER: Evaluating Document-based Knowledge Editing.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing DocTER: Evaluating Document-based Knowledge Editing

Reference 51

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unresolved
no resolver link, observed 2026-08-09T18:26:56.324733Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-09T18:26:56.324733Z digest=sha256:d5ff8c388a9677322ed97343f8511d82640b94050b4f973ba8556a250b358bb2

Observation 3a4b1975-099b-403f-b3f6-dd201f5da4e3 · outbound

This paper cites On early stopping in gradient descent learning.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing On early stopping in gradient descent learning

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T18:26:57.740740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-09T18:26:56.332073Z digest=sha256:7134521f8f3d9fe04bee2365000709a36661ea473580acd3b8a6acbb5f14cfc2

Observation 8e49f47a-7002-4faf-a188-9d5c1d5acbde · outbound

This paper cites Editing Large Language Models: Problems, Methods, and Opportunities.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Editing Large Language Models: Problems, Methods, and Opportunities

Reference 53

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unresolved
no resolver link, observed 2026-08-09T18:26:56.340971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T18:26:56.340971Z digest=sha256:d4434bd29d48ded6d3ae039d14c514ca933e27760bb0eb3d5929939d409d806c

Observation 22b9ae43-f800-4f61-9592-6b9c930a038f · outbound

This paper cites Melo: Enhancing model editing with neuron-indexed dynamic lora.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Melo: Enhancing model editing with neuron-indexed dynamic lora

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T18:26:57.719476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-09T18:26:56.347781Z digest=sha256:5e9001963160fad31663586c803d3e06f349c7626971a5abc71063172bc1681c

Observation 88f435bb-d08e-4a64-9aa3-4da4f8ea247b · outbound

This paper cites Uncovering Overfitting in Large Language Model Editing.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Uncovering Overfitting in Large Language Model Editing

Reference 55

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no resolver link, observed 2026-08-09T18:26:56.356054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T18:26:56.356054Z digest=sha256:65160d71a1b105e7e84139993327f6e3d3511b843972e462cdc42b158cf6c700

Observation f0bb1929-c95e-4343-9de2-610b5fcc3995 · outbound

This paper cites InstructEdit: Instruction-based Knowledge Editing for Large Language Models.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing InstructEdit: Instruction-based Knowledge Editing for Large Language Models

Reference 56

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unresolved
no resolver link, observed 2026-08-09T18:26:56.362331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T18:26:56.362331Z digest=sha256:e6c118cd4914e31dd808a2608faff6c96d8d6e256027c1aa39c139bc959b1fd3

Observation 1fd3d8df-cf8b-4622-860b-5ea3e2833cb3 · outbound

This paper cites A Comprehensive Study of Knowledge Editing for Large Language Models.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing A Comprehensive Study of Knowledge Editing for Large Language Models

Reference 57

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unresolved
no resolver link, observed 2026-08-09T18:26:56.372616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T18:26:56.372616Z digest=sha256:33e3c2f868f12d97e534a64529fdf32b0c159d3fb09c7dde3d75332ad799c9ad

Observation 964594c3-526f-4090-af0e-fb11b1b9a7f9 · outbound

This paper cites Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning

Reference 58

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unresolved
no resolver link, observed 2026-08-09T18:26:56.384264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T18:26:56.384264Z digest=sha256:9ebad6956f441657fe605be050add30284f41924d4c73b699cc874a035a007fe

Observation c4bba2d9-b4be-4a33-b465-fed4a0a72dae · outbound

This paper cites Self-distillation as instance-specific label smoothing.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Self-distillation as instance-specific label smoothing

Reference 59

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unresolved
no resolver link, observed 2026-08-09T18:26:56.406747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T18:26:56.406747Z digest=sha256:a1b3a5c1631340dd0b22edf3134421883f9894a495247a52a23b47f31340ff29

Observation 1055e602-e0a2-4bf2-8cfc-7c7be6c127af · outbound

This paper cites Can We Edit Factual Knowledge by In-Context Learning?.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Can We Edit Factual Knowledge by In-Context Learning?

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-09T18:26:56.413597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T18:26:56.413597Z digest=sha256:224aab1ee371184d427de6d5c940b0c16f5004dd816a77fe305fd3bcee1c8719

Observation 162c339c-90f4-4f6c-ac81-d3c296bdec1c · outbound

This paper cites MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions

Reference 61

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unresolved
no resolver link, observed 2026-08-09T18:26:56.424341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T18:26:56.424341Z digest=sha256:16432365559073271829826bb2f8fd453fee5b3b5f15454784c836160a000c34

Observation 5ed2808a-0148-4403-8ec7-d1902e08bdad · outbound

This paper cites A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

Reference 62

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no resolver link, observed 2026-08-09T18:26:56.431156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T18:26:56.431156Z digest=sha256:c901c730741625d3d9b250c97e5024e077037c3423fe3c1a1746e49ea53714b8

Observation dd1a85f2-4549-4dce-a928-a0a64c61519f · outbound

This paper cites Modifying Memories in Transformer Models.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing Modifying Memories in Transformer Models

Reference 63

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no resolver link, observed 2026-08-09T18:26:56.438382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T18:26:56.438382Z digest=sha256:4aaee7f8142e9624eab9b8fbee7009cd5692ae3abc2a3fa7112f53bb4ae3e1ca

Observation 2a16fbfe-5d10-4b3e-a65e-4f9785d50112 · outbound

This paper cites write newline.

Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing write newline

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-09T18:26:56.446574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T18:26:56.446574Z digest=sha256:1706bff204dffac1a99f83c1ac8e56ea7eefd04213ed2dc682c9f9ddf4a55209

Pith citing papers

Observation 496d7cfd-03fe-4e65-9dff-11f962fc3063 · inbound

ScienceMeter: Tracking Scientific Knowledge Updates in Language Models cites this paper.

ScienceMeter: Tracking Scientific Knowledge Updates in Language Models Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing

Reference 32

Resolution
verified exact
local_arxiv, observed 2026-08-07T12:34:47.862614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T12:34:43.970506Z digest=sha256:19908b28ac15f784294eca7ff01efe1ddb0b5f133f5fe98f1f81afd887efa9ec