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Paper Citation Record · LEDGER

Memory Layers at Scale

As of 21 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 17 inbound Pith citation observations for arXiv:2412.09764.

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

pith.paper-citation-record.v1
2412.09764 v2

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T16:49:54.751347Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:56:40.709328Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T12:46:14.687769Z

Reference resolution

45 of 45 outbound references displayed

  • verified exact0
  • verified fuzzy1
  • unresolved44
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2a3eb023-b44c-4ea8-839d-72aa1e3cb487 · outbound

This paper cites write newline.

Memory Layers at Scale write newline

Reference 1

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T16:49:54.594958Z digest=sha256:ffabe5f915e24c2d182a6bc76a14c7975c88ca9fc7b693ff142cb317de618dea

Observation adb2c361-1cee-46c2-84bb-42e64ea97fee · outbound

This paper cites Program Synthesis with Large Language Models.

Memory Layers at Scale Program Synthesis with Large Language Models

Reference 2

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-11T16:49:54.600190Z digest=sha256:a2cce2cad1a2ea5f005d0a1946e938fcdd48ad779ac90ce86176ef4b3ce19aad

Observation 531483af-c6ab-4ac0-bc5c-08d50709253e · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

Memory Layers at Scale Neural Machine Translation by Jointly Learning to Align and Translate

Reference 3

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source=arxiv_source observed=2026-08-11T16:49:54.604151Z digest=sha256:07cacdb9e81b443c3a086619ec92aadbcdeb0923bda37f64d597300f057b20be

Observation 6f894b6b-7aec-4bef-a0d8-59f027e1ba25 · outbound

This paper cites PIQA: Reasoning about Physical Commonsense in Natural Language.

Memory Layers at Scale PIQA: Reasoning about Physical Commonsense in Natural Language

Reference 4

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source=arxiv_source observed=2026-08-11T16:49:54.607753Z digest=sha256:fc2e0982f5c442356442c78fd74a306c10dd25cdfb052e94fdfd2bbc748945ff

Observation ef976e39-9b65-4a6f-b903-32fc1305a19b · outbound

This paper cites Language Models are Few-Shot Learners.

Memory Layers at Scale Language Models are Few-Shot Learners

Reference 5

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source=arxiv_source observed=2026-08-11T16:49:54.611395Z digest=sha256:30d2499b05b89e8f9ed642466d4d3b3f74a0b77705a12c7add1bd5585963d73a

Observation ff8d2448-06c4-417a-9142-b143ecf06598 · outbound

This paper cites Open-domain question answering.

Memory Layers at Scale Open-domain question answering

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T16:49:54.614983Z digest=sha256:a85f9fb610b090e30187a57ef57bdc9c241ad5be204c681efb6bc6734172f8f7

Observation 716ea335-e8c3-496d-a0f1-118580501f6e · outbound

This paper cites Reading Wikipedia to Answer Open-Domain Questions.

Memory Layers at Scale Reading Wikipedia to Answer Open-Domain Questions

Reference 7

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source=arxiv_source observed=2026-08-11T16:49:54.618492Z digest=sha256:52eadd24751dd23e079a7be64749f389a3aaf33cdee129532bfb9222f1f515b4

Observation 05d21a5b-c452-47ac-9051-80ff075bcade · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Memory Layers at Scale Evaluating Large Language Models Trained on Code

Reference 8

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source=arxiv_source observed=2026-08-11T16:49:54.622329Z digest=sha256:e2c73cc8856836b288115b7e7a769af048144d17ad22a705dcd166caf97ca729

Observation fd0cf9bb-c2b0-44ea-97a0-007c3717d962 · outbound

This paper cites The Llama 3 Herd of Models.

Memory Layers at Scale The Llama 3 Herd of Models

Reference 9

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source=arxiv_source observed=2026-08-11T16:49:54.625959Z digest=sha256:eefe586ebcfe2b32aff32dacfaf8fab85e5eae33050f8ab7ac405b3d8e70b654

Observation e1d98f02-8e13-4095-bf85-d3badd030940 · outbound

This paper cites an unresolved cited work.

Memory Layers at Scale Unresolved cited work

Reference 10

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source=arxiv_source observed=2026-08-11T16:49:54.629445Z digest=sha256:5a51857b9724da22fbb8b67a9da2e9ada7971cad8d4abaf041bf97dcdf34927c

Observation 4153e1bc-ef37-4348-941f-636661813dc1 · outbound

This paper cites Neural Turing Machines.

Memory Layers at Scale Neural Turing Machines

Reference 11

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source=arxiv_source observed=2026-08-11T16:49:54.632638Z digest=sha256:2a1e65311b7abdb288b071b6dd8a41191d5af76f76bf889bb69a6374cc307127

Observation dd5a8adf-2b63-4e33-86d9-ee7543109bcd · outbound

This paper cites Hybrid computing using a neural network with dynamic external memory.

Memory Layers at Scale Hybrid computing using a neural network with dynamic external memory

Reference 12

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source=arxiv_source observed=2026-08-11T16:49:54.636324Z digest=sha256:82056f878b2f2058ef3cfee7b99a74446853d48f20e5ad101ebb81040287a16e

Observation 473ea390-6396-4a0d-b0b5-2137acf16cbb · outbound

This paper cites REALM: Retrieval-Augmented Language Model Pre-Training.

Memory Layers at Scale REALM: Retrieval-Augmented Language Model Pre-Training

Reference 13

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-11T16:49:54.639355Z digest=sha256:2b0ffc8f9ff4e0c57f4d1f70a547b971d525f33f89f2c845833fb5af97aac479

Observation 382e92d6-fb07-4aed-aa86-e77381be2aed · outbound

This paper cites Mixture of A Million Experts.

Memory Layers at Scale Mixture of A Million Experts

Reference 14

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source=arxiv_source observed=2026-08-11T16:49:54.642697Z digest=sha256:44dad7132a137febd8173a4fcff99cb97565c55244c9d2781e411d008a498791

Observation 4142308d-3601-4bb1-96f9-a6c9f19c74aa · outbound

This paper cites Gaussian Error Linear Units (GELUs).

Memory Layers at Scale Gaussian Error Linear Units (GELUs)

Reference 15

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T16:49:54.646237Z digest=sha256:d7b01e73730e3e853f6a7bd8f22b0c6e768562606e45516b13d1b54541e0d059

Observation 9b96f134-7aca-4b63-b0b8-d9f18e6b7707 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Memory Layers at Scale Measuring Massive Multitask Language Understanding

Reference 16

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source=arxiv_source observed=2026-08-11T16:49:54.649976Z digest=sha256:19cb71dc788843c814c00f9c7c6a0be26b84f4256eb55d40aeb41f2187f835d0

Observation 5e45159d-77ea-4ccc-b173-ac1a95a66816 · outbound

This paper cites Multilayer feedforward networks are universal approximators.

Memory Layers at Scale Multilayer feedforward networks are universal approximators

Reference 17

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source=arxiv_source observed=2026-08-11T16:49:54.653457Z digest=sha256:a33a187e3474c9db932cba0d5dc56e6d80387cb88b19e16b3136d5cbe69d908f

Observation cddb954d-22e7-4b22-9fb9-94433cd9669f · outbound

This paper cites Survey of hallucination in natural language generation.

Memory Layers at Scale Survey of hallucination in natural language generation

Reference 18

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source=arxiv_source observed=2026-08-11T16:49:54.656774Z digest=sha256:3cc165d0daed9ed277931e8ff0d5123e17bf92405734a8d1ad21847ec756010c

Observation 0d5231ed-2257-4e6a-a1e5-b3369b8bc69f · outbound

This paper cites Mixtral of Experts.

Memory Layers at Scale Mixtral of Experts

Reference 19

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source=arxiv_source observed=2026-08-11T16:49:54.660107Z digest=sha256:8601c01bc83b9c7ba054a3d24339824c616b514bbe545c15d6198f3708bd860b

Observation 40eff1cf-cfec-40d6-8a16-7d24ffe33938 · outbound

This paper cites Billion-scale similarity search with GPUs.

Memory Layers at Scale Billion-scale similarity search with GPUs

Reference 20

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source=arxiv_source observed=2026-08-11T16:49:54.663630Z digest=sha256:d5c296b0e699987e32a0615b4472fe7c10a37feeecbf5c15315daa39ab746370

Observation fee48aaf-a228-4a69-ba43-e1c03cfa5b7b · outbound

This paper cites TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension.

Memory Layers at Scale TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Reference 21

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source=arxiv_source observed=2026-08-11T16:49:54.666708Z digest=sha256:23f74b33b093e83735acb33099897ac106a34bf7760fb71705ee43f873f1b39c

Observation 838feed6-81d5-4ad9-ae13-f57b6b604677 · outbound

This paper cites Scaling Laws for Neural Language Models.

Memory Layers at Scale Scaling Laws for Neural Language Models

Reference 22

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source=arxiv_source observed=2026-08-11T16:49:54.670325Z digest=sha256:58db381a7962fe8ee9520ed9d2ea8685351e554ca25f54c1cd00dae8a8bbaa8e

Observation ef02867a-441f-4790-8768-f7f68676f479 · outbound

This paper cites Dense Passage Retrieval for Open-Domain Question Answering.

Memory Layers at Scale Dense Passage Retrieval for Open-Domain Question Answering

Reference 23

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source=arxiv_source observed=2026-08-11T16:49:54.673846Z digest=sha256:f439b21b8b284121985cd2e0cf3e2f48cb907e30b9f580b209826dd3a0d1bf9e

Observation 57dff3b2-b629-4b5b-9fc8-511538dd3c37 · outbound

This paper cites Generalization through Memorization: Nearest Neighbor Language Models.

Memory Layers at Scale Generalization through Memorization: Nearest Neighbor Language Models

Reference 24

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source=arxiv_source observed=2026-08-11T16:49:54.677434Z digest=sha256:ff48e5e385b4bc0842d8c184e126fa6d39f7ae9fa04967fbe320e004aa543ad9

Observation 2a444b20-fdcc-4bfb-a030-afca5a9c67eb · outbound

This paper cites Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov.

Memory Layers at Scale Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov

Reference 25

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source=arxiv_source observed=2026-08-11T16:49:54.681198Z digest=sha256:6cc3c634b50632ac3da350ffa20509b6816b91e0c06d2d7c553f601c8c733636

Observation b2a2080b-3db1-4d6c-bf29-cf5102d0d924 · outbound

This paper cites Large Memory Layers with Product Keys.

Memory Layers at Scale Large Memory Layers with Product Keys

Reference 26

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source=arxiv_source observed=2026-08-11T16:49:54.684836Z digest=sha256:efdf7edc5d98e09e1f95f01e2ab392b09141d4ee0fd0fb2828543f1b3a56f548

Observation abf90d65-4fa1-4ed8-836f-246d9fb1ba02 · outbound

This paper cites Latent Retrieval for Weakly Supervised Open Domain Question Answering.

Memory Layers at Scale Latent Retrieval for Weakly Supervised Open Domain Question Answering

Reference 27

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source=arxiv_source observed=2026-08-11T16:49:54.688409Z digest=sha256:956a432b1426d5a1c1cf7cef38c1ffb73021d3817ce8446557a4ec2cec0890e4

Observation 9e1f3eb4-51fa-4685-881a-dbab67a7d417 · outbound

This paper cites GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding.

Memory Layers at Scale GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 28

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source=arxiv_source observed=2026-08-11T16:49:54.691948Z digest=sha256:875dacd47d866d4f9292d045a7679db7e47c0a6b6c119db54f5484b9215efbc4

Observation 25319b52-3a50-416a-9e70-5db663f6652b · outbound

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

Memory Layers at Scale Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Reference 29

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source=arxiv_source observed=2026-08-11T16:49:54.695388Z digest=sha256:9b32ae4c5a18f76b7f56d481a89a67779b78aa06a6a24b52fbeac6593bc47148

Observation d1e7de66-343d-4cf5-959a-b01ee5d861b9 · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

Memory Layers at Scale Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 30

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source=arxiv_source observed=2026-08-11T16:49:54.699004Z digest=sha256:70c7e7c8223ef85b4fd26ec2580c11c46b440b5515b697ac19bf165259a97cbf

Observation 0e642f76-bb90-442a-81e5-77877a654a32 · outbound

This paper cites GPT-4 Technical Report.

Memory Layers at Scale GPT-4 Technical Report

Reference 31

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source=arxiv_source observed=2026-08-11T16:49:54.702690Z digest=sha256:0b2b42908d36012f43cb88aa3fbd9d09f2f119bbd4657ae2bc3148df78a10ba1

Observation 04da0a76-7c8d-47bf-b61e-4fa9646afae3 · outbound

This paper cites KILT: a Benchmark for Knowledge Intensive Language Tasks.

Memory Layers at Scale KILT: a Benchmark for Knowledge Intensive Language Tasks

Reference 32

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source=arxiv_source observed=2026-08-11T16:49:54.705977Z digest=sha256:e755059d37be2437b39ed165ffd6255ecc67d5d85c4753868a87144c39b77043

Observation 31b5175b-9755-43fd-9c53-8285637b6543 · outbound

This paper cites How Much Knowledge Can You Pack Into the Parameters of a Language Model?.

Memory Layers at Scale How Much Knowledge Can You Pack Into the Parameters of a Language Model?

Reference 33

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source=arxiv_source observed=2026-08-11T16:49:54.709459Z digest=sha256:6aee7eec8e2b81f4da118516684a29c7c112fe54c37fd4ed6d8322f81ec5aacd

Observation fd6864ec-c709-4487-b89e-36acd8a66268 · outbound

This paper cites Are Emergent Abilities of Large Language Models a Mirage?.

Memory Layers at Scale Are Emergent Abilities of Large Language Models a Mirage?

Reference 34

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source=arxiv_source observed=2026-08-11T16:49:54.712976Z digest=sha256:b5ad71ee2a6615e077d15bfb00bfb18d69247cfdfdd18f1de1910fc15d783cc1

Observation c39b6222-6b8c-4867-9ac9-cac6746dbeed · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Memory Layers at Scale Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 35

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source=arxiv_source observed=2026-08-11T16:49:54.716493Z digest=sha256:c50c7167f7d364902a97998736efaceddb223d96e2bed40aca9503895581b291

Observation 7194aed6-5dff-41a8-ab08-e1137e391585 · outbound

This paper cites End-to-end memory networks.

Memory Layers at Scale End-to-end memory networks

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-11T16:49:55.293520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-11T16:49:54.719987Z digest=sha256:f905dd5d3698673a41f2db6205f3f3d2007da4399f4b36c6ce0db0d4635fd591

Observation 7f255909-86f5-4813-86c5-4c36626633d7 · outbound

This paper cites Chameleon: Mixed-Modal Early-Fusion Foundation Models.

Memory Layers at Scale Chameleon: Mixed-Modal Early-Fusion Foundation Models

Reference 37

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source=arxiv_source observed=2026-08-11T16:49:54.723262Z digest=sha256:79113784f53603878c57a9faff10954aa387b73ef3342dcb8e61fa79bae7452c

Observation 450455dd-06ea-42d7-b445-2668551d71c7 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

Memory Layers at Scale Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 38

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source=arxiv_source observed=2026-08-11T16:49:54.726687Z digest=sha256:d77cfea7cc1a6f8b2ffec9fa44139ab9cdc8f9b04041195a2ac1af6674111eba

Observation c7bfb580-4a97-4fa3-8338-96f42c6394e0 · outbound

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

Memory Layers at Scale Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 39

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source=arxiv_source observed=2026-08-11T16:49:54.730072Z digest=sha256:699a435c54cd43dc2b76748dd10482c6c27123b03818e03f80a71051d75ef663

Observation 5bdaf2f9-beba-4e2a-9a8f-8d63af3d1f34 · outbound

This paper cites Attention Is All You Need.

Memory Layers at Scale Attention Is All You Need

Reference 40

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source=arxiv_source observed=2026-08-11T16:49:54.733591Z digest=sha256:76bb6416674fa7b8fcfc9ba1161fa40778e2f78b98a23224d6317feec23383d0

Observation 006e381f-f000-4a32-9e75-5bba70abe133 · outbound

This paper cites Emergent Abilities of Large Language Models.

Memory Layers at Scale Emergent Abilities of Large Language Models

Reference 41

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T16:49:54.736619Z digest=sha256:a6c299e64db803b7f4500cdb36b8659f77592b5e720c50fb969c25e7a99558d8

Observation 27f4ddd2-8ac4-4565-b6c2-c89b9fd0162d · outbound

This paper cites Memory Networks.

Memory Layers at Scale Memory Networks

Reference 42

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source=arxiv_source observed=2026-08-11T16:49:54.740859Z digest=sha256:67992b90f2a7b4b974acbec44fb333aea3a3750eca3e622051fc97996f28a998

Observation 01bc4761-4976-4d5d-b81f-8185e058769b · outbound

This paper cites HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering.

Memory Layers at Scale HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Reference 43

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source=arxiv_source observed=2026-08-11T16:49:54.744339Z digest=sha256:cf21ccc8657a25b153c142793b0addbd5fb0e1702e82e554eaeb23c3ebcf73df

Observation dca1bcd5-084a-422b-8deb-dfd7dd628647 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Memory Layers at Scale HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 44

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source=arxiv_source observed=2026-08-11T16:49:54.747832Z digest=sha256:dfefe36443ee8333cc60d967f1e27614609a111dc9e4f2227b1a41c9d8b0ff4b

Observation 629cc6e1-a39d-47ef-ac4a-78f4a3327ced · outbound

This paper cites Mixture-of-Experts with Expert Choice Routing.

Memory Layers at Scale Mixture-of-Experts with Expert Choice Routing

Reference 45

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no resolver link, observed 2026-08-11T16:49:54.751347Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-11T16:49:54.751347Z digest=sha256:5c7259bb7cd731c19cd6eb7b677e3e5700a68d9bb987fd09f6dd45521bcfaade

Pith citing papers

Observation 09a8d0a3-aef7-470e-8556-69f5a416f31b · inbound

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing cites this paper.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Memory Layers at Scale

Reference 4

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no resolver link, observed 2026-08-11T12:03:42.171065Z

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source=arxiv_source observed=2026-08-11T12:03:42.171065Z digest=sha256:95de7f740f2af773dc4fe014269dee5c6dde945cd5a4a325bf370ac75f15eddd

Observation 17da46ac-9091-454c-be6d-c9f559710779 · inbound

Titans: Learning to Memorize at Test Time cites this paper.

Titans: Learning to Memorize at Test Time Memory Layers at Scale

Reference 10

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verified exact
arxiv_id, observed 2026-05-14T22:08:15.372893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-14T22:08:14.982302Z digest=sha256:ba3fbd33f479341b72ebea8be3e26c798bc8945be329ee7417f99cb8f70fad87

Observation 24b3b3aa-0530-47d8-bd9b-f803dedc0a43 · inbound

Position: Episodic Memory is the Missing Piece for Long-Term LLM Agents cites this paper.

Position: Episodic Memory is the Missing Piece for Long-Term LLM Agents Memory Layers at Scale

Reference 7

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no resolver link, observed 2026-08-08T14:13:37.950838Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-08T14:13:37.950838Z digest=sha256:398bc848b275a14ef536e6317b43717273deac6f1c38d78982001001f4b777e7

Observation bb5c5be0-96ea-4c15-9f30-786d5888cdaf · inbound

From Bytes to Ideas: Language Modeling with Autoregressive U-Nets cites this paper.

From Bytes to Ideas: Language Modeling with Autoregressive U-Nets Memory Layers at Scale

Reference 8

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no resolver link, observed 2026-08-15T19:56:40.709328Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T19:56:40.709328Z digest=sha256:ddc631364d2127405e72ccd78eb6b8b44b9547fccee4bd54ce78501e9f786c70

Observation 4b3655e1-a9bd-4695-b72f-48ff463a6ddf · inbound

Learning Facts at Scale with Active Reading cites this paper.

Learning Facts at Scale with Active Reading Memory Layers at Scale

Reference 2019

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no resolver link, observed 2026-08-05T21:05:45.188392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T21:05:45.188392Z digest=sha256:1b5336d1109a49028ec6242ae6b22fd00c26c4cc87da9233dfb7b5b6539dd4e5

Observation e140f4af-107e-4240-a8c5-f9d63a2a6042 · 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 Memory Layers at Scale

Reference 9

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no resolver link, observed 2026-08-05T20:15:47.770663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T20:15:47.770663Z digest=sha256:1f19f8db48c0c4659ac6f5af884a38b086327dd3ccf4f4031b40546473f7f851

Observation bf4eed67-8daf-4c43-8846-cac6c833c7be · inbound

UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning cites this paper.

UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning Memory Layers at Scale

Reference 2

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no resolver link, observed 2026-08-05T16:17:41.871650Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-05T16:17:41.871650Z digest=sha256:dc6fee6107ed0b98541e2a918fcf3a3bbbede2acac6704bd006b97529fb5120c

Observation 0e8e3380-490d-4494-bb96-015b907499f7 · inbound

Back to Basics: Let Conversational Agents Remember with Just Retrieval and Generation cites this paper.

Back to Basics: Let Conversational Agents Remember with Just Retrieval and Generation Memory Layers at Scale

Reference 1

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verified exact
arxiv_id, observed 2026-05-11T10:31:03.674020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-10T15:27:00.693277Z digest=sha256:036888a484eae02de222ef985f39f1180ae7bbbdf6e58c310674928d069490df

Observation 3bce37f9-ac6e-4752-afbf-cf9cecc7a6b9 · inbound

A Parametric Memory Head for Continual Generative Retrieval cites this paper.

A Parametric Memory Head for Continual Generative Retrieval Memory Layers at Scale

Reference 1

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verified exact
arxiv_id, observed 2026-05-11T21:01:13.450367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-08T07:17:36.002595Z digest=sha256:e5e93156fc010d8581ddcad06658f819e2cb664f9830b0cada74601b3f71d3b4

Observation 1f5e1ca7-395f-44b7-b4f8-4de4a0699aa3 · inbound

Sparse Memory Finetuning as a Low-Forgetting Alternative to LoRA and Full Finetuning cites this paper.

Sparse Memory Finetuning as a Low-Forgetting Alternative to LoRA and Full Finetuning Memory Layers at Scale

Reference 1

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verified exact
arxiv_id, observed 2026-07-01T00:55:12.744308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-30T23:53:13.453684Z digest=sha256:af13a54082ef610293abc43d8626c6efa72b8925236bed7534bee355bec6d511

Observation 2261a4d7-4e28-430e-8f14-a2dcd9a761d2 · 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 Memory Layers at Scale

Reference 23

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verified exact
arxiv_id, observed 2026-07-04T01:09:18.556609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-26T20:43:55.740662Z digest=sha256:dc740b95a7eeaf9055f5807aa3e0dcdedad250b0748719700a5b52c5bb05cc2e

Observation aee20ed8-a835-4d64-bd38-9ce99da4be54 · inbound

MMLA: How Memory Lets the Past Shape the Future cites this paper.

MMLA: How Memory Lets the Past Shape the Future Memory Layers at Scale

Reference 31

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verified exact
arxiv_id, observed 2026-06-30T09:54:35.289983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-30T09:46:57.030469Z digest=sha256:7e6632ab53625d048a0b74fda4eabc663386f7008b2cfe65eefcd8cd492edfb2

Observation d0c6d221-4324-45ef-b6ea-af2e7b5f1a1c · inbound

MMLA: How Memory Lets the Past Shape the Future cites this paper.

MMLA: How Memory Lets the Past Shape the Future Memory Layers at Scale

Reference 31

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no resolver link, observed 2026-07-13T07:16:45.194991Z

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source=pdf_text observed=2026-07-13T07:16:45.194991Z digest=sha256:41f124d0c3904237b8c3ad5dce44f9dbac70b9f9539f36acd35766bc668e9c38

Observation db58455a-e42c-47a6-ab81-a8cb8c86c722 · inbound

Sparse Delta Memory: Scaling the State of Linear RNNs through Sparsity cites this paper.

Sparse Delta Memory: Scaling the State of Linear RNNs through Sparsity Memory Layers at Scale

Reference 82

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verified exact
local_arxiv, observed 2026-07-09T12:46:14.688901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-07-09T12:40:09.036905Z digest=sha256:83128f03347f672493470073ddbfa32f7b9ae46a9aa7ccaadd67d9453509853d

Observation af5af27d-ee1a-4a41-9fdf-cae88ddcec9e · inbound

Can a Language Model Learn Facts Continually in Its Weights? cites this paper.

Can a Language Model Learn Facts Continually in Its Weights? Memory Layers at Scale

Reference 3

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no resolver link, observed 2026-07-14T07:36:43.499258Z

Source-reported events for the cited work

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source=pdf_text observed=2026-07-14T07:36:43.499258Z digest=sha256:d299636e01d3b25640e85b63d3094a6214b4966925ed90285ade62a19ed1e811

Observation 2e294914-58df-4a05-bdec-8198211f542f · inbound

More Than Memory: Task-Conditioned Signed FFN Writes in Long-Context Retrieval cites this paper.

More Than Memory: Task-Conditioned Signed FFN Writes in Long-Context Retrieval Memory Layers at Scale

Reference 22

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no resolver link, observed 2026-08-02T09:48:57.754044Z

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source=arxiv_source observed=2026-08-02T09:48:57.754044Z digest=sha256:cd0f1dc8ad7126d565fb1adc2a233531031452c649298aa21ef7d12978ff7015

Observation 434605c4-975c-4aee-81f8-030c3887a850 · inbound

MemSFT: Mitigating Alignment Tax with an External Parametric Memory cites this paper.

MemSFT: Mitigating Alignment Tax with an External Parametric Memory Memory Layers at Scale

Reference 2025

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T01:59:49.773747Z digest=sha256:1e5e2f52b0f2f187e48ba9cfc957443170ed78e6d806de102929c779304bc100