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Paper Citation Record · LEDGER

T^2MLR: Transformer with Temporal Middle-Layer Recurrence

As of 8 August 2026, this Paper Citation Record lists 87 of 87 outbound references and 0 inbound Pith citation observations for arXiv:2607.15178.

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

pith.paper-citation-record.v1
2607.15178 v2

Coverage vector

measured 87 of 87 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T23:59:10.544807Z

measured 87 of 87 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

87 of 87 outbound references displayed

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  • unresolved87
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7f55eab4-1694-47ac-bf7d-d5218a72fd56 · outbound

This paper cites 2025 , month = jul, url =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2025 , month = jul, url =

Reference 1

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source=arxiv_source observed=2026-08-01T23:58:59.603740Z digest=sha256:55c56c1970df2bf4be06f2446b32ad40274906548afcd0f828fde05146b9c013

Observation 3d731eaf-c443-47f9-93a5-27a933a63bee · outbound

This paper cites 2025 , eprint=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2025 , eprint=

Reference 2

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source=arxiv_source observed=2026-08-01T23:58:59.661234Z digest=sha256:22bc6218dd09e452ad3e6729b5098ab6c0c39646e50e4adf8592b443067f57bd

Observation 2f517a27-6299-454c-bd5c-3ac9b91c65bf · outbound

This paper cites ACL , year=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence ACL , year=

Reference 3

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source=arxiv_source observed=2026-08-01T23:58:59.715069Z digest=sha256:f9854ec96c60505acf4fe7bd2297a3093fc48e56e72e17da05eec31222b0912b

Observation 0fe4dd4f-abe2-4ad1-a620-bc544d2f0c99 · outbound

This paper cites ACL , year=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence ACL , year=

Reference 4

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source=arxiv_source observed=2026-08-01T23:58:59.760161Z digest=sha256:0b7a71425eb97324f88bf93fadee623355d9dd8b704efd8e6978799ce37a9d44

Observation be62ec07-ca60-43d1-bb47-db0df13ebb97 · outbound

This paper cites EMNLP , year=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence EMNLP , year=

Reference 5

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source=arxiv_source observed=2026-08-01T23:58:59.875400Z digest=sha256:d468f7cfbebaec639d145e0d2c50b6480ee424b4e8020b918256177a5e49c1df

Observation aa3d484b-f651-4af9-88e2-8d0238dd303d · outbound

This paper cites NeurIPS , year=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence NeurIPS , year=

Reference 6

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source=arxiv_source observed=2026-08-01T23:58:59.998918Z digest=sha256:e0324860e85d668f2359af10abb27cc3c01361db1e414958094cef6e4b920f91

Observation b0430fa5-b2b4-4246-821b-f559f6905f8c · outbound

This paper cites OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models

Reference 7

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source=arxiv_source observed=2026-08-01T23:59:00.140541Z digest=sha256:20d94f9efbc47dd3b775854ee6076c413622d70a0b7dbc9d19af1443ff52e6fb

Observation d18ded05-8f24-4444-9fe6-e21d510317b4 · outbound

This paper cites Proceedings of the 39th Conference on Neural Information Processing Systems (NeurIPS) , year=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Proceedings of the 39th Conference on Neural Information Processing Systems (NeurIPS) , year=

Reference 8

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source=arxiv_source observed=2026-08-01T23:59:00.227058Z digest=sha256:1bf178151e7cb766779f007089d0623191d236a0f53f8f341cb0a3c60b274675

Observation f44990f1-82b3-473e-9dfa-596b2f95f843 · outbound

This paper cites JMLR , volume=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence JMLR , volume=

Reference 9

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source=arxiv_source observed=2026-08-01T23:59:00.374483Z digest=sha256:72b797850f909867fbbf7fe592567eae0da4b881db7b599d7306b9bb25bb39ae

Observation 2f143643-54df-4c11-a54a-066f5fe7a407 · outbound

This paper cites ICLR , year=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence ICLR , year=

Reference 10

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source=arxiv_source observed=2026-08-01T23:59:00.468249Z digest=sha256:beb66088e0987f45c6ae508255c403dca0102e34c7aaaa1b3e96fe3723615687

Observation 801aaa39-7c35-47ea-97b3-3c542afb268e · outbound

This paper cites EMNLP , year=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence EMNLP , year=

Reference 11

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source=arxiv_source observed=2026-08-01T23:59:00.567460Z digest=sha256:e52a5f2842db840879b7399eb438f769210e3923c4b880a68587222bddb5a5ff

Observation 16edb8b5-7fb3-4f90-9a7e-40a91fc1cae3 · outbound

This paper cites Neural Computation , volume=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Neural Computation , volume=

Reference 12

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source=arxiv_source observed=2026-08-01T23:59:00.703229Z digest=sha256:3d46aa9757e972c20caae493c063766f87bc91ffc60b6be124e3979e628105db

Observation d0aa92f4-d5e9-4714-8a69-6e3bb9b99f15 · outbound

This paper cites 2024 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2024 , eprint =

Reference 13

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source=arxiv_source observed=2026-08-01T23:59:00.784086Z digest=sha256:3a5352ca2883cad7b933a03e33a51e98c4fd8b728fdbf40e80b6d243abab243d

Observation fa6e4c94-8076-4e56-83f5-533edbf0168e · outbound

This paper cites 2024 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2024 , eprint =

Reference 14

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source=arxiv_source observed=2026-08-01T23:59:00.934803Z digest=sha256:c29c0b95ce84385c1d8b517db73bb2733ac1cf2518f1938d5ebd489cb44d1de9

Observation 0eed95b6-d29b-4dfa-99ff-b58f28500991 · outbound

This paper cites 2025 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2025 , eprint =

Reference 15

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source=arxiv_source observed=2026-08-01T23:59:01.093811Z digest=sha256:dc914b1ec4d75a5e54183bd086be81a0fcc982ec69c19dd275b3de545a149a44

Observation 31989b68-c3d5-4caf-9d38-af35544d3eda · outbound

This paper cites 2024 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2024 , eprint =

Reference 16

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source=arxiv_source observed=2026-08-01T23:59:01.199986Z digest=sha256:d5d2a64292d8920c5304e6c067b99f54edb6eeb8a21e550b8f97be4f221f60b3

Observation ebaca8cf-d3cf-4b96-9667-07487a74bfa0 · outbound

This paper cites 2023 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2023 , eprint =

Reference 17

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source=arxiv_source observed=2026-08-01T23:59:01.303015Z digest=sha256:0199e9a6c20ed87bfce63f48fde93df398e8f1bd77ada6ade4d989cb4bf328a3

Observation 68149282-5622-413a-b87f-7302bf16e738 · outbound

This paper cites 2024 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2024 , eprint =

Reference 18

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source=arxiv_source observed=2026-08-01T23:59:01.420314Z digest=sha256:8398e0d23d7c152aa651895f095ab822091839356062551d88657848758ddf63

Observation 997bf273-c833-4df6-90a2-6c2656e14a94 · outbound

This paper cites 2024 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2024 , eprint =

Reference 19

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source=arxiv_source observed=2026-08-01T23:59:01.594128Z digest=sha256:a86c7fb48d70d6c6f0a9338049d044319e540068613a3b54786b02fdbdf7fde1

Observation 664fc7b7-99c7-4f8b-b6be-5555e246b5a9 · outbound

This paper cites Forty-second International Conference on Machine Learning (ICML) , year =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Forty-second International Conference on Machine Learning (ICML) , year =

Reference 20

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Observation 14cc6a46-c10f-480d-8f3b-a28b7c2180bf · outbound

This paper cites 2025 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2025 , eprint =

Reference 21

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Observation cd6bf2f1-6e43-4209-9cfd-0cf515818430 · outbound

This paper cites 2024 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2024 , eprint =

Reference 22

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Observation 4213cbe1-3811-4131-8bea-a3843d5dc998 · outbound

This paper cites 2025 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2025 , eprint =

Reference 23

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Observation 79cb2b3f-dabd-490e-b120-e0984978b89b · outbound

This paper cites 2025 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2025 , eprint =

Reference 24

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Observation e24d3734-043a-475d-8df3-2e7bb0c8d6e7 · outbound

This paper cites 2025 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2025 , eprint =

Reference 25

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source=arxiv_source observed=2026-08-01T23:59:02.315483Z digest=sha256:70f6627d43aeae7689ae2e5aa92d12be1f5e76e7cb1aefd57e681274a878264e

Observation 52c5196e-e309-409d-80d9-2b71a9ffa671 · outbound

This paper cites 2025 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2025 , eprint =

Reference 26

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Observation cc8e384f-0a6e-4aa5-9398-a89ae1968a2f · outbound

This paper cites 2023 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2023 , eprint =

Reference 27

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source=arxiv_source observed=2026-08-01T23:59:02.579223Z digest=sha256:d195bc4edab963d9ad3cec15ed8828f0b07285a1d667a5e92ce0b3995d609942

Observation d66d191a-a7aa-4357-974b-56e1d1e04f3d · outbound

This paper cites 2019 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2019 , eprint =

Reference 28

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source=arxiv_source observed=2026-08-01T23:59:02.702875Z digest=sha256:aff54560a7750ca9951a1748e43531168be0362eed15d1f29520579a27232a3d

Observation 125549ac-3953-4cc4-a6b5-61e3cf17efef · outbound

This paper cites 2019 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2019 , eprint =

Reference 29

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source=arxiv_source observed=2026-08-01T23:59:02.834531Z digest=sha256:fba6112862fa87f8793ae4e2e495669be21602047f4e5c4b5c1fdacd6734541d

Observation 70cca30d-787e-44ff-9d2b-ee080c947ca7 · outbound

This paper cites 2022 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2022 , eprint =

Reference 30

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source=arxiv_source observed=2026-08-01T23:59:02.933458Z digest=sha256:7fe372e2ab3481fa3b44ce8a1edf987e481fe48f8a3348abf04943ab2d3945c5

Observation 85556c20-ff33-4198-a313-b73288e9d932 · outbound

This paper cites Advances in Neural Information Processing Systems , year=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Advances in Neural Information Processing Systems , year=

Reference 31

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Observation 8436a3c7-10de-46cd-be73-f4bf63c6c8e7 · outbound

This paper cites International Conference on Learning Representations , year=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence International Conference on Learning Representations , year=

Reference 32

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source=arxiv_source observed=2026-08-01T23:59:03.192695Z digest=sha256:b53f24ba916f9f17fa6073937e8ce11c71c330cc39eb48adefd11f31a249d416

Observation c21ed00f-5101-4b6f-96bf-2178ed46438c · outbound

This paper cites Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation

Reference 33

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Observation 7b611cde-773f-44a3-86e0-dccc4e90a542 · outbound

This paper cites Transactions of the Association for Computational Linguistics , year=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Transactions of the Association for Computational Linguistics , year=

Reference 34

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source=arxiv_source observed=2026-08-01T23:59:03.445571Z digest=sha256:808435e9ca6f386369ed89a1f57d0f83e9dfa14b1b86023d19658124fcb63e68

Observation dd7a10c4-5d44-42e2-869e-388ac80cec30 · outbound

This paper cites arXiv , year =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence arXiv , year =

Reference 35

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source=arxiv_source observed=2026-08-01T23:59:03.553279Z digest=sha256:7fa79a39930e1e4e5bc7856852fc224c4f135e90f2696b6a37b87c5ea916f9a9

Observation 7d435a16-711b-48d8-815f-63da759d00f0 · outbound

This paper cites GPT-4 Technical Report.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence GPT-4 Technical Report

Reference 36

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Observation 9eec0f67-3e36-442f-9428-6e92e127abe5 · outbound

This paper cites International Conference on Learning Representations , year =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence International Conference on Learning Representations , year =

Reference 37

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source=arxiv_source observed=2026-08-01T23:59:03.769619Z digest=sha256:209af57932a8078c387a45b3ac9c0837928c7a9efd594d25486bac8f96f63d59

Observation 135d717d-c363-42f3-b184-edce81701307 · outbound

This paper cites Advances in Neural Information Processing Systems , year =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Advances in Neural Information Processing Systems , year =

Reference 38

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source=arxiv_source observed=2026-08-01T23:59:03.902308Z digest=sha256:5b5658a264db4ec2d4cbab89475831080a6db568e2aa37c9ac7cce44092470d1

Observation 3e3f905c-8c2e-4458-91a2-2983efa8be09 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , year =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Proceedings of the AAAI Conference on Artificial Intelligence , year =

Reference 39

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source=arxiv_source observed=2026-08-01T23:59:04.060826Z digest=sha256:2c7628b86d93864f8cf8de8730c10cdd1cf542213a315149e43fe4c1e8d30fd6

Observation 3bc02b62-05a3-457e-86f1-26c0b1dfdcc6 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 40

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source=arxiv_source observed=2026-08-01T23:59:04.200276Z digest=sha256:cccd7717cf3bbe45d609c61045b455fafafe4a0c1e938f6d99721d9b1e784841

Observation f39f49bf-05b4-401c-8ab4-2e751e76c81e · outbound

This paper cites International Conference on Learning Representations (ICLR) , year=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence International Conference on Learning Representations (ICLR) , year=

Reference 41

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source=arxiv_source observed=2026-08-01T23:59:04.316627Z digest=sha256:50ba05f166c2b22172a8ca25315b1be4630f6ca44de23973e2c3b0fdfe2b48a5

Observation 066e7256-c2e3-43e8-95ca-a84910d17574 · outbound

This paper cites Proceedings of the 41st International Conference on Machine Learning (ICML) , year=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Proceedings of the 41st International Conference on Machine Learning (ICML) , year=

Reference 42

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source=arxiv_source observed=2026-08-01T23:59:04.443577Z digest=sha256:353d7e04f6423b2fb4c17dcef5c7a387d2fe92ba13a9f46ac8b43797cfb93947

Observation 52812987-ad0c-45b2-8ec3-ebd0e15ff097 · outbound

This paper cites International Conference on Learning Representations (ICLR) , year=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence International Conference on Learning Representations (ICLR) , year=

Reference 43

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source=arxiv_source observed=2026-08-01T23:59:04.561346Z digest=sha256:8c7b4c222033e1569b17345bc823b40ced18621c95ef1fed49b93435af8dccee

Observation 653a91d0-5305-4ab1-b347-045f49178ad2 · outbound

This paper cites 2024 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2024 , eprint =

Reference 44

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source=arxiv_source observed=2026-08-01T23:59:04.665395Z digest=sha256:9cee780b320b6eba9d0b0bcb5f77ac0eaa948e0837390de02ef15a24b637f08d

Observation 3836a552-a9e0-4ce9-af71-292544132abb · outbound

This paper cites 2021 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2021 , eprint =

Reference 45

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source=arxiv_source observed=2026-08-01T23:59:04.822622Z digest=sha256:f3546b23a5001a2164987e9bc4ec658180e7063c91bb902374153acbef764e30

Observation 90573d3b-0219-430a-9c00-a57ac367a5b1 · outbound

This paper cites 2023 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2023 , eprint =

Reference 46

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source=arxiv_source observed=2026-08-01T23:59:04.980828Z digest=sha256:9d4c8a06a61ada70c64b92f37514c3fe9ab48b185fbb7f054220032e67afb2ec

Observation 94de707f-669e-414c-8022-e5267710d8b4 · outbound

This paper cites 2023 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2023 , eprint =

Reference 47

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source=arxiv_source observed=2026-08-01T23:59:05.066564Z digest=sha256:68a9e363f47c64199d08f3af3e76908d35549b5e62a318a5a3582dec150e802b

Observation 45645a06-243f-4754-b204-14c6d0b5a55c · outbound

This paper cites 2023 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2023 , eprint =

Reference 48

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source=arxiv_source observed=2026-08-01T23:59:05.192021Z digest=sha256:d27a60901aecb16e27b9ec7b3b8ccb38322926b125107b7629347cbc29eb2f4c

Observation 8a54a73f-4519-4a68-8965-26b91959a2f6 · outbound

This paper cites 2024 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2024 , eprint =

Reference 49

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source=arxiv_source observed=2026-08-01T23:59:05.296176Z digest=sha256:62300a620b6b3b73a44996de134e884898b2e106396a814d70ff2ebce2444d2c

Observation b69f6637-6276-456b-966e-4e9e6cd22b1a · outbound

This paper cites 2024 , eprint =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2024 , eprint =

Reference 50

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source=arxiv_source observed=2026-08-01T23:59:05.431901Z digest=sha256:b9d3057934c9aa60ecc5923f3ddbe475bf4958194bf822b5869652982a371e06

Observation 8bc429d3-9a62-41b6-9716-2b92a722ee24 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Training Verifiers to Solve Math Word Problems

Reference 51

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source=arxiv_source observed=2026-08-01T23:59:05.583892Z digest=sha256:2e74ac5704b1906b01ce4370ac230e74e23b02b48891006968a9d0f4f8f88b7e

Observation 566dcc91-9ca3-44e6-959b-43ac8ef167d2 · outbound

This paper cites Forty-first International Conference on Machine Learning , year=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Forty-first International Conference on Machine Learning , year=

Reference 52

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source=arxiv_source observed=2026-08-01T23:59:05.732599Z digest=sha256:03fcea83338303ebd3d3e17a27b94fe302703c508e6b3532c7c1ab39f44222af

Observation 38351fbb-3526-4758-805a-08f6ec12c2cf · outbound

This paper cites 2024 , eprint=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2024 , eprint=

Reference 53

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source=arxiv_source observed=2026-08-01T23:59:05.902704Z digest=sha256:2903d31f4852f67dbaac1c95f4e6d4043263b8a6c6501504daf76c0d303af98c

Observation f35fffc3-7885-438a-a5d9-0f63849fa3c7 · outbound

This paper cites 2025 , eprint=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2025 , eprint=

Reference 54

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source=arxiv_source observed=2026-08-01T23:59:06.015150Z digest=sha256:ac2536da5224792274e0f79e7f434bc9382ffea022ffe5aed3700af2059c512b

Observation ce93cb97-5155-4275-b854-6d1c42f335ba · outbound

This paper cites Soft Thinking: Unlocking the Reasoning Potential of.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Soft Thinking: Unlocking the Reasoning Potential of

Reference 55

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source=arxiv_source observed=2026-08-01T23:59:06.170336Z digest=sha256:b4c57dbb49a60b55c47d008b1a81b6ed6319266ce3b860e04dd753d0c06196f3

Observation ccc173c1-9b1c-4169-b0da-e5be175bdecc · outbound

This paper cites 2026 , eprint=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2026 , eprint=

Reference 56

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source=arxiv_source observed=2026-08-01T23:59:06.289859Z digest=sha256:5935d66c85d2a1364fe34ae54a3cf436141eb6b20a39ff09385b19e5348904fa

Observation 7bbff521-d1c4-419f-9324-5104ae06b3d2 · outbound

This paper cites 2025 , eprint=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2025 , eprint=

Reference 57

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source=arxiv_source observed=2026-08-01T23:59:06.399430Z digest=sha256:12a9071066a73ddf0284f9b0b91b77c888d5687f0d48cf484c72e502949365f3

Observation 6188cded-67c5-46ee-be9e-2104e189a3a6 · outbound

This paper cites 2025 , eprint=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2025 , eprint=

Reference 58

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source=arxiv_source observed=2026-08-01T23:59:06.523132Z digest=sha256:28d7dc2688488fc1b85272c822acadbbf1f8a4a6866975370f777ad86e5ad83e

Observation 48ccd952-453f-4833-865e-44afe1f313ae · outbound

This paper cites Think-at-Hard: Selective Latent Iterations to Improve Reasoning Language Models.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Think-at-Hard: Selective Latent Iterations to Improve Reasoning Language Models

Reference 59

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source=arxiv_source observed=2026-08-01T23:59:06.630943Z digest=sha256:ac5223e470f5673fb3c201e10bb8f540980b885efeb5580875fce90fe6747b33

Observation c56f5b87-8ef2-49b8-8853-db8866ab4fd0 · outbound

This paper cites The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=

Reference 60

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source=arxiv_source observed=2026-08-01T23:59:06.769064Z digest=sha256:c84b544e5bb6a7558ed9d3322a4fe01d55215959b3744e26b6d94a6fdfd0455f

Observation 4e90040f-9f19-41a8-a17c-a599b489e37b · outbound

This paper cites The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=

Reference 61

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source=arxiv_source observed=2026-08-01T23:59:06.951386Z digest=sha256:a123407303a79132b3bb3b3014240f2bec639852bf8c2613b1feaacc6d6f0685

Observation c85868e3-82bc-468c-b21f-166dd770b2b9 · outbound

This paper cites fairseq: A Fast, Extensible Toolkit for Sequence Modeling.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence fairseq: A Fast, Extensible Toolkit for Sequence Modeling

Reference 62

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source=arxiv_source observed=2026-08-01T23:59:07.105473Z digest=sha256:ab908ba6b6ca32ee68dfec87e4db959839a23f62a229904705d9be6ac071b2a7

Observation 3aa55b87-ab83-4256-af6a-bc73de7c4036 · outbound

This paper cites 2023 , eprint=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2023 , eprint=

Reference 63

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source=arxiv_source observed=2026-08-01T23:59:07.193827Z digest=sha256:81cfc0e76ee2b1cffd27666f4d51a7771793db2a6c8b4f81021acdc25498c551

Observation 81794c7e-fed9-4144-b729-b31d9f7b9863 · outbound

This paper cites 2025 , eprint=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2025 , eprint=

Reference 64

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source=arxiv_source observed=2026-08-01T23:59:07.341365Z digest=sha256:a2c9ab34b5113fe4f8922174e5e14988b4bfaa52b7e6b1be48c8f49d495a6537

Observation 439e67e9-1727-4683-ac6d-1bc5261f0f31 · outbound

This paper cites 2020 , note =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2020 , note =

Reference 65

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source=arxiv_source observed=2026-08-01T23:59:07.479582Z digest=sha256:23d97e7c4e6500c4449698234dc89a4d3af5518bd77971f131465bff56c2a697

Observation 944cb9ec-d9d4-4faa-a319-74a52d5b8a2d · outbound

This paper cites Proceedings of the 40th International Conference on Machine Learning (ICML) , year =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Proceedings of the 40th International Conference on Machine Learning (ICML) , year =

Reference 66

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source=arxiv_source observed=2026-08-01T23:59:07.658337Z digest=sha256:e3186d704d02486ee015dd95408e2893f50543740e76a2d897f8196789357c4d

Observation 6973d784-cfe1-4456-912a-2b2f30b35e27 · outbound

This paper cites International Conference on Learning Representations (ICLR) , year =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence International Conference on Learning Representations (ICLR) , year =

Reference 67

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source=arxiv_source observed=2026-08-01T23:59:07.778821Z digest=sha256:bc891b84ba29b108b319ebd111bd207a4552a7f84eaa24ce40b16a18e12c9d20

Observation b47046ed-ec2a-4beb-bed9-f031ec7ae370 · outbound

This paper cites arXiv preprint , year =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence arXiv preprint , year =

Reference 68

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source=arxiv_source observed=2026-08-01T23:59:07.931706Z digest=sha256:2a4f013a308d36110dc3aa76c35c821804d2d5717a6c5e19e897f4effbc75568

Observation ec9bcb0c-461c-4664-b983-ecdf3ebba6de · outbound

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

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 69

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source=arxiv_source observed=2026-08-01T23:59:08.061295Z digest=sha256:7fd8fadf14fbd0e20b0386aecdf6f6149f83356090b7bc1e908561169f437fa6

Observation 56bd70d2-fc96-42fa-af97-b78b39a9b424 · outbound

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

T^2MLR: Transformer with Temporal Middle-Layer Recurrence HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 70

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source=arxiv_source observed=2026-08-01T23:59:08.234610Z digest=sha256:f9e0905a3671e6720d1799e7c739fa6f4bfa73e7f96d15a362298076121956fe

Observation c0ca0e15-230d-41eb-8233-3ebe7a9fe24a · outbound

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

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 71

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source=arxiv_source observed=2026-08-01T23:59:08.394239Z digest=sha256:7f4f2cfea865aa331c82aea778feb9943e411569463955423a34659696f3e24e

Observation 54f7918a-2cee-4cc9-a413-38779a224960 · outbound

This paper cites Proceedings of the AAAI conference on artificial intelligence , volume=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Proceedings of the AAAI conference on artificial intelligence , volume=

Reference 72

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source=arxiv_source observed=2026-08-01T23:59:08.563608Z digest=sha256:5bcce1b69e426ae5c6970c2d17dc9c1c212dae0fb7a7771ba41532fea60361f0

Observation 19535b06-bc9c-434e-a658-860cda32cfb6 · outbound

This paper cites Crowdsourcing Multiple Choice Science Questions.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Crowdsourcing Multiple Choice Science Questions

Reference 73

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source=arxiv_source observed=2026-08-01T23:59:08.727124Z digest=sha256:9e8ac0580228c011d89884c95be0e13a4c364f7e6a1ec18eba451932efaee7a2

Observation d158b251-6f45-41c6-a002-6f2a32596100 · outbound

This paper cites Communications of the ACM , volume=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Communications of the ACM , volume=

Reference 74

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source=arxiv_source observed=2026-08-01T23:59:08.863385Z digest=sha256:aa47a3074abe8a56a83c46633eac885251c3354634908d1637ea30298ba1c108

Observation 0f774761-aeb8-4877-98c2-e1a227a7e1ba · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Advances in Neural Information Processing Systems , volume=

Reference 75

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source=arxiv_source observed=2026-08-01T23:59:08.947435Z digest=sha256:03b2c7cfffe7511378b9fc0c9412bb48220882bf451d37dab0ef1680d9082fd8

Observation 1c806321-b5cc-42ff-b0b3-88676bc49dd6 · outbound

This paper cites The Eleventh International Conference on Learning Representations , year=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence The Eleventh International Conference on Learning Representations , year=

Reference 76

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source=arxiv_source observed=2026-08-01T23:59:09.011347Z digest=sha256:8396b5a699ac78aebdc7db5b7717c675a6ad462078d0021920175b033db43e9a

Observation d77f8311-c121-438b-a321-3c19e7a6f051 · outbound

This paper cites Forty-second International Conference on Machine Learning , year=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Forty-second International Conference on Machine Learning , year=

Reference 77

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source=arxiv_source observed=2026-08-01T23:59:09.086828Z digest=sha256:1ccd5ce75de58e190c9a8e64daeddcbebfe0cae2fc31eb2dfbde6dc5b96101cb

Observation c3bb7930-1276-4a15-9122-7039307da826 · outbound

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

T^2MLR: Transformer with Temporal Middle-Layer Recurrence LLaMA: Open and Efficient Foundation Language Models

Reference 78

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source=arxiv_source observed=2026-08-01T23:59:09.161707Z digest=sha256:21e58fe86ff1d8f1550f005b6948ec560e2e88d01c0d7dd7b5897905dbfeaeb8

Observation cc00ef8b-936b-4e2e-85b3-140ea193b0f8 · outbound

This paper cites doi:10.5281/zenodo.12608602 , url =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence doi:10.5281/zenodo.12608602 , url =

Reference 79

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source=arxiv_source observed=2026-08-01T23:59:09.252314Z digest=sha256:e450363b703bab13a8c0b5042a1916d26f1646160b372b075a53eb99e220f4d2

Observation c3ae0b9d-4b4d-464a-a248-07a765661a1b · outbound

This paper cites 2024 , eprint=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence 2024 , eprint=

Reference 80

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Observation 7ace3cc0-d90b-4c17-8880-5914799f2a4a · outbound

This paper cites and Salakhutdinov, Ruslan and Manning, Christopher D.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence and Salakhutdinov, Ruslan and Manning, Christopher D

Reference 81

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source=arxiv_source observed=2026-08-01T23:59:09.602397Z digest=sha256:02305d84963ce7519a4ac10dd9e90109b22db5d5307008669c848ab1a9311a59

Observation 06ea87f8-8eec-409f-927f-a99db0d22c93 · outbound

This paper cites Backpropagation , pages=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Backpropagation , pages=

Reference 82

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Observation 7336440d-bc15-4db9-a29e-fcc321283bae · outbound

This paper cites arXiv preprint arXiv:2505.12514 , year =.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence arXiv preprint arXiv:2505.12514 , year =

Reference 83

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source=arxiv_source observed=2026-08-01T23:59:09.860047Z digest=sha256:32cd801d8b8c095c552649c38683fcfea311b9b56c8c55132b1f1c5f78dc0a34

Observation 20583788-a180-4347-b6f2-a8495cb387a5 · outbound

This paper cites International Conference on Learning Representations , year=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence International Conference on Learning Representations , year=

Reference 84

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source=arxiv_source observed=2026-08-01T23:59:10.045449Z digest=sha256:efc9c286e7f7a0514554c7bfd93dbcf887b68f01cb443d77d75fd55f76a78997

Observation 79904855-57a7-4213-9f8e-2ed806db16fb · outbound

This paper cites Advances in neural information processing systems , volume=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Advances in neural information processing systems , volume=

Reference 85

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source=arxiv_source observed=2026-08-01T23:59:10.195273Z digest=sha256:916179deb1378ad8af49cee9f7af94de157b36107eaf9c54e1a57544caf4d3ab

Observation d19e202b-e231-444c-b451-01dd14dc7918 · outbound

This paper cites Gated Delta Networks: Improving Mamba2 with Delta Rule.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Gated Delta Networks: Improving Mamba2 with Delta Rule

Reference 86

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source=arxiv_source observed=2026-08-01T23:59:10.345374Z digest=sha256:5f2819ef08d99cf3fd0437c008de46c65e50213049513878273f8144c6b2ea3f

Observation fea5408c-bcd8-4b16-81bd-b3a17b8c06e1 · outbound

This paper cites Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=.

T^2MLR: Transformer with Temporal Middle-Layer Recurrence Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 87

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source=arxiv_source observed=2026-08-01T23:59:10.544807Z digest=sha256:f5a98b2d7648ef0037568de7739b99847efaf0d29d02b99639f5a437e1496c29

Pith citing papers

No inbound Pith citation observations are available.