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Paper Citation Record · LEDGER

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

As of 10 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 2026-08-09T06:31:02.800959+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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  • verified fuzzy0
  • 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=sha256:54bd8b9461926018d6f9bec03d8f3f62f7149e92ceba4349b074254b126d5a40

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=sha256:60029ae13d362e829b7e91518b03c12a45c3413665dc1b25bba684cf8df6e969

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:cd18a3d275595fd2f0681d1e81cd9108751c7ef7b930b9ed0d2446784d8ffbbd

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:e626da7ba70338b818c97d80b40933708d1415237843deb1bdc7589761ecf46e

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:f9afaea77b34214974dfc02a8a0cc3f1aeca52097ba5d0fc8b5af011c9e4b78d

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=sha256:5952b4f36a75cd272aa4afa6dd34f7a2744a03ad6f2d38ad6f07822152ed9b09

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=sha256:32d2b4158e3b53589d059c4d7726daeea202e0c2f79e62fb926af2c1f545a4a4

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:bed08d76f2e77b5a031d0d8ecbe5ba99e038736bacae240fefd073b5a490be94

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=sha256:1b91b6d90f0526ad6560bd7b0223ab16ba7240174ed92e77d4ab336e160120c2

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=sha256:98bed9cfe37156c2d472259e1011c4935990d695e83c7c1b2e4a7ca589428b08

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:f45e7bfec29b5426dfcd8e835a96c9088deeb161614ccf5328dd6da8b4fd4671

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:d60b778366f532d55fe6df664aa2ca9f1779be573f4fa9051b28104499537f11

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:c97cca800939e6c942d4f437bb812932935c11de83622b1ffcb4a058cffc1b5b

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=sha256:12bc43806188dc56ac9fbea65869cc2750f89060ac1817740c21a46b91969f59

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=sha256:2ae52a83f398ff4bfd8a04a6a6b98b4660e3178e028868a1a9644a59e9c590cb

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=sha256:50916b5fba34f6e9ad443796dd6b4ef9a491ccb2004d989860dc5d23573fc747

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:b3e949242b0a3fdd4360cf077336496dfc4773159372e9d53873828d2928ebc1

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=sha256:7dc1631c45aa2a8cd29a648106fcc9059ce8c044fcbe5c271c207bd37ff38c37

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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no resolver link, observed 2026-08-01T23:59:01.594128Z

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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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source=arxiv_source observed=2026-08-01T23:59:01.760532Z digest=sha256:9ceeab65341b300f5319e4d4c9a5cdb20527c776f4a7db83b557cfa18a36e49b

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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source=arxiv_source observed=2026-08-01T23:59:01.891396Z digest=sha256:440ebe7ab3d3bc063024833a9a1c44329e26f5c66a627c50fea82368c73b562a

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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source=arxiv_source observed=2026-08-01T23:59:02.003533Z digest=sha256:fb4b5a4d8fbd17b385239e9b144a16807786da808ecb7ee2e2a7529f9e60a14c

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=sha256:20b161b2aa2297b13eeefac4a266a1b70a43163e3e335f5b578352f622ccf93c

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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source=arxiv_source observed=2026-08-01T23:59:02.439596Z digest=sha256:52eef52bd70ccb1ab954b2c38b0c059fa17c178e69793e1566758086c1c5372c

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:ee90d6cea42a142411fdf5ca91e0bdcacda343b3faf7e0f28ecb8d93eb47d19a

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=sha256:6aa1a7bd1f7e40c5c36c1ca84075f3a6607a6d9103071131a95afb969fa252fb

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:a103403c205f3444c304c7305124555da0ce17534b31e84dcd522cb1e7b309f0

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:db47e588a22724d1e641341fbbea4272e0df25a0ef5a782381cf3f7a7f39dd04

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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source=arxiv_source observed=2026-08-01T23:59:03.077762Z digest=sha256:9f5c85b56028ab91ee4180240b39c39545d12c5caf069864e73028c5f2417ba7

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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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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source=arxiv_source observed=2026-08-01T23:59:03.293921Z digest=sha256:41a5424ef2544f82dd0f97b128233fa9ce1f25dc0dbe0705ea849e44fa6354dc

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=sha256:4989c7cb9a285d516c6c45ee75e1285b46b4cf9a5aa94b3b189b104eb42de15b

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=sha256:8c2fbd1b23ecb8252e4d6955cfda6cee35d3db34a5147c3e12ac23912e046a5d

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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source=arxiv_source observed=2026-08-01T23:59:03.683487Z digest=sha256:2b31af6bdde603d472eaee40f38acd50a564265b25c7e4224cbd486056d1a0a3

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:d0c573b46bc185bb2a6a1dd5ca29344dfcf3e76cb53903f942461ebadaff2230

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=sha256:1f7835e7597869ec3a5d013958a95884dba8df762619d661261607c7bed45336

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=sha256:05ae4e3b863a1451ee4c0fa27370e9bb9aa1dfbf7dceeed87cafdfffd7bb193e

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=sha256:78e9b178fcfddb649c0199d20fdd30b11dfc387dad7477434c2f28561e550b2f

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=sha256:2f5ed4ae3202cb25d1cf492a5c126cc12026e3e2c9e8b7d6e136520ebda652c4

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=sha256:3b504d8c25ffe0a4d4dd28aeedad6a5b948fff0f99783fc7fd7dc5e1e8c71776

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=sha256:2f89a9775394a82b19b619ba0c93c80f7246bd6e2668418cfbcd5a3ef5993349

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:a214f46adaf16db130e04ce4f0f28ac55f89c1a551c3c8c3b1e96de7f7a1fde0

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:e64a4d09855025967cbbb37faf2b7a0a765a880d3f86765ffe96941fdd6a3713

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:e8f0671b2e03ed75dfde33476fd808588c10b172ce0773ac7e2936e22f65bb0f

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=sha256:63434c92b6f33638a8d9ffca2d6daaef5dd55f321766c1a02006eba6f9a10d8f

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:a8b5a0544f7c715553b7ed1044acd2b832b705a7ed6ffb036ba692fd833b62e2

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=sha256:5a92622e2505093481dd2fc327ab7d2a747353efeb5e9ad4a4569e2c02e069f7

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=sha256:216d748727a0b6c17d4fc0d4fe241a5fa0d4d0a7011386d9535409d02d508e71

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=sha256:142dd27f9c6c29c3bce007e1aac802188e64777e45745a8a0f49a365edf26075

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:b7d545ec5da0f656fd95321612881e5d7cfdc9813a2b15658b704f8443a979c0

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=sha256:05c0afcf20ead6910f16d6c83c38d9e407c1fc026039124ac190e9af49007d95

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=sha256:015e4e755afec95ac377fb8210d687feca504d8f3808dce990ae3ed8cdfb459b

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=sha256:68b8d2a6734ba78593629f2abe01e60857fc70c55977c32e559759d74ad82abd

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:cc39f6c954f5a8278877f367c5971ac4d4b974cd7b57e76b262c6655f32b5d8c

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:a25313d262709f4fad4db76839eee2636e4b451d1388526a2bd16bed8f5ac78a

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:bcb54a45c185a3da2ecc033488d99e497cdffb4efdeee7c62a613f73c63c9d91

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:dbcc50ab12e531cdb54c5f19999628049283dec62e156166c86e1e124c7a3d76

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=sha256:5fcb2bdd6ba1bea9f6059ec19807eb29da64ca1e628faf6c676516e62867cc3f

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=sha256:0d96933e34c97ca27b36e4c8243cab119f5afdbfa183635a381c9a937523c347

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=sha256:01e5d8ef194b399c03f46a21f8f770be9abfb3ce4e2173f42ea33c74b03d87a4

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=sha256:4d3453b508dbb2dd559fdf63ecd5e05691ee3fce816f134c05bcad06845b2f10

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:b6984f2ae5d44d51414daad5b5a09619c3d5d88e2c779c0e23c89173aad15f68

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=sha256:4e98d0a26da267cb33f610fe6388860d072713e4af74f03344a33d97d692b54e

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=sha256:38a12affeba3bc53de74075ff84e7e27251a777e78e6b69f894a36931d8aa28f

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=sha256:3cee7f5153a49e91e7f9565fe1efa29aeac2ee2de67234fc99fd1a63bbbedccf

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:facfd3c6161b81d39717985c3a918cb26cee08b99079448ae829760bcb4fa41c

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:e7ffc0d87816fe9baa71a3a92884424fb6238ec4079b31fd1bb77eb78506e0ad

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=sha256:6c9bc4cfb819b2532ed585cdd6c1e3460a814acff370e1c6215177cd65520090

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:ba95d915dbcedc8f8f26a80cf56f8034c0fbbc9a8aac778824e9073c017d0900

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=sha256:29b198c74586195f008f391b97f987a67bd228672d93d337f7d9bd95d26f0a3e

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:e1fbb1f897d6e40659faa2632b289fc61a0c1eacd97115b5df2f05a870f3460c

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:a508941f9ccefbff5a856b1077bc345fd4bd30c53d8e09b726c342aab0c97465

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=sha256:10f7528b2359898ff62321fe668c162be4134fd35d6436074d770fefba4c3f3d

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:fdd1bf630ddb6a905abf71448fdcb957607040be240d36f27502f1f283b7fd04

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=sha256:2afeb9288d1672b993243b62e413ea64b8c01fb316d62db9191c2e2c7f7d6105

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:a3c2ea8898beca6e3cdddd660d33b6cf4a97a3f080106148ae1b1f30fa91c781

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:fc12b8916b20a2ce783b209e129108935e938ef0676e042accb1ebf8b800b14d

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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source=arxiv_source observed=2026-08-01T23:59:09.407014Z digest=sha256:2c2fd6e478c901978d99c4458995a6c65e77e6d03c98da96b05c45703065b0b9

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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no resolver link, observed 2026-08-01T23:59:09.602397Z

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source=arxiv_source observed=2026-08-01T23:59:09.602397Z digest=sha256:bee81158c37be8340ecc1dff044562c72e1c4ac315269f9c3568c293a8b8b512

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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no resolver link, observed 2026-08-01T23:59:09.717657Z

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source=arxiv_source observed=2026-08-01T23:59:09.717657Z digest=sha256:d5aec14e2b9f7c98d5ad0aa5f8a72a51a1c409cb140941845fc2b6864a97c541

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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no resolver link, observed 2026-08-01T23:59:09.860047Z

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source=arxiv_source observed=2026-08-01T23:59:09.860047Z digest=sha256:d98e789ebfdaa79aaa9220b9b52476b5dfbbf103e5c7db6d4a64afb967350a61

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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no resolver link, observed 2026-08-01T23:59:10.045449Z

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source=arxiv_source observed=2026-08-01T23:59:10.045449Z digest=sha256:863f7d641af9e98272dbb134cf23bf05b6cfae9e610d447d52a03f210b741ccc

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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no resolver link, observed 2026-08-01T23:59:10.195273Z

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source=arxiv_source observed=2026-08-01T23:59:10.195273Z digest=sha256:6f0e3b33966ba09640172051a167be33759a7151cb81f4718260502e306bd4ac

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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no resolver link, observed 2026-08-01T23:59:10.345374Z

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source=arxiv_source observed=2026-08-01T23:59:10.345374Z digest=sha256:0a47abf5d22b37280ad4ec0d7a7233718a2408141f0ee0b86c4b28fd293e4821

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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no resolver link, observed 2026-08-01T23:59:10.544807Z

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source=arxiv_source observed=2026-08-01T23:59:10.544807Z digest=sha256:3a730fbf68e383a5ae3611409c399b2846e3aa4e1e9984d334d7cf1c62c91ce1

Pith citing papers

No inbound Pith citation observations are available.