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Paper Citation Record · LEDGER

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending

As of 18 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2508.20818.

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

pith.paper-citation-record.v1
2508.20818 v1

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:49:53.446298Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

60 of 60 outbound references displayed

  • verified exact8
  • verified fuzzy24
  • unresolved27
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8bce8743-5f04-42db-95b9-04588e09b9ee · outbound

This paper cites Curriculum learning.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Curriculum learning

Reference 1

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T14:49:53.077996Z digest=sha256:cd87b37d020a68045b74378a672f4abebe614a0b8e2ab7173fc1f2c77ca9187a

Observation e8321533-fd03-4df0-bfb9-33e9d65ff7d5 · outbound

This paper cites Geometric and Physical Quantities Improve E(3) Equivariant Message Passing.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

Reference 2

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source=arxiv_source observed=2026-08-05T14:49:53.084615Z digest=sha256:6a7c1dc18be164d5ce2be45441690981962c6a98ed458c7996cb0c45a255491d

Observation c1b6fea4-b81a-4619-b744-8028bc3b6d95 · outbound

This paper cites OpenAI Gym.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending OpenAI Gym

Reference 3

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source=arxiv_source observed=2026-08-05T14:49:53.090729Z digest=sha256:1729dd9a2d065cc077b113eddccc2c1398923e3c6942a3c9ba3145c423a41d69

Observation 17a2bfc7-54a6-4f7d-8697-430d7a63705b · outbound

This paper cites Robust multi-agent reinforcement learning via adversarial regularization: Theoretical foundation and stable algorithms.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Robust multi-agent reinforcement learning via adversarial regularization: Theoretical foundation and stable algorithms

Reference 4

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raw_fallback, observed 2026-08-05T14:49:54.783904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.096649Z digest=sha256:44e1aabe4d63b2e8a6659195fb0126bfc1f7bf36f2f800d88538d8a3ceae2374

Observation d0c64a76-72e8-42d2-9637-a02c9982c1e7 · outbound

This paper cites ${\rm E}(3)$-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending ${\rm E}(3)$-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning

Reference 5

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local_arxiv, observed 2026-08-05T14:49:54.267260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.102518Z digest=sha256:e1ac388e0d63d2022ee0b432ef7995900e110a80e2089762f02817e87cf0c4e8

Observation cde270a0-30ce-48f5-bd85-386f816d068b · outbound

This paper cites Multi-agent deep reinforcement learning for large-scale traffic signal control.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Multi-agent deep reinforcement learning for large-scale traffic signal control

Reference 6

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raw_fallback, observed 2026-08-05T14:49:54.767000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.108073Z digest=sha256:cdae78b05692ecf0afe134b8b42f28d7421327b7bc80ce224b6cebbe05f4c9ca

Observation 2186f324-68e7-4659-8cf1-578be6099af8 · outbound

This paper cites Prompt to transfer: Sim-to-real transfer for traffic signal control with prompt learning.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Prompt to transfer: Sim-to-real transfer for traffic signal control with prompt learning

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.114414Z digest=sha256:eb72c6d322c86e1c88d67e959b9e01115d1a5e5742dd83ae3942fb1c66ef8b73

Observation 6553f578-418f-4acb-ae10-16c3f6421f03 · outbound

This paper cites Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?

Reference 8

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source=arxiv_source observed=2026-08-05T14:49:53.119707Z digest=sha256:3d611be787748e63e234e080c6a20ccb844631c1cfe8c781fc68fe0619cc312e

Observation 60369e7c-564e-4c20-a86c-55061c991f64 · outbound

This paper cites Goal-gan: Multimodal trajectory prediction based on goal position estimation.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Goal-gan: Multimodal trajectory prediction based on goal position estimation

Reference 9

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raw_fallback, observed 2026-08-05T14:49:54.737091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.125566Z digest=sha256:fe2d6a06346dc05e9bd0da70f524791e1a587c015192badb1c6a028e68c7e572

Observation 7ed375d2-6ef5-4d1c-862b-681b0035100f · outbound

This paper cites A Survey on In-context Learning.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending A Survey on In-context Learning

Reference 10

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source=arxiv_source observed=2026-08-05T14:49:53.136104Z digest=sha256:1b3aeb6bc630bfb8da5289e1d6de1ae29ba7bd94bf67a0e5ee716daa397b6a30

Observation 6b9f8659-1f60-4612-b8e0-513f6409bdb5 · outbound

This paper cites J., Li, J., Paduraru, C., Gowal, S., and Hester, T.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending J., Li, J., Paduraru, C., Gowal, S., and Hester, T

Reference 11

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source=arxiv_source observed=2026-08-05T14:49:53.144831Z digest=sha256:0ea6e3435e400812ed6590d02410865c70c5a601208fd410bcd3fa778f56eedc

Observation 5595a402-99e8-4094-aff1-a33f843483ac · outbound

This paper cites Self-paced context evaluation for contextual reinforcement learning.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Self-paced context evaluation for contextual reinforcement learning

Reference 12

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raw_fallback, observed 2026-08-05T14:49:54.721532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.150395Z digest=sha256:ae46d2711c3d5ec4390a356c427d60246292c142c12c61e30c3b324afbcde285

Observation 2a3aefb0-cce6-4477-a7fc-9a7d8c9b3151 · outbound

This paper cites On the convergence theory of debiased model-agnostic meta-reinforcement learning.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending On the convergence theory of debiased model-agnostic meta-reinforcement learning

Reference 13

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raw_fallback, observed 2026-08-05T14:49:54.707397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.158299Z digest=sha256:09322b4dff96265305db11e857c992d40caea9bce2af54d588d69d71ee7b8ab6

Observation ef87b529-fde0-446c-8bff-6dcc433dfdb5 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Model-agnostic meta-learning for fast adaptation of deep networks

Reference 14

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source=arxiv_source observed=2026-08-05T14:49:53.166176Z digest=sha256:d924107a52eebd4e0c32a0be9b9764afadb9e713bb6909c1b2a83e4b4988eb04

Observation b739b558-5062-441c-970d-846fe12274eb · outbound

This paper cites Automatic goal generation for reinforcement learning agents.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Automatic goal generation for reinforcement learning agents

Reference 15

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raw_fallback, observed 2026-08-05T14:49:54.682857Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.172002Z digest=sha256:2fe52e4f406f6ae4282a0d92716752034a680a0018012be7c2a9dff9fa0f0134

Observation 3c292955-ea99-439a-95b3-0c2b5149435a · outbound

This paper cites Contextual Markov Decision Processes.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Contextual Markov Decision Processes

Reference 16

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source=arxiv_source observed=2026-08-05T14:49:53.177817Z digest=sha256:c84fb351d00a1f21fbc63d8a769914b57a8efc57888a07efc591dcb5eef05a27

Observation deaa23e6-2cb5-4d47-91b8-93bfde568955 · outbound

This paper cites Meta-Learning in Games.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Meta-Learning in Games

Reference 17

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verified exact
local_arxiv, observed 2026-08-05T14:49:54.195297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.185523Z digest=sha256:da2c85cf3df236ddda574eeafef023d1ccbb818113ee807b39f1675b02ec91d6

Observation e638e57e-0d6b-4ec6-8cba-91cb9e690c44 · outbound

This paper cites Robust Multi-Agent Reinforcement Learning with State Uncertainty.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Robust Multi-Agent Reinforcement Learning with State Uncertainty

Reference 18

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source=arxiv_source observed=2026-08-05T14:49:53.191701Z digest=sha256:3553d636d8e1f915ecf7c2c80a0f179c3a60ea7495d0e8d26eab274245e884b0

Observation a5bf4872-b2a7-40e8-901e-7c00e8d39c39 · outbound

This paper cites IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning

Reference 19

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source=arxiv_source observed=2026-08-05T14:49:53.197679Z digest=sha256:83d429623e5d646b7c126c4d413a8ad5ad5e214d9e303c25831aeedf0b117849

Observation 4442ed75-4efe-4671-b296-3905f42d162b · outbound

This paper cites Prioritized level replay.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Prioritized level replay

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.203509Z digest=sha256:65fd11ec641b2319b2050ab8fbeb26774130807332c2028f211b28b8b584d24c

Observation f55d1d7c-b757-41a1-a332-1b7e6b8bef4e · outbound

This paper cites Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method

Reference 21

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source=arxiv_source observed=2026-08-05T14:49:53.209213Z digest=sha256:a98385d5e069be76801c74818853479036e3fe82cc6cc07695582ca593200b70

Observation 17769e9d-0829-451a-b62b-d15618f65e22 · outbound

This paper cites R., and Pajarinen, J.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending R., and Pajarinen, J

Reference 22

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.215191Z digest=sha256:8150697338af167c92c5cf7731934b823ab7cf970574730c5d8b4a1279a241f0

Observation 99e073d4-6fd6-4048-9da1-a10a19d9218d · outbound

This paper cites Google research football: A novel reinforcement learning environment.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Google research football: A novel reinforcement learning environment

Reference 23

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source=arxiv_source observed=2026-08-05T14:49:53.220946Z digest=sha256:ec675d8dc62b0d628273e37bd2b37dd7382d4b2544ecf2525b5c39391ec2253c

Observation 66e4915a-862f-4ff8-8f9d-ed3f2dbf24f5 · outbound

This paper cites H., Gonzalez, J.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending H., Gonzalez, J

Reference 24

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source=arxiv_source observed=2026-08-05T14:49:53.226915Z digest=sha256:8d29949baaa5b03604761f7e0ccf48e4be0e4de18afd8ed5c6dfa311125606f9

Observation 33a56a9d-64a9-4cd5-b44b-ec8be114c5e8 · outbound

This paper cites Awq: Activation-aware weight quantization for on-device llm compression and acceleration.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Awq: Activation-aware weight quantization for on-device llm compression and acceleration

Reference 25

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raw_fallback, observed 2026-08-05T14:49:54.629125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.232654Z digest=sha256:aa2141d2ea333fc2faa096e90a79a87f40ad075a31bd1407118a9ac340f43c3b

Observation 8e08bafc-809b-487c-aea5-ab698128cc62 · outbound

This paper cites Multi-agent deep reinforcement learning for multi-echelon inventory management.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Multi-agent deep reinforcement learning for multi-echelon inventory management

Reference 26

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raw_fallback, observed 2026-08-05T14:49:54.613773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.240212Z digest=sha256:7c994df6af262d589a54a4a98a7bbcd17799c70136df757eca9456cd18b6cea2

Observation 4fe8a5e0-4b85-4b5c-a89d-ebfe4e2debf0 · outbound

This paper cites Eureka: Human-Level Reward Design via Coding Large Language Models.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Eureka: Human-Level Reward Design via Coding Large Language Models

Reference 27

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source=arxiv_source observed=2026-08-05T14:49:53.245926Z digest=sha256:48f88f97a4cfa54b819a406a99895919afdd76c5dd1a73840b11c78a8d4431c6

Observation 243501db-e2fb-448f-b141-5eda65845c25 · outbound

This paper cites DrEureka: Language Model Guided Sim-To-Real Transfer.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending DrEureka: Language Model Guided Sim-To-Real Transfer

Reference 28

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source=arxiv_source observed=2026-08-05T14:49:53.251513Z digest=sha256:de21057cf949325246fb8f14d8b5cdb7305c7e6a59232e8c748ac023fbb32a65

Observation 6373eaf7-8f25-4928-b12f-5d97e4d0ec8d · outbound

This paper cites Multi-agent meta-reinforcement learning: Sharper convergence rates with task similarity.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Multi-agent meta-reinforcement learning: Sharper convergence rates with task similarity

Reference 29

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raw_fallback, observed 2026-08-05T14:49:54.598366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.258884Z digest=sha256:32c4d12e7e7a43a92874548a935055be35ff3b3373cc690055c5187b2f460c7c

Observation 3860828f-80fe-4207-b27e-97445fec5a25 · outbound

This paper cites Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance

Reference 30

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local_arxiv, observed 2026-08-05T14:49:54.095788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.264238Z digest=sha256:dec4b8634829ebc6a4d2ffc8f159e4cc3f9082f9a93f36f6582c4830b27d26ef

Observation 52cb87fa-97b4-4512-9f42-003ae8575670 · outbound

This paper cites PEnGUiN: Partially Equivariant Graph NeUral Networks for Sample Efficient MARL.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending PEnGUiN: Partially Equivariant Graph NeUral Networks for Sample Efficient MARL

Reference 31

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verified exact
local_arxiv, observed 2026-08-05T14:49:54.070069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.269780Z digest=sha256:064ef39b0e8a44cd7afcd0ff6893c5b8b7bcd4e17411d68b7ae41c25fe1431bc

Observation 2d0752e8-3812-416a-b471-c9b6b9b226a4 · outbound

This paper cites A., and Mowbray, M.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending A., and Mowbray, M

Reference 32

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raw_fallback, observed 2026-08-05T14:49:54.580550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.275380Z digest=sha256:a681b94b36764b657f48d608768446644cb4d4da1542d9bba0322197052e00c7

Observation d25d5f15-0119-478e-9497-d3d7867b3d65 · outbound

This paper cites On First-Order Meta-Learning Algorithms.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending On First-Order Meta-Learning Algorithms

Reference 33

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T14:49:53.280725Z digest=sha256:e0518ab5f6b6f8191b3b9efaa2d1f36c92c7f42861db738ca71b569277b62dc0

Observation d76f10dd-64d4-4753-840b-ba8f3717fa7f · outbound

This paper cites Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Reference 34

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unresolved
no resolver link, observed 2026-08-05T14:49:53.286553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T14:49:53.286553Z digest=sha256:229a8807ddc85543d9eccf287ba0d9483de2c7d367b7128ff9c85e1009f5ba6c

Observation 14d1d631-e50b-4d31-9bb5-c5de9b3f33db · outbound

This paper cites Evolving Curricula with Regret-Based Environment Design.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Evolving Curricula with Regret-Based Environment Design

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-05T14:49:53.292174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T14:49:53.292174Z digest=sha256:46975748a5faba5eb17520d923faee09469bb5e50faf306e802a9ac7fadfe6e2

Observation ece27638-2542-4c76-a893-c7c036d872ca · outbound

This paper cites Teacher algorithms for curriculum learning of deep rl in continuously parameterized environments.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Teacher algorithms for curriculum learning of deep rl in continuously parameterized environments

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:49:54.565076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.297918Z digest=sha256:da3da8a3a36cd93463201a4ef8b804199428c9805831bab1d6c07614e2162e3d

Observation 3c993876-5d60-4289-a74a-7c76678fc352 · outbound

This paper cites The StarCraft Multi-Agent Challenge.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending The StarCraft Multi-Agent Challenge

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-05T14:49:53.303698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T14:49:53.303698Z digest=sha256:376d6a34db569625ccd110b65e16feaf6cbbec1291dc1426548feccd399ea22f

Observation bbc954b2-fbe0-449b-9b59-5a4f01043f38 · outbound

This paper cites A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control

Reference 38

Resolution
verified exact
local_arxiv, observed 2026-08-05T14:49:53.987688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.309265Z digest=sha256:9f4e44556b6e6d8caa68ed38a2d796226de2be176218609af9a159df5ad8bea2

Observation 2e950c51-3702-42c3-bd11-c43feb13a3a4 · outbound

This paper cites G., Hoogeboom, E., and Welling, M.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending G., Hoogeboom, E., and Welling, M

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:49:54.550100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.314914Z digest=sha256:16c7d9b4a28c09403583a61e1b04b81f97229613b1f61facccf3ec46960034d2

Observation 1d333b3d-90ef-4217-b17d-bc03d7ab2cf3 · outbound

This paper cites Learn to follow: Decentralized lifelong multi-agent pathfinding via planning and learning.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Learn to follow: Decentralized lifelong multi-agent pathfinding via planning and learning

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:49:54.535459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.319745Z digest=sha256:1a3c763b74c25f64ceff0ba467f24589a0d5c01b31881a29fa935a80e6b8092e

Observation 69b1c2fc-6cc0-4e9c-bf22-60873c0177cd · outbound

This paper cites H., Wu, J., Washington, C., Sadler, B.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending H., Wu, J., Washington, C., Sadler, B

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:49:54.519724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.325585Z digest=sha256:7fa42e626e39fa5fafcc24c424297c04688fe8c7f139549a93badaeda9ef1db7

Observation 2a600fc5-1a07-4ffc-a446-80978cd35340 · outbound

This paper cites Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-05T14:49:53.331348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T14:49:53.331348Z digest=sha256:7d84099d2ada3c02267f80cec8c26e42b77060a846fd041f3fc93a5e554a1bab

Observation b8cc58f3-6e8b-40f2-9741-7afde705b34e · outbound

This paper cites Qwen2.5: A party of foundation models, September 2024.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Qwen2.5: A party of foundation models, September 2024

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-05T14:49:53.337410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T14:49:53.337410Z digest=sha256:108d38031a6e8279bbce608dd17685452906b0eef721ccd8c08660b53ce630c6

Observation a8f0b07b-b38b-4243-9aa6-93ece1282d61 · outbound

This paper cites Domain randomization for transferring deep neural networks from simulation to the real world.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Domain randomization for transferring deep neural networks from simulation to the real world

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:49:54.495009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.344210Z digest=sha256:a6c1c1cb31dc68528f8b2bb064c34fb1dff8637a54fb16f25c515f83b799084b

Observation 1a04ae97-f2a5-4ed5-a414-3e4d61a59840 · outbound

This paper cites Adapting deep visuomotor representations with weak pairwise constraints.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Adapting deep visuomotor representations with weak pairwise constraints

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:49:54.480278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.349726Z digest=sha256:0882fb01a95972a4fdab60c54b333b137c518b4385ca25bc32a4c7b0f0b74d30

Observation 0120d4fc-5953-418c-8add-10e5d4b5cb3a · outbound

This paper cites Presslight: Learning max pressure control to coordinate traffic signals in arterial network.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Presslight: Learning max pressure control to coordinate traffic signals in arterial network

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-05T14:49:53.355477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T14:49:53.355477Z digest=sha256:3130bfcd9a618caecfd53b15265119acd5b5a4a398b897a8ee47160c362a22c5

Observation d6c76b24-d557-4085-af4a-03cc43905d94 · outbound

This paper cites Colight: Learning network-level cooperation for traffic signal control.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Colight: Learning network-level cooperation for traffic signal control

Reference 47

Resolution
metadata mismatch
raw_fallback, observed 2026-08-05T14:49:53.887301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.361305Z digest=sha256:60022a4cb0ed66ed72e6f83666973325bfdd097d65fe24ac9ef03c27095d6d1d

Observation 59ab2662-e97a-4f3a-be63-78dfe53d29c0 · outbound

This paper cites H., Peng, H., and Zhang, S.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending H., Peng, H., and Zhang, S

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:49:54.463977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.367163Z digest=sha256:4b47a682909c5372b86f2b8190276a5a464010190801bdb3fc6452bef2b952bf

Observation 7594c0cf-4664-46fe-b3ed-bbe6443f7bd1 · outbound

This paper cites LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-05T14:49:53.373380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T14:49:53.373380Z digest=sha256:a116df4fe5ba02343dcb8ba891b980c953d42b815f86d778dd6d0afde13c0111

Observation 5d242e2c-5998-4a57-bbba-cb96f7fa8058 · outbound

This paper cites MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctions.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctions

Reference 50

Resolution
verified exact
local_arxiv, observed 2026-08-05T14:49:53.774193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.381662Z digest=sha256:18a81d3bdfdcf112c9b2ffc157428a33b8ab91887ebf7ed9f26ba557b8691ed4

Observation 29b6a978-c4e8-4209-a457-3a69175d1852 · outbound

This paper cites Webshop: Towards scalable real-world web interaction with grounded language agents.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Webshop: Towards scalable real-world web interaction with grounded language agents

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:49:54.449480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.389974Z digest=sha256:9dd5ad646e32f62e7b49978d86c5623f7abad4ea7e8173781d1e9c1be25b5af5

Observation 53c43ccb-bf17-41f5-9be0-bc94893204e4 · outbound

This paper cites The surprising effectiveness of ppo in cooperative multi-agent games.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending The surprising effectiveness of ppo in cooperative multi-agent games

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-05T14:49:53.397210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T14:49:53.397210Z digest=sha256:15fa067b665467d0368597378ab7237e0e6089605b7e1af6402b696a85bff393

Observation b0527413-2068-46f6-847a-58ab26c5a296 · outbound

This paper cites EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-05T14:49:53.402378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T14:49:53.402378Z digest=sha256:5c91928c9c1f8c5fcc26e9b54d07e92c62b0c4287763efe7380f36e9ce7ded7e

Observation a9690a80-875f-460a-b9b6-f89d8d81074e · outbound

This paper cites Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:49:54.426021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.408024Z digest=sha256:714522e5cf024d6f287898715a210cd19afdacfc34ae6e7cc3dc560c829a1ee5

Observation fbc0c12f-0092-48db-84ac-524b0e2b01ca · outbound

This paper cites Robust multi-agent reinforcement learning with model uncertainty.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Robust multi-agent reinforcement learning with model uncertainty

Reference 55

Resolution
verified exact
raw_fallback, observed 2026-08-05T14:49:53.736368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.413440Z digest=sha256:29c9e541f541f19a2bdce6c9f38fd23e29379cca923eff59bf5b3bb62094dcfc

Observation 7955efdf-ec44-40fa-914e-0058d2a25487 · outbound

This paper cites No-regret learning in time-varying zero-sum games.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending No-regret learning in time-varying zero-sum games

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:49:54.410813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.419372Z digest=sha256:76bdf72b5226baf5e167f9de5c453a084f1fc11ef3aecc57445505aed67b0123

Observation 2761b115-2637-4c60-9cae-c93b17fedbc7 · outbound

This paper cites Learning meta representations for agents in multi-agent reinforcement learning.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Learning meta representations for agents in multi-agent reinforcement learning

Reference 57

Resolution
verified exact
raw_fallback, observed 2026-08-05T14:49:53.660591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.425833Z digest=sha256:20422f267e84cc007d48e3b9c720563f0feba03a83b8f6016f0e4273c38e7c77

Observation 9455f57b-0e5d-48fe-b1f0-654d389f2d71 · outbound

This paper cites Met-mapf: A metamorphic testing approach for multi-agent path finding algorithms.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Met-mapf: A metamorphic testing approach for multi-agent path finding algorithms

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T14:49:54.395671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-05T14:49:53.433486Z digest=sha256:3e934436368441b98760370274979c47e48ec6609105e8ef5dcc0aea4d29479b

Observation a3522728-cdb9-4801-b7fa-c7da52b77948 · outbound

This paper cites P., and Westerlund, T.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending P., and Westerlund, T

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-05T14:49:53.440473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T14:49:53.440473Z digest=sha256:d9435e4ec26f717cbc54eb95b41c9d248abc793ca712fdd1f748c627181f3955

Observation b34fbfef-bfd4-465c-9c65-413ffe6c4bdd · outbound

This paper cites write newline.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending write newline

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-05T14:49:53.446298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T14:49:53.446298Z digest=sha256:f39fb7848abfc84a8d549c93c8ec1ce63bd1751bb2ae335c0c2ba97fdf1e99e5

Pith citing papers

No inbound Pith citation observations are available.