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Paper Citation Record · LEDGER

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning

As of 14 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 0 inbound Pith citation observations for arXiv:2412.15517.

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

pith.paper-citation-record.v1
2412.15517 v1

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:24:46.222224Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

56 of 56 outbound references displayed

  • verified exact4
  • verified fuzzy9
  • unresolved43
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 25e7e7e8-18d7-4416-95d1-f1b45d4fc851 · outbound

This paper cites Emergent Tool Use From Multi-Agent Autocurricula.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Emergent Tool Use From Multi-Agent Autocurricula

Reference 1

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no resolver link, observed 2026-08-11T11:24:45.981209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:45.981209Z digest=sha256:33c50f21d917a241ece1e25e16434b10a8256e64c22a5ac9e6cb0c600e9bf453

Observation 8e772463-11d2-4801-a311-9383d77657c4 · outbound

This paper cites Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning

Reference 2

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no resolver link, observed 2026-08-11T11:24:45.986379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:45.986379Z digest=sha256:446c4618fe7a329183f91a2324842703b73ea3fb6c82ed0ddb563999a63addcd

Observation f61fce6d-5d2f-4e31-9a1f-61dcbc2c72e2 · outbound

This paper cites Exploration by Random Network Distillation.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Exploration by Random Network Distillation

Reference 3

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no resolver link, observed 2026-08-11T11:24:45.991128Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:45.991128Z digest=sha256:b3cb010132114b32047b1ac8b3ba061bafe19e5e3ca4d30b84a0ceaf3f445479

Observation bfeda0eb-ffc0-4910-b591-546036d2d5a4 · outbound

This paper cites C.; Nunzio, L.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning C.; Nunzio, L

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-11T11:24:47.028013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:45.995854Z digest=sha256:679c452d14252beecaaf4ade8f1cf89eaa8e600381b73710739bab24332e30c0

Observation fd24b1da-968e-4817-94d3-214cfe12ca0f · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 5

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raw_fallback, observed 2026-08-11T11:24:47.014162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.000542Z digest=sha256:f25d438b292a2179657844cb716f9ca0a16004fcabbbe6b6354611e815c708f2

Observation 37668738-3a4c-46ed-850b-7cd90e5ad63c · outbound

This paper cites L.; Hernandez-Leal, P.; Kartal, B.; and Taylor, M.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning L.; Hernandez-Leal, P.; Kartal, B.; and Taylor, M

Reference 6

Resolution
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raw_fallback, observed 2026-08-11T11:24:47.000598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.005023Z digest=sha256:59e82a6f640f287d3d35d2a6763e050d28f3b915a35e8dc0a0dc6cf72f16102b

Observation f027e80f-152d-4b41-91f0-a8f2663a1959 · outbound

This paper cites Novelty-based Sample Reuse for Continuous Robotics Control.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Novelty-based Sample Reuse for Continuous Robotics Control

Reference 7

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verified exact
local_arxiv, observed 2026-08-11T11:24:46.495018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.009881Z digest=sha256:ea429ea84f9c0f7839a623ed4649480ba52ffad6edd323f0b0d85309a7e060e4

Observation 3717e4a8-556f-4cce-8090-cab1da4a4a04 · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 8

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.014365Z digest=sha256:dc2771fff839110366c0f978e10412f82e1ae379f4b853c7a64914e2a426c275

Observation b910d9c5-5b01-4d9e-b3ce-e37c6c71c20b · outbound

This paper cites Diversity is All You Need: Learning Skills without a Reward Function.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Diversity is All You Need: Learning Skills without a Reward Function

Reference 9

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source=arxiv_source observed=2026-08-11T11:24:46.018845Z digest=sha256:5bd9abd740fe263df897eb2677216e8da2615d04fce15c6062e173e0c363897d

Observation 02f251d3-7e7b-482d-9331-e47a93610cf8 · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 10

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raw_fallback, observed 2026-08-11T11:24:46.972642Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.023354Z digest=sha256:9d0be50b405f5061feaa08c35c351bbc1b561ef4ae98d179ef46e93e027c8cf4

Observation 44fcd0eb-8c1b-4b4f-9bb4-724cf2a71e92 · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 11

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.027652Z digest=sha256:7bc4ae16391f3efa14d756510290e9f32190cf46605a961f9bea9a8ac67d496f

Observation 812cd65c-4f52-4488-b7e4-e1deb7bcc7df · outbound

This paper cites S.; Campbell, J.; Stepputtis, S.; Li, R.; Hughes, D.; Fang, F.; and Sycara, K.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning S.; Campbell, J.; Stepputtis, S.; Li, R.; Hughes, D.; Fang, F.; and Sycara, K

Reference 12

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verified fuzzy
raw_fallback, observed 2026-08-11T11:24:46.945719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.031701Z digest=sha256:98a5884bb9254700df282317f9d41f274e0e7c0b5bd97bee1ec5eeca2055ac1a

Observation 996a60cd-3c6c-4aed-b796-fbaccc4c8ae5 · outbound

This paper cites F.; and Yamins, D.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning F.; and Yamins, D

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.035720Z digest=sha256:48eb58081ae920a4b20fdebe8bfa42dcd9dde7ac268baef0d71bae560363e60f

Observation d0fa5aa8-f8a8-496e-af06-3fdff2af6e6b · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 14

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.039716Z digest=sha256:21b82c513355be4cd2bc2d07f914f3fe202df78fe0ad7a6d4155f2cb4d481e6d

Observation 5693ad38-a656-4674-9f5e-bec5cb8fe837 · outbound

This paper cites A.; Wu, H.; and wei Liao, S.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning A.; Wu, H.; and wei Liao, S

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:24:46.902802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.043693Z digest=sha256:39ad7b8805ee38caf07d86e9757cb5159163ada3b09383fc16a1967ca17b5707

Observation aefe3b68-1cec-4864-b1a4-2cfc1a4f4025 · outbound

This paper cites Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL

Reference 16

Resolution
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local_arxiv, observed 2026-08-11T11:24:46.458171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.047911Z digest=sha256:801cd49c95495c998da1dc213c5fabb984fa6ec2c86a1fbd05dcf9ebf491e246

Observation 83f62352-0b06-40bf-8cd0-650cb4b373f2 · outbound

This paper cites Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning

Reference 17

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local_arxiv, observed 2026-08-11T11:24:46.436976Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.052610Z digest=sha256:020a2b85dae2d73e278f8baa6b73e10871bd648d5b5ad4c5f5e5ca1911e7b3dc

Observation 00559195-64df-4911-a45d-e31e7990a8ae · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 18

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.057057Z digest=sha256:15c843a8da2736f5a22495c9d6bd0a926225b9f2d521936f52948837eada4419

Observation 6486a17b-9d7f-476c-bbed-5f91aab8c787 · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 19

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.061168Z digest=sha256:c22a96836897002dc876ae23644fefd3b818af4613a5d355fb5067fd3a4e53a5

Observation b355d4a7-c892-4ae9-914d-4c32317d145d · outbound

This paper cites Emergent Multi-Agent Communication in the Deep Learning Era.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Emergent Multi-Agent Communication in the Deep Learning Era

Reference 20

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source=arxiv_source observed=2026-08-11T11:24:46.065586Z digest=sha256:947e4a22f037614859a83ccaf4f19a1656cf4fa9bea14e0d959da6e1acb1f62d

Observation 3c95e554-4b81-4f29-bea0-784eabe0b72a · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 21

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no resolver link, observed 2026-08-11T11:24:46.070357Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:46.070357Z digest=sha256:7761123f51050fb380eaee297c3bc8b6dc218f4cf80769fd04ef54146282d2e2

Observation 8083be86-8b80-4c21-b500-0b7324820e7e · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 22

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source=arxiv_source observed=2026-08-11T11:24:46.074336Z digest=sha256:8602bc8ca92a9cdf9d14d25fec23a94bcdfe797ef9da569d3b3657b29016f14d

Observation dff8c0fd-081b-472f-b251-3317bcfe5cf9 · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 23

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.078496Z digest=sha256:0dd4a0c8d57c5fdad890c5976db237124dfe9a467b9188c56784f7aaf5b188d5

Observation 178a22d7-f949-4f45-b2f3-34ac4c0b3e90 · outbound

This paper cites Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning

Reference 24

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source=arxiv_source observed=2026-08-11T11:24:46.082731Z digest=sha256:b2cdc8737d5d2e7935c38c422b2493a997084aab8366bd1b79baee65c43e7358

Observation d62dc40a-477b-4e68-a203-c4710d8158ab · outbound

This paper cites I.; Tamar, A.; Harb, J.; Pieter Abbeel, O.; and Mordatch, I.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning I.; Tamar, A.; Harb, J.; Pieter Abbeel, O.; and Mordatch, I

Reference 25

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no resolver link, observed 2026-08-11T11:24:46.087415Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:46.087415Z digest=sha256:8bcb283f437cb2e2694ee425c4fd76d096af3a67cdf52ed97325ba6d474c16ca

Observation e011fbd9-37f8-44e6-a90d-f095f295e66f · outbound

This paper cites Cross-Domain Policy Adaptation by Capturing Representation Mismatch.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Cross-Domain Policy Adaptation by Capturing Representation Mismatch

Reference 26

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:46.091690Z digest=sha256:14a0bebc3b16379a6954f31164ec05ecbd002031651e425c5e367bf6e93293d3

Observation 8ad93ec9-0bd2-4abe-8ecf-8c97dc316870 · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 27

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.096004Z digest=sha256:0b679da560338cb72d6102f29d45fe904ceacf1c63a834387ecd7ef8212f84f2

Observation 12cdf508-ee27-43fe-be25-90a1f3f6c607 · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 28

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raw_fallback, observed 2026-08-11T11:24:46.796934Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.100243Z digest=sha256:b8ada32ae68e30b81636ff4b11f0fa2ae0b63c5c8efaa9be02310d8c9836e3bf

Observation b7f15dee-4cea-4ed6-9e94-029b42a6f136 · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 29

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.104418Z digest=sha256:718b4a6feaccbb5184cea0200fba10a9e7c7f7de5c871da5e93afe9d11fdecf4

Observation 5510c82c-4415-4929-bf14-83f5de7030e1 · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 30

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.108663Z digest=sha256:c54910da366ae8c6ba34ac506e5c864e7c80d5981de514bf20855f28c29c3eb9

Observation 4697a6ca-9c16-499b-a690-9eba2dfa1a06 · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 31

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.112690Z digest=sha256:0e16d01122f4327328d0eff91372ae87d1a850226de53af0f803efcdd821a2ca

Observation 2129420a-5e33-4a0d-bf4c-582f8127884a · outbound

This paper cites A.; Amato, C.; et al.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning A.; Amato, C.; et al

Reference 32

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no resolver link, observed 2026-08-11T11:24:46.116745Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:46.116745Z digest=sha256:ca05b4b092b0a936b0b0ae1591b0da139a2eeee6237a99edd475833a81bc75b5

Observation aa791ded-d3c4-4d1f-a372-20f943ec26bb · outbound

This paper cites A.; and Darrell, T.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning A.; and Darrell, T

Reference 33

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no resolver link, observed 2026-08-11T11:24:46.120917Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:46.120917Z digest=sha256:fce2d91c903dedf316ee63fc04569e58025c762daae444539a0d405a6584c7a8

Observation 9655bb13-acb9-4db0-a1f8-3474c44b2a00 · outbound

This paper cites M.; Liu, C.; and Zhou, B.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning M.; Liu, C.; and Zhou, B

Reference 34

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raw_fallback, observed 2026-08-11T11:24:46.723039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.125055Z digest=sha256:b74c038c0be217a32ab14b3e0bb9cdcb3304ad6c4c19b79b35b63c3cd797d5e0

Observation 9f92f7de-33d3-448d-8ca2-74935f1cf468 · outbound

This paper cites S.; Farquhar, G.; Foerster, J.; and Whiteson, S.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning S.; Farquhar, G.; Foerster, J.; and Whiteson, S

Reference 35

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raw_fallback, observed 2026-08-11T11:24:46.709304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.128954Z digest=sha256:dc09d3e15b6af96b30b0dc9e7e553b86cbf3cbbc0147366334ed7d6fb990e21b

Observation 90d4f63c-5738-4f9e-a816-825aaa2a9fda · outbound

This paper cites The StarCraft Multi-Agent Challenge.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 36

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no resolver link, observed 2026-08-11T11:24:46.132980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:46.132980Z digest=sha256:55ec772aa03bb5f0180b68c37a38f402b9f15942b78509720dfa5c56674f85fd

Observation 66769412-ae89-43c8-8c0a-a6a987f1f503 · outbound

This paper cites J.; Hostallero, D.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning J.; Hostallero, D

Reference 37

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no resolver link, observed 2026-08-11T11:24:46.137063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:46.137063Z digest=sha256:e595dce8061edd4c282fcef094362c828c6c2682e20051b3858cc453cdaaf0a2

Observation 1670c04f-b623-44a4-8911-efebcbcf730b · outbound

This paper cites Value-Decomposition Networks For Cooperative Multi-Agent Learning.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Value-Decomposition Networks For Cooperative Multi-Agent Learning

Reference 38

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unresolved
no resolver link, observed 2026-08-11T11:24:46.141230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:46.141230Z digest=sha256:de48ca73678c5e2a485e01a8ec77390be8c2593f0fcebba2b8e6778fa2122e2c

Observation c8d25a6a-efc1-4f46-871d-7711f93711a9 · outbound

This paper cites S.; Barto, A.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning S.; Barto, A

Reference 39

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verified fuzzy
raw_fallback, observed 2026-08-11T11:24:46.685745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.145819Z digest=sha256:99ab147299f02b7c5a94ee51081e76c13035b7b5c0e506874559700c23cb8975

Observation 7a698a58-8124-4787-99a2-99591eec6a4e · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 40

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raw_fallback, observed 2026-08-11T11:24:46.671388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.150128Z digest=sha256:2f3e4b8b7e5aa5722c7bfd9fa5002533b7682b0f43debdb9789469f35ca78ac6

Observation e29afd53-c1fd-4f6d-bcc5-ca4a79501076 · outbound

This paper cites M.; Mathieu, M.; Dudzik, A.; Chung, J.; Choi, D.; Powell, R.; Ewalds, T.; Georgiev, P.; Oh, J.; Horgan, D.; Kroiss, M.; Danihelka, I.; Huang, A.; Sifre, L.; Cai, T.; Agapiou, J.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning M.; Mathieu, M.; Dudzik, A.; Chung, J.; Choi, D.; Powell, R.; Ewalds, T.; Georgiev, P.; Oh, J.; Horgan, D.; Kroiss, M.; Danihelka, I.; Huang, A.; Sifre, L.; Cai, T.; Agapiou, J

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:24:46.655613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.154364Z digest=sha256:03d5eb2547777afdf60f3ba20440178cad7e610c12ad1b219763d7455bba0786

Observation 009db5db-a19c-49cd-985c-40e54e14c5e6 · outbound

This paper cites QPLEX: Duplex Dueling Multi-Agent Q-Learning.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning QPLEX: Duplex Dueling Multi-Agent Q-Learning

Reference 42

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unresolved
no resolver link, observed 2026-08-11T11:24:46.159464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:46.159464Z digest=sha256:f7fde3b476bd6ece97f6ba62b43f2d466638c81b72927bef09fde8290f270168

Observation 3a10fd97-88e2-45dc-b737-3ecbd68b3c39 · outbound

This paper cites ROMA: Multi-Agent Reinforcement Learning with Emergent Roles.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning ROMA: Multi-Agent Reinforcement Learning with Emergent Roles

Reference 43

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unresolved
no resolver link, observed 2026-08-11T11:24:46.163828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:46.163828Z digest=sha256:49ebc82bd6c3f480cb66a21f015ede51c48bc94fb19da9ccfcc2c200e8c2051d

Observation fff1348b-0ea2-48ae-8546-c54b1dbede53 · outbound

This paper cites RODE: Learning Roles to Decompose Multi-Agent Tasks.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning RODE: Learning Roles to Decompose Multi-Agent Tasks

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-11T11:24:46.169084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:46.169084Z digest=sha256:b2745579b0200f31b7700d30740bbfbff67e8f4e67d719c04e85670fb54a21d3

Observation 38592a44-c80f-47ab-98d4-d6f071645fd3 · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 45

Resolution
unresolved
raw_fallback, observed 2026-08-11T11:24:46.641146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.173333Z digest=sha256:7b1b0fe34ab7056f8eddaf6cb9c469b55666a7a1e1e1686fa3edd770a29a07cd

Observation 5caa6a4f-5020-4aa6-ad44-045abb8e20f6 · outbound

This paper cites Action Semantics Network: Considering the Effects of Actions in Multiagent Systems.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Action Semantics Network: Considering the Effects of Actions in Multiagent Systems

Reference 46

Resolution
verified exact
local_arxiv, observed 2026-08-11T11:24:46.308726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.177556Z digest=sha256:b19053dcedfbcfd095038b21b39031d1038d9b9563edd8b798a27bbd5db93ca2

Observation 0fa6a989-fb70-48b7-90e3-dd78109bbf93 · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 47

Resolution
unresolved
raw_fallback, observed 2026-08-11T11:24:46.627011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.181834Z digest=sha256:f9594af9036a6bf9da304361d5c50ce902bc82713f11f3e95faf549c32840430

Observation 6cc2ed05-2e15-4c00-8f15-8fc816e3c8f6 · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 48

Resolution
unresolved
raw_fallback, observed 2026-08-11T11:24:46.612925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.185744Z digest=sha256:0e51314ea6ddd6bcca4c7cea0502a3220b5ae5fca8de6a2a775f84badec9a78c

Observation 22eb225d-146d-46a0-b10b-0008df1ac33e · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 49

Resolution
unresolved
raw_fallback, observed 2026-08-11T11:24:46.599183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.189866Z digest=sha256:e22e30b22fd1026f1b96776f92eba9ca6dca930bea9789ca0786435a8fb71283

Observation 8f552d30-c97f-4eb1-94de-9a26bdbfc608 · outbound

This paper cites Exploration and Anti-Exploration with Distributional Random Network Distillation.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Exploration and Anti-Exploration with Distributional Random Network Distillation

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-11T11:24:46.194807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:46.194807Z digest=sha256:4b74b9c1f9bef6da4861d03c97e508e135ecdd00a05b86187d59999faad2c6a6

Observation 354f4261-23e8-4c00-8711-f5669f58fd02 · outbound

This paper cites Qatten: A General Framework for Cooperative Multiagent Reinforcement Learning.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Qatten: A General Framework for Cooperative Multiagent Reinforcement Learning

Reference 51

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unresolved
no resolver link, observed 2026-08-11T11:24:46.199179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:46.199179Z digest=sha256:06fe732bb2d170a26361b4439ea7af28ad34c138aa0e70fbd2847646d44e3848

Observation 649950b9-385e-471f-8917-3ce5aebc5051 · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-11T11:24:46.203593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:46.203593Z digest=sha256:0c5c860cae32e2f625a9acbc3570618877894c3287e21c6bf49405564e2eae74

Observation 9224184d-c456-46e5-9d2b-d4c94d5ecc65 · outbound

This paper cites an unresolved cited work.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Unresolved cited work

Reference 53

Resolution
unresolved
raw_fallback, observed 2026-08-11T11:24:46.574979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.207808Z digest=sha256:07bfad7f72034808f207dc19a38f8bff522ea80e808ecd2071a5ea12a0070024

Observation ab656809-5506-466c-9ceb-a1bfafcdec4c · outbound

This paper cites Hierarchical Reinforcement Learning for Multi-agent MOBA Game.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Hierarchical Reinforcement Learning for Multi-agent MOBA Game

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-11T11:24:46.212541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:46.212541Z digest=sha256:e025a51fe9b955a2a516f7f0e3e759d59936088c4b3b9b0cdd2f388a7384be80

Observation e8080c15-c9c6-412f-9f18-4a518e5de405 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning , " * write output.state after.block = add.period write newline

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-11T11:24:46.217009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:46.217009Z digest=sha256:f09cdbb12a7f000cc72a822c3a61dba6599c191525c2f2e0e2f4a6afc1d00eb2

Observation f5bbc259-b6ce-403c-bdd7-cf09a3685537 · outbound

This paper cites write newline.

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning write newline

Reference 56

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unresolved
no resolver link, observed 2026-08-11T11:24:46.222224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:46.222224Z digest=sha256:273b125f38fe19862ebd9c7f4e65811bbd5b8f08e960509c98674d093f9f90a5

Pith citing papers

No inbound Pith citation observations are available.