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Paper Citation Record · LEDGER

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

As of 18 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 2026-08-17T06:30:58.91139+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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:45.981209Z digest=sha256:0e8422247d5ee8d3bb09a94a4dd3dbf01cab5099c6deaede6dc07a0e8627fc2f

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=sha256:9b87d2d3928ea7d9b3eba1d1423320fa658a08284012091074f7bc98514700c2

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=sha256:9598cde93c1c643e951974176ba737b34b95f3e8cf6e216b00b4df54612d9442

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:45.995854Z digest=sha256:115212dfd1d25670c5919766367bf79551dabbb85ea4f1e3b16554fbbc08ce53

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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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-11T11:24:46.000542Z digest=sha256:c9ca8d4fa6fbbb78f93efaae0d0057dafa25e529f634c54f20b7e5a3ab0050fe

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
verified fuzzy
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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.005023Z digest=sha256:80a95a0d65f7156162a8dfa758d568c7605f21da0b8d76d86024ad003ed355c0

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.009881Z digest=sha256:39f93c263f1b0443fa6448c1461c1be72fa19312a0c1352eaedf8649eb405680

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.014365Z digest=sha256:86f82a1d6d1c0461dd4469b3c173833d4b335dc2dda379537f2e82b4d5bba3ed

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-reported events for the cited work

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source=arxiv_source observed=2026-08-11T11:24:46.018845Z digest=sha256:c588f9f6811c3e3594fd10c1dfb7034ef89bbd672f0304cddbc03fe34698981e

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

source=arxiv_source observed=2026-08-11T11:24:46.023354Z digest=sha256:3103d800363551318e65ac1a3d3a5ee100959dce01e6d068e24ca1556d15a322

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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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-11T11:24:46.027652Z digest=sha256:f975be946da145ae59eb52214bfc9dd6f8f9b2b7211047a21a0db4a7c17ee29f

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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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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.031701Z digest=sha256:92294a8999850c86312bfce87ba4de0ecf8bf77744d2325df3b7363c103d931e

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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raw_fallback, observed 2026-08-11T11:24:46.930192Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-11T11:24:46.035720Z digest=sha256:3d0e05a6f77ebd92d634079795c6c9006d693f195c8d6d1f3d8d31cd7edd3ec7

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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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-11T11:24:46.039716Z digest=sha256:d108bcedbaff87a3e680a1b30113f220ef133100329f4acbdfb385e717894478

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

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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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.043693Z digest=sha256:75c1b2cb84d8d63a7c4a7ae5bf0a743d1fc31aa64d57fdc324234bc28db61ef9

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

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verified exact
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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.047911Z digest=sha256:cc53e36eb8ae6d6ffafa9b84f40b7d7f3c161b633e28bcf57c99e35e3f642a49

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.052610Z digest=sha256:41ef15acba021ec22b3d071b0e27f1134e26ebe9a84b1ce39af2b2f09ec196d4

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.057057Z digest=sha256:82809780d2672034d2cae83f34e911cfc916e6ee9cea981856616f49e236d141

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.061168Z digest=sha256:abbefa45c9039a1cd322eb56af0f418fcdd339d36dc8089217a1bdfa24f08e34

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=sha256:0e418fec0fedc62476b05a06ffcbe724bf881836807a78efe7453ec2dc724146

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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source=arxiv_source observed=2026-08-11T11:24:46.070357Z digest=sha256:9cabed218406b76dc6a420e84aac2c94c9737f64fab702b9ecae742c5117d4dc

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

source=arxiv_source observed=2026-08-11T11:24:46.074336Z digest=sha256:47393ae5b5214cdcf8f37b54d25d5ee06d3270f163b6cc39b4afac86a08ae47f

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.078496Z digest=sha256:a1727c6fd8ce9fa6afaf34eb2e65e864bbafd9699e89e192d17e71bc3b52fb06

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=sha256:73ce5de29d77405239e7e5805fc03e58f758625c9fcb00019240221a48a0c579

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:ea4aab2ce346dede1745621f0b40071326039ea1a199abf84ad25c14d791c5f8

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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no resolver link, observed 2026-08-11T11:24:46.091690Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:46.091690Z digest=sha256:eec8b44fe2f76ea36f00cffba538b4460ec6caadcad0b5c46cf3fdd2ed1d71a4

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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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-11T11:24:46.096004Z digest=sha256:deb4733b3a4c37b37a224226d46f0d7627d5ea78fd92831f5ff572b91086c33d

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.100243Z digest=sha256:184b16815207be199fa9b7543897ada03f372465ca06881a0e317f6264dd9b4d

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.104418Z digest=sha256:82d2be572f7feabd24c1493cce11017c96f0c14594793052a7b02fb7fb043580

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.108663Z digest=sha256:839ad5626448fa391887a42d479bdcda74f9eded8a971715383625000effbc38

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.112690Z digest=sha256:fd669a14829a0df3f31546e9e5974c97cdf15d6b5684cb389e5d22ef12949da0

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:46.116745Z digest=sha256:19f281fb102aef0118bcfc0e3cbcd304857dff880a8d558ca80965aae69c7835

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=sha256:4fbaa8521833bbcc3a7d1490ecb1ca0e56be961d4b17f10a24b280c26e5910ef

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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verified fuzzy
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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.125055Z digest=sha256:8ea9ed7bb8d97e023f7735d24504f0d3f2a83879776df8df6782bd9fd47e2343

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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verified fuzzy
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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.128954Z digest=sha256:521f9510dc458c96dab5bf8e39a02f35af61ae5cd9f29b68b0fc78e03995bf80

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:d284b1c82ce10ef9bc47c0d5fb83885a32bb493916e271f12299fcf3487490c9

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=sha256:88f65a0bd05ca87b200763f85202e244a14f1d1bdd68fe41ab49aa9ce857c788

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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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=sha256:3f05b13b00acae04e136b51e148930972d512bd61850c45b70b865ad578f07d2

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.145819Z digest=sha256:2ebb483d7751b992a61ca90df29cdbbbd3787481a0678628086cbfb68e8a1b2d

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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unresolved
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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.150128Z digest=sha256:b3107d73c259d0d06fef5e6e471ab2ec1c4ac6dc8530c15451a10ded7cd75830

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.154364Z digest=sha256:a0da7c68c953b14d16f2ce647dcbb1bffeb17c42799b365c33dd3ad2f9137bba

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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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:a442c50ee91ee51b3d202e919856cf9f5d3fae0ae3dd4f5801ce194b90514a52

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=sha256:9382c271b02b723226b3b349e21f384a709a9502e366fb9d7e44f20cbc7701b6

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=sha256:44d03a2b9b2bbfa33833943d59eddc3b6298c060f8c44a7e43839570b8f197b0

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.173333Z digest=sha256:133fe79b22d6bb72e3e6a55c3a4d25d48c2c8189dcf7e1b126d4bf2701c9fb54

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.177556Z digest=sha256:36ecea1c63f356a96bc984c4a473cf4e0ff4c012f013de6cab7530a4c95aa9a4

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.181834Z digest=sha256:461169b49e8f135db79f704629ee42e751f8071d0cccfe1eeda05d2a660a91cd

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.185744Z digest=sha256:f59e066b3e1fced256d2f9f701ea42623430ed457037f7bc84430abe2c19d91c

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.189866Z digest=sha256:1ed54f57662f29f3f521334aa354ced87a899eb20e9aeddb8145ef59617ad120

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=sha256:6b8d24f736b56743077d25e85a13b9a7a6af7544a297563b7dde86024b056858

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:ece2fa8a1e954ca1bc7a8e660c3129b7ae8d7ab01877918ddccf4d27fc2c51c3

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

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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=sha256:48d7ead813b8d9c2667fa2e81eaf49c0f59abeba24fcd925307852bf348dd97b

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T11:24:46.207808Z digest=sha256:35ff5832b58f2a6ecca5c727b53f04e6fdc4bc6425525f905a5c493c1962b3c9

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

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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=sha256:701762d27ba1c2195ac74a56489e2c6e2b55e70929b007731d1f5e605e19b03c

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:b6b9461e4eebd18ec861598879513247de4b00a67634b7259fcddaa3eee8b4b2

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=sha256:0043eb172df0efede19c5faefbf4e32d80ee16949059689da425c346804e184c

Pith citing papers

No inbound Pith citation observations are available.