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Paper Citation Record · LEDGER

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control

As of 13 August 2026, this Paper Citation Record lists 100 of 294 outbound references and 0 inbound Pith citation observations for arXiv:2608.07870.

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

pith.paper-citation-record.v1
2608.07870 v1

Coverage vector

measured 100 of 294 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:48:44.749058Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

100 of 294 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved100
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e57fe484-753a-4801-94ee-3ff9606f0877 · outbound

This paper cites Reinforcement Learning Conference , year=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Reinforcement Learning Conference , year=

Reference 1

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source=arxiv_source observed=2026-08-12T00:48:43.369255Z digest=sha256:d17428074febd2cde63462eb6e8e5e65fe0b6d55e810572efa51bb6e597ba973

Observation 0a1769ee-b33b-472d-bce1-c6040c873083 · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Forty-first International Conference on Machine Learning , year=

Reference 2

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source=arxiv_source observed=2026-08-12T00:48:43.374775Z digest=sha256:e9016b8daaa293ca66a834357b9e8dd5418aee128698dd4b54f8a545c289c98f

Observation f566992d-9f2a-4076-a641-251a09bb0f24 · outbound

This paper cites 1995 , publisher=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control 1995 , publisher=

Reference 3

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source=arxiv_source observed=2026-08-12T00:48:43.379946Z digest=sha256:0bff309e5909f659eb1e12ebfa2ad6e3b30e38d3b43bb6edf6feab4e532d89dc

Observation 6fe540d3-b1d3-44f6-8415-3847da813fe3 · outbound

This paper cites Nature , volume=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Nature , volume=

Reference 4

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source=arxiv_source observed=2026-08-12T00:48:43.385181Z digest=sha256:b32e9b479ff9ed864310d36490a5ad16dd816bc64843677f9897e7ab68f49153

Observation 05dafc2f-1811-4649-9c9c-2d5770715ac2 · outbound

This paper cites Computing in science & engineering , volume=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Computing in science & engineering , volume=

Reference 5

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source=arxiv_source observed=2026-08-12T00:48:43.390233Z digest=sha256:e92485bb03c24f6e260d65d5560b1d1bc9d12218da01f236c8d1b182fca855a5

Observation c76a30be-da7f-48e6-a214-3974facf7d31 · outbound

This paper cites an unresolved cited work.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Unresolved cited work

Reference 6

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source=arxiv_source observed=2026-08-12T00:48:43.396065Z digest=sha256:a37d0156ee431ae1e7a3b79c031016f0d59ac92c388df4e0793a3a3f2e77f87f

Observation 6b68cf87-fb4b-4cf4-a1d3-0cb5b3867532 · outbound

This paper cites , journal=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control , journal=

Reference 7

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source=arxiv_source observed=2026-08-12T00:48:43.400994Z digest=sha256:7922abb4aa92336be1471dd7c492e401e73be516a5fc1cdcfc20f65574677824

Observation 2ce24967-37c6-4a39-afae-8b685bd9e7fe · outbound

This paper cites IOS Press , year = 2016, pages =.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control IOS Press , year = 2016, pages =

Reference 8

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source=arxiv_source observed=2026-08-12T00:48:43.406424Z digest=sha256:f5b51cad1be933efa8644cd61c1d79cec0ae8ff3d58c6aa914be338930bc6000

Observation 537cbd8a-edd5-4e73-b8dc-77b7344564ac · outbound

This paper cites Python for Data Analysis: Data Wrangling with Pandas,.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Python for Data Analysis: Data Wrangling with Pandas,

Reference 9

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source=arxiv_source observed=2026-08-12T00:48:43.411348Z digest=sha256:15223d7ad7e92bfd7b62ceec45be23015d908bc278988eb02ba5030d9d57fabf

Observation 88ca4713-8e5c-451e-bdb2-b82c241a8bd2 · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Forty-second International Conference on Machine Learning , year=

Reference 10

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source=arxiv_source observed=2026-08-12T00:48:43.416067Z digest=sha256:2a2931c4f3f9663ac65d8098c8fba690bc295d2ba6d2827001c7a969e97e1535

Observation ba8fa836-86f2-4c74-8f27-38838dfbb0eb · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control The Thirteenth International Conference on Learning Representations , year=

Reference 11

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source=arxiv_source observed=2026-08-12T00:48:43.421960Z digest=sha256:838561531f8ef2b2d854af8bcc195247a6a4642ecaff5aac926539aa01872ebb

Observation 5307efec-b126-4005-ba32-fc25dc5c5554 · outbound

This paper cites Mixture of Experts in a Mixture of.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Mixture of Experts in a Mixture of

Reference 12

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source=arxiv_source observed=2026-08-12T00:48:43.427133Z digest=sha256:2fff4104237a53909c00f2af0c0bccdc2c64d8891083b585985b197fce7f8dd8

Observation bad3e48c-f9ca-41f6-b3e6-e3fecc873e94 · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Forty-third International Conference on Machine Learning , year=

Reference 13

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source=arxiv_source observed=2026-08-12T00:48:43.432382Z digest=sha256:215333a614e3f8ca99124a9663d387f6971f0d9bab408a6a6127162b03fade86

Observation 0a8446cb-4ed2-45e6-bb9b-e499db6719b4 · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=

Reference 14

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source=arxiv_source observed=2026-08-12T00:48:43.436841Z digest=sha256:56ff38929d1b7bcdab2713160ee4a7618c72cc20b97f1e3fe2595cbd4edb70c4

Observation 5150b72d-6b65-4fd6-98e3-f72911551c87 · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in Neural Information Processing Systems , volume=

Reference 15

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source=arxiv_source observed=2026-08-12T00:48:43.481309Z digest=sha256:bf51ba6facc43f54b0b358a555405293abed520c65b55e687d8a7da49ee3858e

Observation 88367737-f250-4c95-9ced-7c15ca0d2c37 · outbound

This paper cites A Survey of State Representation Learning for Deep Reinforcement Learning.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control A Survey of State Representation Learning for Deep Reinforcement Learning

Reference 16

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source=arxiv_source observed=2026-08-12T00:48:43.522343Z digest=sha256:9d021997f031466a0469cb074c14f72f3066e869e0f81719ab7ab2b791d87310

Observation e44d22c7-a2b9-4b68-bb0d-17eb3c6e61f3 · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International Conference on Learning Representations , volume=

Reference 17

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source=arxiv_source observed=2026-08-12T00:48:43.548531Z digest=sha256:032d90774a90b1b348398e7feb7c9bfc27ec945548cdf15772c852f9f33c4ad6

Observation 11ba5a13-27d9-4a8a-9098-e9b6fbb2c8b6 · outbound

This paper cites A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning

Reference 18

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source=arxiv_source observed=2026-08-12T00:48:43.611443Z digest=sha256:55c56e52e63a02eb11b241fd2e98255125d6ec167b2cd3e3ec776855fbd63e76

Observation f100fbb2-efc4-4b6c-a740-d696d3c323c9 · outbound

This paper cites an unresolved cited work.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Unresolved cited work

Reference 19

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source=arxiv_source observed=2026-08-12T00:48:43.661040Z digest=sha256:d2e68b82fc9f71e3e3aece21b712a872ebcb36491063f759f55fc961f091df17

Observation 822fcae4-438d-4c94-ba91-80311edbadab · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in Neural Information Processing Systems , volume=

Reference 20

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source=arxiv_source observed=2026-08-12T00:48:43.682920Z digest=sha256:bd95221ef7ae78daa4f1a776d104ed84d387e95a6e98dde66160311eeff1b293

Observation add1ad3c-3f92-43de-9a1f-3a94bc49de80 · outbound

This paper cites International conference on machine learning , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International conference on machine learning , pages=

Reference 21

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source=arxiv_source observed=2026-08-12T00:48:43.688473Z digest=sha256:c4f1e9766aadbc7b2f1144966e6ebadd40cd9e919dd6a10d8ced72d813a0d97c

Observation 01506ef6-e2e9-4d3c-bb21-c04ad0be6a5d · outbound

This paper cites Towards General-Purpose Model-Free Reinforcement Learning.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Towards General-Purpose Model-Free Reinforcement Learning

Reference 22

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source=arxiv_source observed=2026-08-12T00:48:43.695144Z digest=sha256:fa66819e03577582ca670f3bc4bc163f518aa99139d42bee7572f2f88b07df71

Observation e823bf09-6b87-401b-a16f-93f22e1f97e0 · outbound

This paper cites Data-Efficient Reinforcement Learning with Self-Predictive Representations.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Data-Efficient Reinforcement Learning with Self-Predictive Representations

Reference 23

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source=arxiv_source observed=2026-08-12T00:48:43.700829Z digest=sha256:aee2cc4c009e6f8c5d6e4fd7bba88706c2b3181f5cff5bb1deccbbbec2c77337

Observation 659b241e-5954-401b-b281-00999289429d · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in Neural Information Processing Systems , volume=

Reference 24

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source=arxiv_source observed=2026-08-12T00:48:43.706395Z digest=sha256:22e63705bf2173d5be34ba3913ce2ff04420fb64b2c4b7060967a69235366607

Observation 588fb875-6a1a-40b6-b008-c2be8e4ddcc3 · outbound

This paper cites 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS) , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS) , pages=

Reference 25

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source=arxiv_source observed=2026-08-12T00:48:43.711324Z digest=sha256:c9a11f9598ad3a416c88a896a45e7376f928c9a2f1537cc236d7631929ce1090

Observation f08bcd87-10c5-41df-8e2a-a31162099b81 · outbound

This paper cites International conference on machine learning , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International conference on machine learning , pages=

Reference 26

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source=arxiv_source observed=2026-08-12T00:48:43.716805Z digest=sha256:37e2ee711c7671b049d6d17dbe6590dd6b6b1122e345e5a39e38137705888f9f

Observation f6d33a24-ca73-4944-a4ef-b070bf066548 · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in Neural Information Processing Systems , volume=

Reference 27

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source=arxiv_source observed=2026-08-12T00:48:43.722823Z digest=sha256:9065d8ec01fbb2464ac9090e02048868e74367094c172615b6f9d1535ee2a3e7

Observation aacf2e18-d1af-4451-8395-b794e5fe600f · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in Neural Information Processing Systems , volume=

Reference 28

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source=arxiv_source observed=2026-08-12T00:48:43.728230Z digest=sha256:7f1425ec3c26694aefe9cfd8d2ef47679e8090735d220d25061a05433fa04259

Observation fcf7acd0-e71d-4f35-9770-c83b315843e7 · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in Neural Information Processing Systems , volume=

Reference 29

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source=arxiv_source observed=2026-08-12T00:48:43.735816Z digest=sha256:c48ed44873918574d4afdf78d0025e13867f69e665f8166d002f9ca2b15e2906

Observation fa20d1a6-60b3-4c7d-bb46-db45444c0afd · outbound

This paper cites International Conference on Machine Learning , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International Conference on Machine Learning , pages=

Reference 30

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source=arxiv_source observed=2026-08-12T00:48:43.746142Z digest=sha256:68586f7c961a5dbdb4ce7d31a729edefe21a28d7d83723bf976747fc2b785029

Observation 740f8dde-456b-467f-97e5-c227892f50db · outbound

This paper cites Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning

Reference 31

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source=arxiv_source observed=2026-08-12T00:48:43.752787Z digest=sha256:f8104c82147c7864ddef4d6a08fb9fbb7f09852580f969863d6c2fba84f2a730

Observation 191fbf1a-a53a-4466-8534-fd926951d195 · outbound

This paper cites International Conference on Machine Learning , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International Conference on Machine Learning , pages=

Reference 32

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source=arxiv_source observed=2026-08-12T00:48:43.758999Z digest=sha256:ff7bc2e11f20729cead9c1b183a6ca2c0382f133ad833448dc1bb1154fbccc13

Observation a702cc7e-7664-411c-a733-6e62d9961d49 · outbound

This paper cites , author=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control , author=

Reference 33

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source=arxiv_source observed=2026-08-12T00:48:43.764378Z digest=sha256:b70c3e6db7333ec6318157e2f8c691fcb34707542a843c38206ad038cfef49d2

Observation 66e176ce-c77d-4edd-a955-5227dad8bba4 · outbound

This paper cites Bridging State and History Representations: Understanding Self-Predictive RL.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Bridging State and History Representations: Understanding Self-Predictive RL

Reference 34

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source=arxiv_source observed=2026-08-12T00:48:43.773234Z digest=sha256:5027e524df74d1fabb5592a37ccb24ee5e897f02c7bbf7cfe6d908312e32885f

Observation 5974cec4-da9d-49dd-b719-f69b3a4ea8c8 · outbound

This paper cites TD-M(PC)$^2$: Improving Temporal Difference MPC Through Policy Constraint.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control TD-M(PC)$^2$: Improving Temporal Difference MPC Through Policy Constraint

Reference 35

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source=arxiv_source observed=2026-08-12T00:48:43.783070Z digest=sha256:6be0d74c43c4898f2c7c10453766e364950827c4cd58b7f0369d525c7fde7d5b

Observation a21eed42-d0d2-43af-906c-fadf49141fc7 · outbound

This paper cites Conference on robot learning , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Conference on robot learning , pages=

Reference 36

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source=arxiv_source observed=2026-08-12T00:48:43.788493Z digest=sha256:f91c43dd2ffa58569f2c13aa65073c214db14afa700f4affdc361ee8fb429035

Observation e74c4449-ae73-48dc-9134-5bedfa202383 · outbound

This paper cites World Models.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control World Models

Reference 37

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source=arxiv_source observed=2026-08-12T00:48:43.793741Z digest=sha256:9aa47e7806d7f7fec75564ea6e6d1c9064e71a67528fc4b9a0718ee0128365aa

Observation 05453046-1d4f-4fed-baa2-6bf8004e17e1 · outbound

This paper cites 2016 IEEE International Conference on Robotics and Automation (ICRA) , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control 2016 IEEE International Conference on Robotics and Automation (ICRA) , pages=

Reference 38

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source=arxiv_source observed=2026-08-12T00:48:43.800026Z digest=sha256:381495af84062c7b354d2870523824f9feecc6d1fec53056776764be3958cf4d

Observation fdbb37c7-128f-4300-99a0-637a8505ac81 · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in neural information processing systems , volume=

Reference 39

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source=arxiv_source observed=2026-08-12T00:48:43.805467Z digest=sha256:6dbbf99a854e73ff44631566da376fd6cadea0f47aaba8c95b78525a64b7ee2c

Observation 6346b7e8-e270-4e32-8b59-a358ed14bec9 · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in neural information processing systems , volume=

Reference 40

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source=arxiv_source observed=2026-08-12T00:48:43.812219Z digest=sha256:d89748eee49aa71f4048c189c6e6cab1de78f01b5a842ce12eb8acc6dc734dfe

Observation ba4fd107-1f6d-4719-9e68-1681dc3207e4 · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in neural information processing systems , volume=

Reference 41

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no resolver link, observed 2026-08-12T00:48:43.887581Z

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source=arxiv_source observed=2026-08-12T00:48:43.887581Z digest=sha256:2e5f7418aa6ba1cd56700523afacb092ff148e980f90d3abcab52bcb2cb4753d

Observation df9dfe30-75cb-421d-9aa3-6b57b8d080bc · outbound

This paper cites COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL

Reference 42

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source=arxiv_source observed=2026-08-12T00:48:43.931187Z digest=sha256:7713567f893d152d460c6716a2c2f1a011fc260922b855adae34c701da3c671d

Observation 8a59a100-b319-49e4-a15c-c3632c901f6c · outbound

This paper cites International conference on machine learning , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International conference on machine learning , pages=

Reference 43

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no resolver link, observed 2026-08-12T00:48:43.961083Z

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source=arxiv_source observed=2026-08-12T00:48:43.961083Z digest=sha256:fb79a9bad0c5ecc73f50cae468841acf0e009a9b182e5f88d2c6284f19530d47

Observation 866e7b91-cd58-4b2b-a212-1903425b48c6 · outbound

This paper cites International conference on machine learning , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International conference on machine learning , pages=

Reference 44

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no resolver link, observed 2026-08-12T00:48:44.014325Z

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source=arxiv_source observed=2026-08-12T00:48:44.014325Z digest=sha256:7da5a90769ec07e77ff190d756cedb8d741a7a7992371cea3fa9999c191d94ee

Observation 7de685ac-596f-42f6-8523-7251ead4d9af · outbound

This paper cites Never Give Up: Learning Directed Exploration Strategies.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Never Give Up: Learning Directed Exploration Strategies

Reference 45

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source=arxiv_source observed=2026-08-12T00:48:44.053445Z digest=sha256:f9e83f0a36dc4ad65bc12353212b339eec14ebac78be760ec9bdcaa4898d0612

Observation a5fc1bad-1600-422a-b509-cbab437ef210 · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in Neural Information Processing Systems , volume=

Reference 46

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no resolver link, observed 2026-08-12T00:48:44.059904Z

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source=arxiv_source observed=2026-08-12T00:48:44.059904Z digest=sha256:d063a3def8090a824b0471359d73575cd376cb63cc67787b6ca7ec152e3b07ac

Observation c938ee00-49a0-4d3f-9224-54bdcfd536ce · outbound

This paper cites MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization

Reference 47

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source=arxiv_source observed=2026-08-12T00:48:44.066449Z digest=sha256:1a2602f839516f7c70e1a4700e41d080dfae2734524286fac3df94a1556fead6

Observation a89cd13f-7dd9-4fb9-908d-3ed70fd1b6f9 · outbound

This paper cites nature , volume=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control nature , volume=

Reference 48

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no resolver link, observed 2026-08-12T00:48:44.072332Z

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source=arxiv_source observed=2026-08-12T00:48:44.072332Z digest=sha256:23555635ec25008e1f562382431391ec2deabb9d118ce61a65ad669ec4c095bf

Observation 1d7635b6-8253-4f0e-a4db-b7e2aaba4979 · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Proceedings of the aaai conference on artificial intelligence , volume=

Reference 49

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no resolver link, observed 2026-08-12T00:48:44.078290Z

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source=arxiv_source observed=2026-08-12T00:48:44.078290Z digest=sha256:c6cff2a768ca3677d7dc79cc8a86f9994b405d09ebdd14b1db4a0c2a4e5775e2

Observation a9715d7f-6d3d-4154-b07d-d133c6410890 · outbound

This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 50

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no resolver link, observed 2026-08-12T00:48:44.084451Z

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source=arxiv_source observed=2026-08-12T00:48:44.084451Z digest=sha256:a98244e781ece78bd2f422bbf55966151e3063e95dde5fb82382aa95e23e167b

Observation 0787e997-171a-42a8-bbc5-fb5348ab050a · outbound

This paper cites SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning

Reference 51

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source=arxiv_source observed=2026-08-12T00:48:44.091692Z digest=sha256:19aa20ae332b43c6c66ea5ada2cf5a3b827d914927d4feb2a9fb2fce465e7de9

Observation 5a1f2ae1-7053-4aa4-8249-1cf7dd2ab0c7 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 52

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source=arxiv_source observed=2026-08-12T00:48:44.098974Z digest=sha256:7e4a2dbd0595272bb3025a92d75795393c7699f2066c498f5f6796842eb42ae6

Observation a7848cbe-36cc-4fd7-b152-8a881c73140c · outbound

This paper cites Proceedings of Thirty Third Conference on Learning Theory , pages =.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Proceedings of Thirty Third Conference on Learning Theory , pages =

Reference 53

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no resolver link, observed 2026-08-12T00:48:44.105270Z

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source=arxiv_source observed=2026-08-12T00:48:44.105270Z digest=sha256:6f8c9688cab55730f819f48c1d02cc4d84e989eb6702fb93dd68123571b81e43

Observation 9ad6e267-1e30-4cd5-afe6-1f372f6bf4a4 · outbound

This paper cites Proceedings of Thirty Third Conference on Learning Theory , pages =.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Proceedings of Thirty Third Conference on Learning Theory , pages =

Reference 54

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no resolver link, observed 2026-08-12T00:48:44.111469Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-12T00:48:44.111469Z digest=sha256:4d5ffbe4db426ec36ecce74e422628365a8ae1c06dc55077cab539746a8aef45

Observation a1dac091-19f1-4d9f-935e-403de56169eb · outbound

This paper cites International conference on machine learning , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International conference on machine learning , pages=

Reference 55

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no resolver link, observed 2026-08-12T00:48:44.117463Z

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source=arxiv_source observed=2026-08-12T00:48:44.117463Z digest=sha256:c70541acb5a05bfb2be096413883a552aab31edbf538b5aa3f8337a13aa1c320

Observation 481ad074-4492-4aec-af65-831112326ca8 · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in Neural Information Processing Systems , volume=

Reference 56

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source=arxiv_source observed=2026-08-12T00:48:44.123263Z digest=sha256:d86132cd4e7699a908976331a7f05d936680cac747ccd2db2ab478405a5abfd1

Observation f7b6e6e6-1e94-46b4-8bd9-b07cfae3ea6e · outbound

This paper cites 2022 , url=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control 2022 , url=

Reference 57

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no resolver link, observed 2026-08-12T00:48:44.133001Z

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source=arxiv_source observed=2026-08-12T00:48:44.133001Z digest=sha256:5e49dcc4b417bebdeb9db33ac0061a3e17f0a65d145b7637233e209c3e5a48c6

Observation aa9f190a-1632-4f81-a78a-6eedaf61126d · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International Conference on Learning Representations , year=

Reference 58

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no resolver link, observed 2026-08-12T00:48:44.139018Z

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source=arxiv_source observed=2026-08-12T00:48:44.139018Z digest=sha256:ade1f7acf2e7ce4308158d2a38ce8efe0e2a3b8820a9d39fcf0ca03205077cc0

Observation 7159aae7-50a7-4cca-8517-d2148e9c84d2 · outbound

This paper cites Proceedings of the 36th International Conference on Machine Learning , pages =.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Proceedings of the 36th International Conference on Machine Learning , pages =

Reference 59

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no resolver link, observed 2026-08-12T00:48:44.146145Z

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source=arxiv_source observed=2026-08-12T00:48:44.146145Z digest=sha256:c5099329b5c6505cfca7612b20a28d0bba0647085ccf9296bac35594ac0d26d9

Observation 88b5cb1d-64b3-4d44-ac5b-b5468ef11ab0 · outbound

This paper cites Hyperspherical Normalization for Scalable Deep Reinforcement Learning.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Hyperspherical Normalization for Scalable Deep Reinforcement Learning

Reference 60

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no resolver link, observed 2026-08-12T00:48:44.152418Z

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source=arxiv_source observed=2026-08-12T00:48:44.152418Z digest=sha256:e1e808a83d990cecd37d07bf2db5a1b5a95cd34701f9b76a0e0bc94b41aa6b13

Observation 7db6e75c-242c-4144-bafb-a2ad44777109 · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in neural information processing systems , volume=

Reference 61

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no resolver link, observed 2026-08-12T00:48:44.157720Z

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source=arxiv_source observed=2026-08-12T00:48:44.157720Z digest=sha256:c418872ca6165ab8a80bec5eb125925e0b6819bbc713b02674c3b137279e19d1

Observation ae02036d-b966-4feb-a596-1ea638cd1e4e · outbound

This paper cites Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning

Reference 62

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source=arxiv_source observed=2026-08-12T00:48:44.162632Z digest=sha256:263291ab8ae18e08a39298a58b00643e58d197423b74f3d66e964dbf571b0ebe

Observation a694a16d-40ef-4fa2-a5a3-3939c02c198e · outbound

This paper cites Deep Transformer Q-Networks for Partially Observable Reinforcement Learning.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Deep Transformer Q-Networks for Partially Observable Reinforcement Learning

Reference 63

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source=arxiv_source observed=2026-08-12T00:48:44.168393Z digest=sha256:2c45e617ebf9598c1ed7a92175a5f2ff260eb0d2c8315ba1fd15c738d1dc3ca9

Observation 5f3d7c36-6692-421e-8af4-341ce0394667 · outbound

This paper cites Dual PatchNorm.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Dual PatchNorm

Reference 64

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source=arxiv_source observed=2026-08-12T00:48:44.173965Z digest=sha256:460e2a5e49bbc077e5773c31f4911a9f81748a901e43d4d893d6d2c6abf3bc0a

Observation 6e5d77ba-9bca-45a6-9596-99d4cc7545b6 · outbound

This paper cites Neural Networks: Tricks of the trade , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Neural Networks: Tricks of the trade , pages=

Reference 65

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source=arxiv_source observed=2026-08-12T00:48:44.179034Z digest=sha256:29918d104cad09a2c2730b7b892bb8919cf8e195a94c06b9c2ffba2626681293

Observation 89892540-148f-41c9-8199-3c3757fef08d · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in neural information processing systems , volume=

Reference 66

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no resolver link, observed 2026-08-12T00:48:44.196180Z

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source=arxiv_source observed=2026-08-12T00:48:44.196180Z digest=sha256:bfc7571efe911c92043907016ba15b0fdbf8b7b1cc6d7e97eb5d73933b34852c

Observation 656a39ed-5baf-4ff7-ae54-47192e079f5f · outbound

This paper cites Neural computation , volume=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Neural computation , volume=

Reference 67

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no resolver link, observed 2026-08-12T00:48:44.230892Z

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source=arxiv_source observed=2026-08-12T00:48:44.230892Z digest=sha256:43e2ababec3779ff7915906b469759ada924285af04710b0f05c845ec7ea2653

Observation eec08843-19a2-4d19-abf2-c7bcd0c556bc · outbound

This paper cites Proceedings of the IEEE international conference on computer vision , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Proceedings of the IEEE international conference on computer vision , pages=

Reference 68

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source=arxiv_source observed=2026-08-12T00:48:44.264251Z digest=sha256:a91b5627b4db7616a4c4944477d4603745a91a2ca3c4ac5753c1f23af1052a6f

Observation 7a978f6a-975e-4358-b266-4072dd8a47da · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in Neural Information Processing Systems , volume=

Reference 69

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no resolver link, observed 2026-08-12T00:48:44.320932Z

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source=arxiv_source observed=2026-08-12T00:48:44.320932Z digest=sha256:aac6c60b7b26afaddfa1afd36b80748221485c49fe601724e4ae58c9018fa160

Observation 5c1560b4-eaec-41f4-9ffd-8313f288d136 · outbound

This paper cites an unresolved cited work.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Unresolved cited work

Reference 70

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source=arxiv_source observed=2026-08-12T00:48:44.343414Z digest=sha256:e7c324549e9a58f11f0711e4539a84b4de0f7e8efcf50eb7a536426f8b9e6927

Observation 092922f2-095b-4ee8-8015-1802f4ca0e8c · outbound

This paper cites an unresolved cited work.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Unresolved cited work

Reference 71

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source=arxiv_source observed=2026-08-12T00:48:44.397908Z digest=sha256:4754565c1d118c3c4639afb8a24af128eb8b0322acf87cccb500161982b7d822

Observation 4a6eba36-c76c-475c-baf8-4355bc403b0b · outbound

This paper cites Loss of Plasticity in Continual Deep Reinforcement Learning.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Loss of Plasticity in Continual Deep Reinforcement Learning

Reference 72

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no resolver link, observed 2026-08-12T00:48:44.416722Z

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source=arxiv_source observed=2026-08-12T00:48:44.416722Z digest=sha256:bbe905e6c07680cb88ee9585c052aa22c8014249851e098b3c2f9325e25c6a35

Observation 3083b5e7-f1ff-4425-bf69-49df7a39c65b · outbound

This paper cites international conference on machine learning , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control international conference on machine learning , pages=

Reference 73

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source=arxiv_source observed=2026-08-12T00:48:44.422601Z digest=sha256:47c02ef51934c935858a3109404fb291ad8cb0e8ae849bab64ff0c85f453be5a

Observation cc5631e0-0328-4c37-8697-398b072ed5b0 · outbound

This paper cites What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study

Reference 74

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source=arxiv_source observed=2026-08-12T00:48:44.428924Z digest=sha256:db937b4a46bb28860a79b098ff7e867b82da702ae5f823b80745d43874da1196

Observation f1048067-a307-4dd2-aefb-2b27646ef0d1 · outbound

This paper cites an unresolved cited work.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Unresolved cited work

Reference 75

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source=arxiv_source observed=2026-08-12T00:48:44.434101Z digest=sha256:57420bbe333028ff7244b35db4a32e2149b96b9585f8f249f5e1b4e168d0dbc1

Observation ac1316a7-b1ff-449e-87d7-9f2ebf58db65 · outbound

This paper cites Efficient Deep Reinforcement Learning Requires Regulating Overfitting.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Efficient Deep Reinforcement Learning Requires Regulating Overfitting

Reference 76

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source=arxiv_source observed=2026-08-12T00:48:44.439522Z digest=sha256:86d6251f023117a97fce1562b607237d0ccc492ac307d314756974925489c641

Observation 48e6131f-5c0d-4be8-8b2f-04f73cc9ef8e · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control The Twelfth International Conference on Learning Representations , year=

Reference 77

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source=arxiv_source observed=2026-08-12T00:48:44.446555Z digest=sha256:8dc02634532f980832a38daa385bfccca8c99c9a1923e655061cea8fc77816a3

Observation 3621492d-7eb3-494d-ac9d-af93697f62c6 · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control The Eleventh International Conference on Learning Representations , year=

Reference 78

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source=arxiv_source observed=2026-08-12T00:48:44.454142Z digest=sha256:6a12a1817dda842cd61018daf0e899ea7a06adb0ea88cb3204c9d4f8810682cf

Observation e74d3c0f-1377-4b60-bc1c-b54ecebacb77 · outbound

This paper cites International Conference on Machine Learning , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International Conference on Machine Learning , pages=

Reference 79

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source=arxiv_source observed=2026-08-12T00:48:44.459722Z digest=sha256:f23bff14297fe4e04e38f001d220015b17fa4393af6160b9b347c98654dec278

Observation 8c078f70-f851-4f6d-ba70-56c10cbd7c74 · outbound

This paper cites Maintaining Plasticity in Continual Learning via Regenerative Regularization.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 80

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source=arxiv_source observed=2026-08-12T00:48:44.464619Z digest=sha256:df395697478891ff1161778bbc343b7187cfcbe5af931a30ad530a61c8370f90

Observation b1338b8b-881d-4944-bd49-7cc6e514922c · outbound

This paper cites Maintaining Plasticity in Deep Continual Learning.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Maintaining Plasticity in Deep Continual Learning

Reference 81

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source=arxiv_source observed=2026-08-12T00:48:44.469730Z digest=sha256:df49321ddc04647a56e40bfab1b7ce2f376280b783c9de0d0393bdf4da282f75

Observation a540b251-0436-4bb3-b40a-c6a3b011decd · outbound

This paper cites Directions of Curvature as an Explanation for Loss of Plasticity.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Directions of Curvature as an Explanation for Loss of Plasticity

Reference 82

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source=arxiv_source observed=2026-08-12T00:48:44.475182Z digest=sha256:2df1720166bab67ce98f3f66a14f1793b2b8a1b0cd3834135d8b30346040ed88

Observation 42e671c6-30d9-48c4-a982-7ee30ad37c4b · outbound

This paper cites Continual Learning as Computationally Constrained Reinforcement Learning.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Continual Learning as Computationally Constrained Reinforcement Learning

Reference 83

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source=arxiv_source observed=2026-08-12T00:48:44.481432Z digest=sha256:c7a2795967ff8f9876233b82b7f5f4348696c1dd8bbad36f8939c3e793badcac

Observation 5b658bb8-0a54-45f2-bffb-f9ae7a1703dd · outbound

This paper cites Proceedings of the thirteenth international conference on artificial intelligence and statistics , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Proceedings of the thirteenth international conference on artificial intelligence and statistics , pages=

Reference 84

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source=arxiv_source observed=2026-08-12T00:48:44.487020Z digest=sha256:57e70c728dbc197d3e926cb09ab7186269d87eab5ece975a87b85cfed382ecda

Observation 939ebcf0-c446-41c6-addd-c322d521372b · outbound

This paper cites Fantastic Generalization Measures and Where to Find Them.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Fantastic Generalization Measures and Where to Find Them

Reference 85

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source=arxiv_source observed=2026-08-12T00:48:44.492251Z digest=sha256:b7d27e1c39450325ce79700f69dbf4885333c38ba2504da4f00452566b56673d

Observation e431a621-eb13-49e9-b1ca-0682e4555641 · outbound

This paper cites International Conference on Machine Learning , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International Conference on Machine Learning , pages=

Reference 86

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no resolver link, observed 2026-08-12T00:48:44.532328Z

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source=arxiv_source observed=2026-08-12T00:48:44.532328Z digest=sha256:0ba7fb24d66ebcb8e328681fa073d9ce7d86c26a97a6171d85dfab1847da8bab

Observation 03aa83d4-fd71-4c1d-b067-36639e57df63 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 87

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source=arxiv_source observed=2026-08-12T00:48:44.565673Z digest=sha256:a551e10fdb40d8e138dcb0ab1ad086b4228263c7e5bdb24c73cbd03ed4c0ff8b

Observation 01a53970-1303-4af8-9233-dd8b5f0e2875 · outbound

This paper cites Proceedings of the IEEE conference on computer vision and pattern recognition , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

Reference 88

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source=arxiv_source observed=2026-08-12T00:48:44.594067Z digest=sha256:3c016dadb5803001745a468777baafa2315ab35bfea45fa8b8e4dc7e9a465a6e

Observation 0733bf70-d8c7-4548-952e-dc4d006018cb · outbound

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

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 89

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source=arxiv_source observed=2026-08-12T00:48:44.632705Z digest=sha256:63aa8e23c10cae20cbb2082207d002d81eeb29bae7fd076fb0a72be1146e28dd

Observation c9b395ce-24c1-40c4-8e06-7e113214e74f · outbound

This paper cites GPT-4 Technical Report.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control GPT-4 Technical Report

Reference 90

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source=arxiv_source observed=2026-08-12T00:48:44.662535Z digest=sha256:5fbb82641a26ec94a2dac40f7f0ba22e2e56cc77667e85da12d77f8ac74d178d

Observation ce4d376a-584b-4940-b510-3a5af02928f5 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Gemini: A Family of Highly Capable Multimodal Models

Reference 91

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source=arxiv_source observed=2026-08-12T00:48:44.699620Z digest=sha256:a6eda5361cb08734fe0ccf7ad0b12900b8eda5c8c412dc1620210070add767b9

Observation 250fcb3d-91fb-4ccb-9fa3-0cf71b0967b9 · outbound

This paper cites 2009 , institution=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control 2009 , institution=

Reference 92

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source=arxiv_source observed=2026-08-12T00:48:44.705590Z digest=sha256:83e6be048129d2b8c107211dd2e418929e23bb58486be3464ef03cf79a1d4e5f

Observation 11008eb8-e879-48f1-a295-7a029984a27e · outbound

This paper cites http://yann.lecun.com/exdb/mnist/ , year=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control http://yann.lecun.com/exdb/mnist/ , year=

Reference 93

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source=arxiv_source observed=2026-08-12T00:48:44.710831Z digest=sha256:214dc0eda37cf44f07f299e84f0f7034487bf88673efcba61d779bd28462a791

Observation 171825c9-837a-4b80-bdd9-d3643d880535 · outbound

This paper cites Decoupled Weight Decay Regularization.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Decoupled Weight Decay Regularization

Reference 94

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source=arxiv_source observed=2026-08-12T00:48:44.715365Z digest=sha256:d22a5cf7b0d4de055b748f7db0f795d0da0e11702b0d5187bae0a779b721d01d

Observation 57338cf0-9981-44cc-baf3-464c9f27ba0d · outbound

This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 95

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source=arxiv_source observed=2026-08-12T00:48:44.721218Z digest=sha256:df2f0cd4d648b64619521439ec21aed92bade0f45fc1a82b02e8e723b4ac4bd1

Observation da8aa900-e90f-42fa-ae55-5bb26bb1c785 · outbound

This paper cites International conference on machine learning , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International conference on machine learning , pages=

Reference 96

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source=arxiv_source observed=2026-08-12T00:48:44.726473Z digest=sha256:c089232069aef6a6a9242d82303a3346a7438aafd5d4e9e2b97d2a00e7da7831

Observation e41de224-c6e4-4d61-96a2-cfd42fd61d95 · outbound

This paper cites Layer Normalization.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Layer Normalization

Reference 97

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source=arxiv_source observed=2026-08-12T00:48:44.731432Z digest=sha256:2959a878c96c254df599e6e0a611566022803b1f38c7a14f9045eea64c97cd20

Observation 2ee77a5a-2619-40d5-a0fd-0aa89e696a3b · outbound

This paper cites Resetting the Optimizer in Deep RL: An Empirical Study.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Resetting the Optimizer in Deep RL: An Empirical Study

Reference 98

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source=arxiv_source observed=2026-08-12T00:48:44.737121Z digest=sha256:607d7f93f656bb0a068841261674460553249adf384755713403fb35f7c2aee4

Observation c087bb13-2e33-4eb3-9917-a72127cbb8a4 · outbound

This paper cites International conference on machine learning , pages=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International conference on machine learning , pages=

Reference 99

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source=arxiv_source observed=2026-08-12T00:48:44.743105Z digest=sha256:bae017d6ac0351a43541d1f437b678f9be1825a33a603ce2fa836da7ea1cb8ad

Observation 99a9f70a-aaba-457c-88c4-3832fdb5da5b · outbound

This paper cites International journal of computer vision , volume=.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International journal of computer vision , volume=

Reference 100

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source=arxiv_source observed=2026-08-12T00:48:44.749058Z digest=sha256:e0c30ab0e02a7b5d4de0489e96a95955b4ea048b95a007997e30a3847e8e7b7d

Pith citing papers

No inbound Pith citation observations are available.