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Paper Citation Record · LEDGER

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning

As of 10 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2607.08393.

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

pith.paper-citation-record.v1
2607.08393 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-10T08:11:11.566370Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

  • verified exact3
  • verified fuzzy25
  • unresolved1
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch6

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 41d3cf17-19a8-4434-ab71-04c903c78707 · outbound

This paper cites an unresolved cited work.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Unresolved cited work

Reference 1

Resolution
unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:ceb5625c336b89ea3f594042472742c80b2f6896d3393f73beefcfb34cba4f28

Observation fff37db9-4bde-4924-88be-5b4230fb18f3 · outbound

This paper cites Understanding intermediate layers using linear classifier probes.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Understanding intermediate layers using linear classifier probes

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.366561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:c1b73617791c7b3c01bf7488cc0e160dc095c1f4e93e18778213f0c734ad5c71

Observation 0922f8d9-5744-454f-afbe-ab9f58d7ae37 · outbound

This paper cites Physics of language models: Part 3.3, knowledge capacity scaling laws.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Physics of language models: Part 3.3, knowledge capacity scaling laws

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.375760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:72d9f0c713e1523a7875493b5444157c525576ab6e085e2fad3cd7ef9ee3a315

Observation 3b9f4fc1-9cbc-4588-98ed-a333de0a1669 · outbound

This paper cites Cocarascu, F.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Cocarascu, F

Reference 4

Resolution
malformed identifier
doi_truncated, observed 2026-07-10T08:16:58.534665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:5b6c66ab68fef937e78691726f0e64f215d5a91ecf4e781029791ca9cb1ad6fd

Observation c0dbd0c1-0629-49b8-95a9-619bfa9dd793 · outbound

This paper cites a is b” fail to learn “b is a.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning a is b” fail to learn “b is a

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.351180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:1096faee5a3aab4baf7c24ac8b5675a79baa9f09adc5c359523dc7363d2287a8

Observation 4c7aabc0-32fe-47af-9944-ee28fcff9154 · outbound

This paper cites Hopping too late: Exploring the limitations of large language models on multi-hop queries.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Hopping too late: Exploring the limitations of large language models on multi-hop queries

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.368429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:be9bd1190d3a14938f82eb5addc75d628798a65bf2466ff72ebf144124f92980

Observation cf46e937-ead8-4d10-9590-cbcc9e7aff1e · outbound

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

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Evaluating the ripple effects of knowledge editing in language models

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.379209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:3d6de2ee87c2fe8542f79f83069a02a5b366041806ae7526fbd0df10620bd928

Observation 8e1868b6-e4c8-42c2-9af0-9bdc298fa66c · outbound

This paper cites doi: 10.18653/v1/2022.acl-long.581.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning doi: 10.18653/v1/2022.acl-long.581

Reference 8

Resolution
metadata mismatch
doi, observed 2026-07-10T08:16:58.521192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:4e061d137618f2c64382d1dd0d747feea4f858117e7edfb2f22620a0d910620f

Observation 35980cfc-7680-44dd-ba60-3c823b59802e · outbound

This paper cites Scaling and evaluating sparse autoencoders.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Scaling and evaluating sparse autoencoders

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.372094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:26ad89f97f8ea6d226e23b062dd24d820d5c413118317c4ae621e37090d1097c

Observation ff7a7c0f-2342-4446-8fce-b5f3a6304966 · outbound

This paper cites Key-value memory in the brain.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Key-value memory in the brain

Reference 10

Resolution
metadata mismatch
local_arxiv, observed 2026-07-10T08:16:58.541359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:a2bc0359f2c204744ef431b4cc60a3c8de7febd957421a30ea145e8feb1455b6

Observation 321608e5-9b84-4e40-a544-2ab2565c6454 · outbound

This paper cites Dissecting recall of factual associations in auto-regressive language models.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Dissecting recall of factual associations in auto-regressive language models

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.393521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:242217044a1e459b00f1fe1536fce4c174fa5db5a84a8cd98586c4322bcec3fb

Observation 6e67a8b4-9c0e-47aa-90f1-425f48988141 · outbound

This paper cites Patchscopes: A unifying framework for inspecting hidden representations of language models.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Patchscopes: A unifying framework for inspecting hidden representations of language models

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.395388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:75d5462b57f26bfa1245ea698909ea7389fa2cc4fc28aa1adbfc6bfd6a653417

Observation 1862aa5d-2902-49b2-9a8e-538a223591c3 · outbound

This paper cites Model editing at scale leads to gradual and catastrophic forgetting.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Model editing at scale leads to gradual and catastrophic forgetting

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.391591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:cbbf23d319f2967179ea73b6b9305bef89817bb0e0060946ed781afe5ad02a2c

Observation 0bfee5fe-cf15-43da-bac3-f45fda8aaeeb · outbound

This paper cites Sparse autoencoders find highly interpretable features in language models.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Sparse autoencoders find highly interpretable features in language models

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.389609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:571e894a7276f28c4786c81d9896cb54af644fc2b3ab802a3016adee21dc828f

Observation e8b1ce0f-bdfb-4212-90e2-029a15c5bc11 · outbound

This paper cites Where to find grokking in llm pretraining? monitor memorization-to-generalization without test.arXiv preprint arXiv:2506.21551, 2025k.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Where to find grokking in llm pretraining? monitor memorization-to-generalization without test.arXiv preprint arXiv:2506.21551, 2025k

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-07-10T08:16:58.525175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:937d1667803507337cc0e0faee0268db6476bf497000d626e3a09b844cd04952

Observation 19395955-3524-4b73-953a-373ff4516a65 · outbound

This paper cites Michaud, Max Tegmark, and Mike Williams.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Michaud, Max Tegmark, and Mike Williams

Reference 16

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:3ffcadd3c552292568622ebb42431367f9d5207b14c0f5b33952fc57b93a8c77

Observation 23dc7490-8bd7-4e9c-a2ec-1fa6198d392e · outbound

This paper cites Mass-editing memory in a transformer.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Mass-editing memory in a transformer

Reference 17

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:bf22c5663cce08f90e66df59144f266a0cc925c8b3576e0d4ef280dd7a07ab47

Observation c19347d2-f0ff-43d8-878a-eb1f3bf34b6d · outbound

This paper cites Locating and editing factual associations in GPT.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Locating and editing factual associations in GPT

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.383143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:1e28de5dbea970bbbb5ecfe88487a34b711a55d0414c6740cfd68ff28e855001

Observation 996e8311-1e2e-4906-bc61-b1a41a5ca82e · outbound

This paper cites How much do language models memorize?.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning How much do language models memorize?

Reference 19

Resolution
metadata mismatch
local_arxiv, observed 2026-07-10T08:16:58.784744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:e9225d6014048711880ee80b8a8f63dabd94796628414802e94ae8ce5c714bfd

Observation de209e04-5640-4cdb-9367-bb3ce7211807 · outbound

This paper cites Progress measures for grokking via mechanistic interpretability.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Progress measures for grokking via mechanistic interpretability

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.385015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:6595e4660421b62b7c3eaed0fae7137622aaf22f8ec38c5ccafce1d9ca2e5b63

Observation ffa22f44-a852-4937-bbef-d94487c349c6 · outbound

This paper cites interpreting GPT : the logit lens, August 2020.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning interpreting GPT : the logit lens, August 2020

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.398759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:51ceedf6e0d8a1c5bde9e8f8e5b5070966a1768a72e705c99156c2a7fb6b0839

Observation 0986e177-7118-4831-addd-39a99b2d85c6 · outbound

This paper cites In-context Learning and Induction Heads.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning In-context Learning and Induction Heads

Reference 22

Resolution
metadata mismatch
local_arxiv, observed 2026-07-10T08:16:58.532280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:80d9992e3a710ab2dbf5f96e99dce4794460d68ef079f9d29397d08eaa7949b1

Observation 7975282f-011f-40af-91ba-f1f09a2aa38c · outbound

This paper cites Fine-tuning or retrieval? comparing knowledge injection in llms.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Fine-tuning or retrieval? comparing knowledge injection in llms

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.381236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:1de7e48a775506728ded5a58c2fd79ab4940d7db3285ba79aba83db36e971024

Observation 8f6d7fd8-3025-484e-bc32-c7128b553e60 · outbound

This paper cites Burbi, A.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Burbi, A

Reference 24

Resolution
metadata mismatch
arxiv_id, observed 2026-07-10T08:16:58.538181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:7f19c93548150f2c83cc88f22c1443e80e2c5fc2ab62704a97f863d01fac3f64

Observation 39597c06-0e70-4d01-9d07-b368ea8226fa · outbound

This paper cites The linear representation hypothesis and the geometry of large language models.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning The linear representation hypothesis and the geometry of large language models

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.370107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:8f299aa47fa41d45f3dcc754306bb8a199884da3e17703dd170ed763bf14fa3d

Observation 83343dee-8a08-4884-bf0a-d32f54f852fd · outbound

This paper cites Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Reference 26

Resolution
verified exact
local_arxiv, observed 2026-07-10T08:16:58.779339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:5b151865719d90a7cbeb2cbc9ea131fef99115ac938d326cfbdd01d048d21c68

Observation 16beef6b-5832-4a96-9f52-d4534bf35362 · outbound

This paper cites Fine-tuning enhances existing mechanisms: A case study on entity tracking.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Fine-tuning enhances existing mechanisms: A case study on entity tracking

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.374083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:e1f1297686d87db88c56d0140e48a3305dadecc6f88307c004f6417c7ff443d2

Observation 411fbf06-46bd-433e-b510-574d87d26c55 · outbound

This paper cites Fine tuning vs.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Fine tuning vs

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.377339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:37930d3b8266715e76ba2ef47ef6e15803625b6d6b44c873b5b731da25ad6279

Observation fd9aeb94-0d09-4060-aa1e-3312f206b030 · outbound

This paper cites Grokking of implicit reasoning in transformers: A mechanistic journey to the edge of generalization.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Grokking of implicit reasoning in transformers: A mechanistic journey to the edge of generalization

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.360841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:23c29073427a348526d8e945bd1995ab9bcd5bb39aae7f7981d69baf07fbe06a

Observation b08b8d1d-48da-4d85-be31-711c1973ec25 · outbound

This paper cites Interpretability in the wild: a circuit for indirect object identification in GPT-2 small.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Interpretability in the wild: a circuit for indirect object identification in GPT-2 small

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.364504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:95ce0fee0618adba38547abd35a3fe1159075a52c3b7c4df8b99aa72ec38e753

Observation 592092ef-e7b6-4bbb-aeba-64c1e2568c39 · outbound

This paper cites doi: 10.18653/v1/2024.acl-long.820.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning doi: 10.18653/v1/2024.acl-long.820

Reference 31

Resolution
metadata mismatch
doi, observed 2026-07-10T08:16:58.528926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:2952fcb67fa905b92951c7a378e16a3df24b3711f1e5384546770c9ec159240d

Observation 338ffd4c-10eb-4fd2-9082-5d73c3db0274 · outbound

This paper cites Ioannidis, Karthik Subbian, James Y.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Ioannidis, Karthik Subbian, James Y

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.356928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:4c2a0250190169b1ab352944cf04c6525bc6e186692034aeef3fef1c622f535a

Observation 00a225be-897a-462d-baaf-9213f07ba4d2 · outbound

This paper cites Knowledge circuits in pretrained transformers.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Knowledge circuits in pretrained transformers

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.358871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:899a7470c6bc29d0afde53de0c63a328935ad109ef0a1b8c66343e64ca9bf1c9

Observation d0ccca99-2165-4800-9a68-a6c009df59fb · outbound

This paper cites Cake: Circuit-aware editing enables generalizable knowledge learners.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Cake: Circuit-aware editing enables generalizable knowledge learners

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-07-10T08:16:58.782324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:03f4b01c891229062a6a9aac6efcedd498f13c5af67a7a3ef4d3f68f144f7ae9

Observation 5fc7136f-9f42-45b7-bc50-037e2d72611d · outbound

This paper cites Towards best practices of activation patching in language models: Metrics and methods.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Towards best practices of activation patching in language models: Metrics and methods

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.353133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:73ecda6d1a5301c834fbda1b467abde9a210dc742330a33a4675b923e65d18f6

Observation 4169bc55-a310-4e20-9b64-9bfca67edadb · outbound

This paper cites Mquake: Assessing knowledge editing in language models via multi-hop questions.

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning Mquake: Assessing knowledge editing in language models via multi-hop questions

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-07-10T08:16:59.354999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-10T08:11:11.566370Z digest=sha256:13d8aba9aeeba2b82c60c1b606e7ea3983d6a7f5aacc6b7a8a955044a3a62cfb

Pith citing papers

No inbound Pith citation observations are available.