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Paper Citation Record · LEDGER

Ultra-Sparse Memory Network

As of 17 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 9 inbound Pith citation observations for arXiv:2411.12364.

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

pith.paper-citation-record.v1
2411.12364 v2

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:40:11.880130Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:03:42.481591Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T01:09:19.454543Z

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation 49dd7439-157b-4e5c-a064-76f8c89ed5ba · outbound

This paper cites Singular value decomposition (svd) and generalized singular value decomposition.

Ultra-Sparse Memory Network Singular value decomposition (svd) and generalized singular value decomposition

Reference 1

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source=arxiv_source observed=2026-08-12T17:40:11.713352Z digest=sha256:470334d76db6141abdb785d6b68d5339adca502c320a22bded9b186fa8d97464

Observation 90d13969-5966-4fde-9ac7-57bc76824daa · outbound

This paper cites GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints.

Ultra-Sparse Memory Network GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Reference 2

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source=arxiv_source observed=2026-08-12T17:40:11.718011Z digest=sha256:235c69827c9ed4d9938ac28c5c77dd662d0a75a99d8f13e1ca2cf92a8c77d953

Observation 168cae66-14e8-4f63-a909-ba55dd778b30 · outbound

This paper cites Layer Normalization.

Ultra-Sparse Memory Network Layer Normalization

Reference 3

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source=arxiv_source observed=2026-08-12T17:40:11.722221Z digest=sha256:4482d9495b307f5172ee664eda36ccd7ff48a292220acc6998a294350076f6e2

Observation f6d40994-6891-41eb-9625-ab09f9c52583 · outbound

This paper cites LoTR: Low Tensor Rank Weight Adaptation.

Ultra-Sparse Memory Network LoTR: Low Tensor Rank Weight Adaptation

Reference 4

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source=arxiv_source observed=2026-08-12T17:40:11.725991Z digest=sha256:e20004e4281002edc50b0fb9f62043dccca504ae22038af4cbb8f2df83a9fa42

Observation e02b3f88-803a-4aff-b065-4a1b7452254c · outbound

This paper cites GPT-NeoX-20B: An Open-Source Autoregressive Language Model.

Ultra-Sparse Memory Network GPT-NeoX-20B: An Open-Source Autoregressive Language Model

Reference 5

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source=arxiv_source observed=2026-08-12T17:40:11.730098Z digest=sha256:36ba51e911c5cc1c3e5b421924f0a3b3ef9485ef37f86394886a37498588ca28

Observation f701ef5f-3caf-4fb8-b0bb-0dc73efae480 · outbound

This paper cites Language Models are Few-Shot Learners.

Ultra-Sparse Memory Network Language Models are Few-Shot Learners

Reference 6

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source=arxiv_source observed=2026-08-12T17:40:11.733772Z digest=sha256:acee63f758d2b5eacccbeb2c830381fea2499c8b6b969196dd551efecd1ddb75

Observation e50a5810-8fa1-4aa3-bb7f-1b75b55a83d0 · outbound

This paper cites On the representation collapse of sparse mixture of experts.

Ultra-Sparse Memory Network On the representation collapse of sparse mixture of experts

Reference 7

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source=arxiv_source observed=2026-08-12T17:40:11.738251Z digest=sha256:407b3330ee2e394d18cb61bbd8c0b3fe1532ce08de064299a23c0c003e81c723

Observation 9225d8b9-6342-4e42-bf6d-afab1d4231aa · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

Ultra-Sparse Memory Network BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 8

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source=arxiv_source observed=2026-08-12T17:40:11.741689Z digest=sha256:d0a9c655ac9d0c30efa10285068f2bd7597a08fbf5e19a2edf58e86ef843a61c

Observation dcba7540-6c9f-485d-a8ba-1bab9920f714 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Ultra-Sparse Memory Network Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 9

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source=arxiv_source observed=2026-08-12T17:40:11.745425Z digest=sha256:45261363b6b2478c4f9e4aca7084da937f50281b5267e6189833ad7c00c2b24d

Observation 56d6fdc6-72b9-4807-9a98-95aa69ef0e86 · outbound

This paper cites Redpajama: An open source recipe to reproduce llama training dataset, 2023.

Ultra-Sparse Memory Network Redpajama: An open source recipe to reproduce llama training dataset, 2023

Reference 10

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source=arxiv_source observed=2026-08-12T17:40:11.749017Z digest=sha256:37ec54793875513cec5d0979eff97f07544631609359c70d0e15ff2a55aa3fb7

Observation c6f7d534-4cca-42e0-88f7-859593b37b3e · outbound

This paper cites Approximating Two-Layer Feedforward Networks for Efficient Transformers.

Ultra-Sparse Memory Network Approximating Two-Layer Feedforward Networks for Efficient Transformers

Reference 11

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source=arxiv_source observed=2026-08-12T17:40:11.752516Z digest=sha256:53bcdf5355113770ab1916ab18011a665aa248d63e663f56d83fec32d5a48f10

Observation 793ab649-2840-47a9-8a7f-57020a0cda5c · outbound

This paper cites DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models.

Ultra-Sparse Memory Network DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 12

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source=arxiv_source observed=2026-08-12T17:40:11.756344Z digest=sha256:40ff06fd707279744008c865ab8e3e124c673063a8347f9bf35eb9842edae734

Observation 38f6861d-5427-4df1-8efd-9bfc4aa2b7e4 · outbound

This paper cites DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.

Ultra-Sparse Memory Network DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Reference 13

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source=arxiv_source observed=2026-08-12T17:40:11.759746Z digest=sha256:e68732001dfd1a7c59680ca50f2040eeb9b4ee0876fe25fea2ca090f7022a186

Observation bb380a63-bb88-450b-bef9-61fcf15e1dc0 · outbound

This paper cites The Llama 3 Herd of Models.

Ultra-Sparse Memory Network The Llama 3 Herd of Models

Reference 14

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source=arxiv_source observed=2026-08-12T17:40:11.763763Z digest=sha256:24fbfd988c98b25b2ae3f8307efcf9d0e3d677b0437202c818daba32b8b282ef

Observation 837ad763-237e-40b5-86f9-b6359c37b3cc · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.

Ultra-Sparse Memory Network Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

Reference 15

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source=arxiv_source observed=2026-08-12T17:40:11.767356Z digest=sha256:9a5c2020db134254487a0ea874ded123c94c034b0618c799526c74adb175ba69

Observation 7cae23e5-68a7-4d02-8b71-e25c2c3a5bab · outbound

This paper cites Transformer Feed-Forward Layers Are Key-Value Memories.

Ultra-Sparse Memory Network Transformer Feed-Forward Layers Are Key-Value Memories

Reference 16

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source=arxiv_source observed=2026-08-12T17:40:11.770687Z digest=sha256:3ca153eb274adb054b3eec987077daf0478c460650e8d843326112725823125b

Observation 38ad07e4-8279-44d0-83b1-d549a73128b0 · outbound

This paper cites Mixture of A Million Experts.

Ultra-Sparse Memory Network Mixture of A Million Experts

Reference 17

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source=arxiv_source observed=2026-08-12T17:40:11.774516Z digest=sha256:d6e84881831447a3b27eb180e46ecae6fe7505a47842a8955040e50c34e0cced

Observation 01cad70a-52e7-4f86-b8b2-86e9405c7870 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Ultra-Sparse Memory Network Measuring Massive Multitask Language Understanding

Reference 18

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source=arxiv_source observed=2026-08-12T17:40:11.778396Z digest=sha256:ea3d9f9c955b29070a5d2cfbbbc0244cdbc9f069cdbc90ae0ddd3ae05de71cc1

Observation 0bbf8997-344a-4614-988d-05df52dec7fc · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Ultra-Sparse Memory Network MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 19

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source=arxiv_source observed=2026-08-12T17:40:11.782216Z digest=sha256:da69c773bbe8f33fd0ce259ed40f10fd4e0d3305b412094ec51b74df4e5c1b06

Observation c98a8dd7-81f7-456d-b2a3-3491cabf162f · outbound

This paper cites Product quantization for nearest neighbor search.

Ultra-Sparse Memory Network Product quantization for nearest neighbor search

Reference 20

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source=arxiv_source observed=2026-08-12T17:40:11.785609Z digest=sha256:64e2bb88e97ef71c83643ee49418c317bfb7a23281440997e83cdc5063915ec3

Observation cf2fe7cc-3dc0-41af-be00-b9f54fccc2b7 · outbound

This paper cites Mixtral of Experts.

Ultra-Sparse Memory Network Mixtral of Experts

Reference 21

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source=arxiv_source observed=2026-08-12T17:40:11.788900Z digest=sha256:f36bc238bfb243fff493ad2e360350e22f8b31d24e8723a3ef0193294e85e472

Observation f5b6a5c7-2924-4699-b298-f3874bedbd2a · outbound

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

Ultra-Sparse Memory Network TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Reference 22

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source=arxiv_source observed=2026-08-12T17:40:11.792408Z digest=sha256:0f9e5c57c65edec7f1bf174970ebdc848803645b7b82f1c65d22b0f8b40a99ed

Observation eef2b7ce-bc41-4ad5-b63d-54980cf762fe · outbound

This paper cites Large Product Key Memory for Pretrained Language Models.

Ultra-Sparse Memory Network Large Product Key Memory for Pretrained Language Models

Reference 23

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source=arxiv_source observed=2026-08-12T17:40:11.796041Z digest=sha256:c020d0ed5ca281ea1656d48d5f815ef413d65f1ca2e3816d98158d38283c0326

Observation 55220a9d-b5c6-428f-b64d-973a86cb10ba · outbound

This paper cites Scaling Laws for Fine-Grained Mixture of Experts.

Ultra-Sparse Memory Network Scaling Laws for Fine-Grained Mixture of Experts

Reference 24

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source=arxiv_source observed=2026-08-12T17:40:11.799681Z digest=sha256:836038f1f094c602618bb4b78a73a6c0b3daa1bdf1bd5f27e283974881c77ae8

Observation e15c0b11-7d9b-4a29-9f09-9ae6a723a9e6 · outbound

This paper cites Large memory layers with product keys.

Ultra-Sparse Memory Network Large memory layers with product keys

Reference 25

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source=arxiv_source observed=2026-08-12T17:40:11.803785Z digest=sha256:47524935c58de42be198ac18326f21303f89195d8d32dea5d8e4a041a824d3da

Observation 7b815b85-e9ab-4701-9624-96aa1d4dbaf3 · outbound

This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

Ultra-Sparse Memory Network DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 26

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source=arxiv_source observed=2026-08-12T17:40:11.807604Z digest=sha256:0a5c9d2458fbd7aeaeb58f3c63ccbe833efb5afe546305b267374b2680ac2df8

Observation c08f18c0-0d65-408f-a388-7880799ec553 · outbound

This paper cites Low-rank tucker decomposition of large tensors using tensorsketch.

Ultra-Sparse Memory Network Low-rank tucker decomposition of large tensors using tensorsketch

Reference 27

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source=arxiv_source observed=2026-08-12T17:40:11.811038Z digest=sha256:6da86c96a3998a06a3f26e0e10d6c1f7ac3574fcfe95c1543d1f406d1e754c5f

Observation a522919e-9bde-4b9d-a226-9c561f661c50 · outbound

This paper cites Efficient large-scale language model training on gpu clusters using megatron-lm.

Ultra-Sparse Memory Network Efficient large-scale language model training on gpu clusters using megatron-lm

Reference 28

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Observation ceec7ec9-9da3-4730-aa58-35fd6a9fada6 · outbound

This paper cites Language models are unsupervised multitask learners.

Ultra-Sparse Memory Network Language models are unsupervised multitask learners

Reference 29

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Observation fe2f46d1-93fb-4e1b-a590-a890f30b35b8 · outbound

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

Ultra-Sparse Memory Network Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 30

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source=arxiv_source observed=2026-08-12T17:40:11.821670Z digest=sha256:e82024e7d82205e714ecf6739b8a879feacbb2b8671eba4bdf9e14dc4c9e05da

Observation 5098089c-cb9e-413e-9e15-15daad3e080b · outbound

This paper cites Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale.

Ultra-Sparse Memory Network Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale

Reference 31

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Observation a5b61079-0425-4b16-b1ea-b59d91fae9c9 · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

Ultra-Sparse Memory Network GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 32

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Observation 7991625d-3965-4cba-8660-45da45170c0b · outbound

This paper cites Hash layers for large sparse models.

Ultra-Sparse Memory Network Hash layers for large sparse models

Reference 33

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source=arxiv_source observed=2026-08-12T17:40:11.832312Z digest=sha256:5bee9f942d4db46f8afb446a36db1fcec076b77f90d4a7deaa8f1ef29c37e796

Observation 417f0688-3ddf-4135-bc75-1ce52165397e · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.

Ultra-Sparse Memory Network Winogrande: An adversarial winograd schema challenge at scale

Reference 34

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source=arxiv_source observed=2026-08-12T17:40:11.835946Z digest=sha256:ee158010796668233f80828bdadfa30d94ddeee766cc56f82e06c16a1ed5d26a

Observation 3dda8bfd-b93f-4cd4-acdc-4f3e3cbdf1ec · outbound

This paper cites Neural Machine Translation of Rare Words with Subword Units.

Ultra-Sparse Memory Network Neural Machine Translation of Rare Words with Subword Units

Reference 35

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source=arxiv_source observed=2026-08-12T17:40:11.839323Z digest=sha256:7f35fec59e82177e9093b423345e354cf0cb640a5c1df6a7e92ecb0e1def2073

Observation f6eeeeeb-0339-4fe8-915d-adba2ce6b7c8 · outbound

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

Ultra-Sparse Memory Network Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 36

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source=arxiv_source observed=2026-08-12T17:40:11.843128Z digest=sha256:a4befa87e3ac0f169f9dbb562ca275353198e78165fd88c7361bf24d37094fc2

Observation 4178765d-a19c-4b53-a8a4-42790ac2b85a · outbound

This paper cites A Study on ReLU and Softmax in Transformer.

Ultra-Sparse Memory Network A Study on ReLU and Softmax in Transformer

Reference 37

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source=arxiv_source observed=2026-08-12T17:40:11.846944Z digest=sha256:f8644695a515b709aa6819dcadc16c70093446b82f350934bd2797f381d06847

Observation b268acff-ebab-4aa8-a23e-1dc64dfbd0e8 · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

Ultra-Sparse Memory Network Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 38

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source=arxiv_source observed=2026-08-12T17:40:11.850766Z digest=sha256:585348d883216fdd492dd5b2ec1ef45d719510ce43724998ac5ac2b5403cb10d

Observation d8b17f34-6df3-4d25-96fd-30cbe79577db · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.

Ultra-Sparse Memory Network Roformer: Enhanced transformer with rotary position embedding

Reference 39

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source=arxiv_source observed=2026-08-12T17:40:11.854624Z digest=sha256:bc95cf32974eb050a0cf1f905c3d9c0d4589cfd0f492794539bf65a1b6d79732

Observation 2798ba0c-1687-4752-802c-a3c2dcf5fba7 · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

Ultra-Sparse Memory Network Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 40

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no resolver link, observed 2026-08-12T17:40:11.858088Z

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source=arxiv_source observed=2026-08-12T17:40:11.858088Z digest=sha256:b62ebec983a06a57a004b2ad517a8165817a93ec91b43313f4dc58d300010740

Observation a8de3515-0ce9-4973-8b3e-a78c8e442842 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Ultra-Sparse Memory Network LLaMA: Open and Efficient Foundation Language Models

Reference 41

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no resolver link, observed 2026-08-12T17:40:11.862189Z

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source=arxiv_source observed=2026-08-12T17:40:11.862189Z digest=sha256:c28b4fca7d47233443a4782a6692454179a996d7fbbf34588415c9e405b13eb0

Observation d430c2f1-83cc-4949-a235-da421349aa98 · outbound

This paper cites On layer normalization in the transformer architecture.

Ultra-Sparse Memory Network On layer normalization in the transformer architecture

Reference 42

Resolution
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no resolver link, observed 2026-08-12T17:40:11.866101Z

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source=arxiv_source observed=2026-08-12T17:40:11.866101Z digest=sha256:2fe2c09d880261f0373e4f3296ee767ecf8f6c802a83fad445d606facec8446a

Observation 1bd81ccb-27cc-4159-b932-60140161e6cb · outbound

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

Ultra-Sparse Memory Network HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 43

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no resolver link, observed 2026-08-12T17:40:11.869601Z

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source=arxiv_source observed=2026-08-12T17:40:11.869601Z digest=sha256:5aa53614f70f2d1421c7d0ecb41ff8dad935f833d66b287816178d675fbff68c

Observation f964a509-040d-4db2-8c1f-3416831f6264 · outbound

This paper cites AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models.

Ultra-Sparse Memory Network AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models

Reference 44

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no resolver link, observed 2026-08-12T17:40:11.873164Z

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source=arxiv_source observed=2026-08-12T17:40:11.873164Z digest=sha256:0c8265e1b6a972e8e85007060078281793276a3c0d39f262a05738418e3b3846

Observation a166d638-22f7-4ea9-8a54-d8577b75451c · outbound

This paper cites Mixture-of-experts with expert choice routing.

Ultra-Sparse Memory Network Mixture-of-experts with expert choice routing

Reference 45

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no resolver link, observed 2026-08-12T17:40:11.876728Z

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source=arxiv_source observed=2026-08-12T17:40:11.876728Z digest=sha256:9cf356a06ceafb32ab641a50c0431b5ca94691298b3bab8fbee01be5bfd61ec0

Observation 57c341e5-e6ae-4d34-b417-20149107f429 · outbound

This paper cites write newline.

Ultra-Sparse Memory Network write newline

Reference 46

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no resolver link, observed 2026-08-12T17:40:11.880130Z

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source=arxiv_source observed=2026-08-12T17:40:11.880130Z digest=sha256:c0f6813baf6c96ffd3934bc91d06051e1f71742ed62aea7cda48617b82d1b81a

Pith citing papers

Observation 2b074278-78de-4f66-a8ea-d8d8537c3e3c · inbound

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

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Ultra-Sparse Memory Network

Reference 17

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no resolver link, observed 2026-08-11T12:03:42.481591Z

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source=arxiv_source observed=2026-08-11T12:03:42.481591Z digest=sha256:fb0bff8267dea2585cf64d46ee2bf7109090c82c5a24bcfcb458ac8db5bc522d

Observation ab689d32-1985-4586-bc5b-1776e1733e0c · inbound

Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities cites this paper.

Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities Ultra-Sparse Memory Network

Reference 12

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no resolver link, observed 2026-08-07T13:43:20.892077Z

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source=arxiv_source observed=2026-08-07T13:43:20.892077Z digest=sha256:a72ae85bb52deeecda94b38034df591a20d18776d435a8126ce29a2dba0a54ff

Observation e8ecbb92-aa3c-432f-9a7c-a9f8c04ef171 · 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 Ultra-Sparse Memory Network

Reference 18

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no resolver link, observed 2026-08-05T16:17:41.998354Z

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source=pdf_text observed=2026-08-05T16:17:41.998354Z digest=sha256:74b9a11fe3500f91bd7a14b820af5160e595aff8f6b765aa002e632ea2216561

Observation 9a4381f8-292a-496d-8f4e-52436d4de2cd · inbound

MIDUS: Memory-Infused Depth Up-Scaling cites this paper.

MIDUS: Memory-Infused Depth Up-Scaling Ultra-Sparse Memory Network

Reference 11

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verified exact
arxiv_id, observed 2026-05-16T22:03:36.155671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-16T22:02:42.297041Z digest=sha256:b4e07134c8cdca5160daa7266673a74bb76adc74e61d1a29b370395c979a95f3

Observation 05776b31-66e4-4652-bfcc-c8b9859efb31 · inbound

Memory Grafting: Scaling Language Model Pre-training via Offline Conditional Memory cites this paper.

Memory Grafting: Scaling Language Model Pre-training via Offline Conditional Memory Ultra-Sparse Memory Network

Reference 21

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verified exact
arxiv_id, observed 2026-05-21T05:49:40.770806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-21T05:49:14.789955Z digest=sha256:d434b7ab65ec12fc46cb1dcafa1ad5e1379a7f10bc78395b8b848088712554c8

Observation 4b42034b-fa73-4903-833d-76720aa0da34 · inbound

SinkRec: Mitigating Semantic State Sink in Long Sequence Recommendation with Memory-Conditioned Gated Delta Networks cites this paper.

SinkRec: Mitigating Semantic State Sink in Long Sequence Recommendation with Memory-Conditioned Gated Delta Networks Ultra-Sparse Memory Network

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-07-02T07:06:43.815473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-28T07:13:54.031036Z digest=sha256:0d5358a360d00d57bb8907a33f526ea65ecfe4018ca280b983bd3531114420ed

Observation 07965896-2fa1-4712-ae28-18f94b5ae216 · inbound

Augmenting Molecular Language Models with Local $n$-gram Memory cites this paper.

Augmenting Molecular Language Models with Local $n$-gram Memory Ultra-Sparse Memory Network

Reference 28

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metadata mismatch
arxiv_id, observed 2026-07-03T10:37:56.681243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-06-27T09:57:12.398344Z digest=sha256:f50b5f51ab8d24f6846c9b4920701562e1226e03a0126c967d2c97964b50236b

Observation 3ef7cfa3-82d5-4c3d-af11-1ef9450c970d · inbound

User as Engram: Internalizing Per-User Memory as Local Parametric Edits cites this paper.

User as Engram: Internalizing Per-User Memory as Local Parametric Edits Ultra-Sparse Memory Network

Reference 38

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verified exact
arxiv_id, observed 2026-07-04T01:09:19.455849Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-06-26T20:37:01.382431Z digest=sha256:43d29094b5768f02bc5433d7eb53cb8f944105967cb25ae8e6c1c390671fb8bc

Observation e364a06c-7f2d-4cab-aeaf-bc5df9d406ab · inbound

Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training cites this paper.

Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training Ultra-Sparse Memory Network

Reference 7

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no resolver link, observed 2026-07-11T10:45:46.618668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T10:45:46.618668Z digest=sha256:e21af81e1bec53344e0f83e67967d68c28049135edc88ec0582a7b09f76d62d8