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Paper Citation Record · LEDGER

LM2: Large Memory Models

As of 19 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 7 inbound Pith citation observations for arXiv:2502.06049.

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

pith.paper-citation-record.v1
2502.06049 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:00:45.783574Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:37:13.154209Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T01:09:18.542024Z

Reference resolution

26 of 26 outbound references displayed

  • verified exact1
  • verified fuzzy5
  • unresolved20
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 60a7845a-38a2-46cd-a543-7860c7d18871 · outbound

This paper cites ETC: Encoding Long and Structured Inputs in Transformers.

LM2: Large Memory Models ETC: Encoding Long and Structured Inputs in Transformers

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T17:00:45.665011Z digest=sha256:505235eed2f60fe171d4de1281e561a569c99de23245d7736899aff74a6e7895

Observation 7e7e832e-8bd7-4bda-903d-db46be8414b5 · outbound

This paper cites Longformer: The Long-Document Transformer.

LM2: Large Memory Models Longformer: The Long-Document Transformer

Reference 2

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source=arxiv_source observed=2026-08-08T17:00:45.670536Z digest=sha256:c8a838200829daeea6e52d7876645f4b3b9284860ac890e59362b5de18cff410

Observation 32ba0d0c-930f-4bc3-beba-535bda3b22d8 · outbound

This paper cites Language models can explain neurons in language models.

LM2: Large Memory Models Language models can explain neurons in language models

Reference 3

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source=arxiv_source observed=2026-08-08T17:00:45.675619Z digest=sha256:b189d795fb8ff0895edf2db3dae95b6e8fd1f3995fc9b777fb71e30c71ca3b49

Observation 02446a26-8d76-401a-b38f-24e0dfd3dd5d · outbound

This paper cites Language models are few-shot learners.

LM2: Large Memory Models Language models are few-shot learners

Reference 4

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source=arxiv_source observed=2026-08-08T17:00:45.681170Z digest=sha256:a7e1dd7c115473ae10faa64be9955f8c5134cc0a0144d7d65544d42be84abe12

Observation 9af3750d-7387-4f44-9a9d-fd39168885f0 · outbound

This paper cites Recurrent Memory Transformer.

LM2: Large Memory Models Recurrent Memory Transformer

Reference 5

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-08T17:00:45.686336Z digest=sha256:549f9aca93d3337181633c5ca5b91de5104c2a88fb33691e779345c5271d10e3

Observation 562cdcf8-9692-42f3-908c-f23154757651 · outbound

This paper cites Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context.

LM2: Large Memory Models Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

Reference 6

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source=arxiv_source observed=2026-08-08T17:00:45.691686Z digest=sha256:d5a4a6aa4b7ae4820393abfaeb16d61853f4e548abb9737c25da30e1d40d3d07

Observation 1ef32d56-9471-483b-880b-18f1c51a1f81 · outbound

This paper cites The archival advantage: Integrating archival expertise into management of born-digital library materials.

LM2: Large Memory Models The archival advantage: Integrating archival expertise into management of born-digital library materials

Reference 7

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verified fuzzy
raw_fallback, observed 2026-08-08T17:00:46.173676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T17:00:45.697822Z digest=sha256:8be831fd204b6e53d27299f4f58de2919fec2011fef548363e1ad77dbe9f59a2

Observation df4a5247-c674-4c22-850c-53fa5db1009a · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

LM2: Large Memory Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

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source=arxiv_source observed=2026-08-08T17:00:45.702483Z digest=sha256:efd2fffdd57aa77f3ea0286df2fd4f46683b297b142b96920480bb1e9651af04

Observation 11de1167-9f8c-4528-b107-4a9867bcb3c9 · outbound

This paper cites The Llama 3 Herd of Models.

LM2: Large Memory Models The Llama 3 Herd of Models

Reference 9

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source=arxiv_source observed=2026-08-08T17:00:45.707195Z digest=sha256:12dc3b42df3d31903f94cf612fccfca1b4149ae03d33c57dcb45c243f07a26eb

Observation 3acc36f4-0523-4b6a-90b6-eba13a914cc1 · outbound

This paper cites GMAT: Global Memory Augmentation for Transformers.

LM2: Large Memory Models GMAT: Global Memory Augmentation for Transformers

Reference 10

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no resolver link, observed 2026-08-08T17:00:45.711810Z

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source=arxiv_source observed=2026-08-08T17:00:45.711810Z digest=sha256:d72a4fe01df3fadd40c0b9da374c6905a0d2d1198760fceb97057c5c0decaa4a

Observation 5e90ded1-5cc2-4389-9447-48a190c07979 · outbound

This paper cites Measuring massive multitask language understanding.

LM2: Large Memory Models Measuring massive multitask language understanding

Reference 11

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source=arxiv_source observed=2026-08-08T17:00:45.716826Z digest=sha256:2463f055d77d617994586e071409bc5aaed75d794e8d01638be6104797301e3a

Observation f9734e81-29d3-458a-ba2f-1c8c39445f29 · outbound

This paper cites Think before you act: Decision transformers with working memory.

LM2: Large Memory Models Think before you act: Decision transformers with working memory

Reference 12

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T17:00:45.721381Z digest=sha256:22b9a5cbac7aea38f6a35d74fa60170ee2266d83fddebf611fb487bbb4644760

Observation e49ddde8-e6ba-4e7b-880e-ffe9dbdcf6b8 · outbound

This paper cites Scaling Laws for Neural Language Models.

LM2: Large Memory Models Scaling Laws for Neural Language Models

Reference 13

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-08T17:00:45.725882Z digest=sha256:8e4aa61468da29040cf9d7c499286b844e4758077fe3ac528e935042f7b604b9

Observation a268cb15-0d17-4f50-846d-579248a41186 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

LM2: Large Memory Models Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 14

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T17:00:45.730545Z digest=sha256:4c1978059fdb02bfedb641c57d056f5e3ff1bbf60706d00a8e88e8093369a7f8

Observation c14fec7c-3201-451c-a584-7b1dab882a8a · outbound

This paper cites Memreasoner: A memory-augmented llm architecture for multi-hop reasoning.

LM2: Large Memory Models Memreasoner: A memory-augmented llm architecture for multi-hop reasoning

Reference 15

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verified fuzzy
raw_fallback, observed 2026-08-08T17:00:46.122977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T17:00:45.735758Z digest=sha256:7bf3ce8a5aa6530963e1968cf70e5ead41db29a88daf9208374be829a030258a

Observation b2cdafe4-91bc-4dfe-9d0e-a139f63ab41b · outbound

This paper cites Babilong: Testing the limits of llms with long context reasoning-in-a-haystack, 2024.

LM2: Large Memory Models Babilong: Testing the limits of llms with long context reasoning-in-a-haystack, 2024

Reference 16

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source=arxiv_source observed=2026-08-08T17:00:45.740161Z digest=sha256:d6ff575d2ec10a6eaa8116eb34af655ea5eb7c903536633881a01ce0aebed0b6

Observation 206010f1-b6d4-4e83-a089-7e712b7a2c0b · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

LM2: Large Memory Models Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Reference 17

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source=arxiv_source observed=2026-08-08T17:00:45.744784Z digest=sha256:21bcc336f8f554f5742f38618edf512ef6247ea39e7b06914f18e6766ce8948a

Observation afbe3f2b-0b33-4cc6-b9cd-ca560b1618db · outbound

This paper cites LaFFi: Leveraging Hybrid Natural Language Feedback for Fine-tuning Language Models.

LM2: Large Memory Models LaFFi: Leveraging Hybrid Natural Language Feedback for Fine-tuning Language Models

Reference 18

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local_arxiv, observed 2026-08-08T17:00:45.921642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T17:00:45.749654Z digest=sha256:050796e27ea38a74df400d2eb79987d10802a5dd916a04fd5f1b021299bc5f51

Observation 926cb661-4232-44e3-ad20-566ea51d4dec · outbound

This paper cites Hierarchical transformers for multi-document summarization.

LM2: Large Memory Models Hierarchical transformers for multi-document summarization

Reference 19

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Observation 9c26983c-0152-4adf-82df-063a3ca9b9b4 · outbound

This paper cites Small language models: Efficient, accessible, and effective.

LM2: Large Memory Models Small language models: Efficient, accessible, and effective

Reference 20

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verified fuzzy
raw_fallback, observed 2026-08-08T17:00:46.097691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T17:00:45.759006Z digest=sha256:214fa7ec549f96864b8c319aba8d77ea752e792d827328c437d43c56d8319b00

Observation c635c2b4-d011-4dd6-bd8e-b5e66f8e0c7a · outbound

This paper cites Multi-hop Question Answering.

LM2: Large Memory Models Multi-hop Question Answering

Reference 21

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no resolver link, observed 2026-08-08T17:00:45.762827Z

Source-reported events for the cited work

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Observation 10ab0423-231b-4e81-98fc-db9a402effca · outbound

This paper cites The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale.

LM2: Large Memory Models The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale

Reference 22

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Observation 74be2733-a4b8-470b-9f6b-7cc82188614d · outbound

This paper cites Associative Recurrent Memory Transformer.

LM2: Large Memory Models Associative Recurrent Memory Transformer

Reference 23

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source=arxiv_source observed=2026-08-08T17:00:45.770973Z digest=sha256:94887e2dd0d0c211b73de96d2087c24e86cf32be5f870abec9f1c13a78a343bb

Observation 265333a0-1328-4ef3-8ed9-5626ada52c5b · outbound

This paper cites Towards ai-complete question answering: A set of prerequisite toy tasks.

LM2: Large Memory Models Towards ai-complete question answering: A set of prerequisite toy tasks

Reference 24

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raw_fallback, observed 2026-08-08T17:00:46.080964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T17:00:45.775018Z digest=sha256:0b9e5dbf7b5eb918dd2ab53137c777362bb1e0b4e8aae53a192d31796242ef33

Observation 96674689-74b0-4731-bd5e-524644f3de67 · outbound

This paper cites Big Bird: Transformers for Longer Sequences.

LM2: Large Memory Models Big Bird: Transformers for Longer Sequences

Reference 25

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source=arxiv_source observed=2026-08-08T17:00:45.778863Z digest=sha256:233d06e9e84f8dbb53a771c5ebfd76dcbbf569f287c71d5056fadfd7eeb3daa8

Observation 9fa22996-549f-48b6-b23d-7aa4bb5a89e4 · outbound

This paper cites Incorporating BERT into Neural Machine Translation.

LM2: Large Memory Models Incorporating BERT into Neural Machine Translation

Reference 26

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source=arxiv_source observed=2026-08-08T17:00:45.783574Z digest=sha256:dfe85b204171cd03f2b3cf761fce62395346375a84ac398da21a47d68d69dbe8

Pith citing papers

Observation b2a0f437-f212-4e14-a919-471c3198f109 · inbound

Memory-Augmented Transformers: A Systematic Review from Neuroscience Principles to Enhanced Model Architectures cites this paper.

Memory-Augmented Transformers: A Systematic Review from Neuroscience Principles to Enhanced Model Architectures LM2: Large Memory Models

Reference 42

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no resolver link, observed 2026-08-05T20:15:50.651550Z

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source=arxiv_source observed=2026-08-05T20:15:50.651550Z digest=sha256:0bc5fbeb8ffa70e8841e64368196a69161224cdedc4172edbfc48db05cc06375

Observation be97cb97-c86b-4567-9dd2-2dc00d867fbc · inbound

Orchid: Orchestrating Context Across Creative Workflows with Generative AI cites this paper.

Orchid: Orchestrating Context Across Creative Workflows with Generative AI LM2: Large Memory Models

Reference 23

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Observation 804b1712-f544-4aa2-a515-780c4d1ca7e2 · inbound

HAGE: Harnessing Agentic Memory via RL-Driven Weighted Graph Evolution cites this paper.

HAGE: Harnessing Agentic Memory via RL-Driven Weighted Graph Evolution LM2: Large Memory Models

Reference 43

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verified exact
arxiv_id, observed 2026-05-12T06:31:25.151341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-12T04:14:06.843343Z digest=sha256:c2116d5aa2505cc7e9596a578c9734ccd5a62ffe19ff1371bd14cedb55152f44

Observation f3793089-c6fa-4986-818f-0a2659bbe6a4 · inbound

The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes cites this paper.

The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes LM2: Large Memory Models

Reference 112

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T05:57:41.663922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-06-27T12:59:51.091008Z digest=sha256:0da4f36e0f2836c53a1ddf175c87959a5264a18b19c0ad3f3fce5cc12c797098

Observation 2673ab80-4838-497d-ab02-212559529e34 · inbound

Repeated Shared Access Enables Grokking, but Edit Propagation Depends on an Addressable Memory cites this paper.

Repeated Shared Access Enables Grokking, but Edit Propagation Depends on an Addressable Memory LM2: Large Memory Models

Reference 26

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metadata mismatch
arxiv_id, observed 2026-07-04T01:09:18.543507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 6d2fd494-2ae4-4260-8b7e-275b86d13e90 · inbound

Memory for Large Language Models cites this paper.

Memory for Large Language Models LM2: Large Memory Models

Reference 45

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Observation f1277a8b-720f-455f-93c4-adf05d28d50b · inbound

LoKiFormer: Locality-aware Attention with Decoupled Knowledge Memory for Efficient Large Language Model Pretraining cites this paper.

LoKiFormer: Locality-aware Attention with Decoupled Knowledge Memory for Efficient Large Language Model Pretraining LM2: Large Memory Models

Reference 13

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