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Paper Citation Record · LEDGER

Eurekaverse: Environment Curriculum Generation via Large Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2411.01775.

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

pith.paper-citation-record.v1
2411.01775 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T00:03:16.457081Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 99f53cd6-4de4-4efb-a43d-0f792ba826d3 · inbound

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards cites this paper.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Eurekaverse: Environment Curriculum Generation via Large Language Models

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-08T00:03:16.457081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:03:16.457081Z digest=sha256:32b1bd5c97b8c5299bb96cae002615a2e7835bd9b86d34eb24a9c9ca485e0aac

Observation d37a556b-2458-43da-919f-b8703e104bfc · inbound

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks cites this paper.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Eurekaverse: Environment Curriculum Generation via Large Language Models

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-06T11:03:25.196049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:25.196049Z digest=sha256:067965b79854cc245e0c468157d8050caabf013df9c3719310c6e60d648ae295

Observation 7b8c3017-7052-4050-a7b0-583d1028e0c7 · inbound

SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning cites this paper.

SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning Eurekaverse: Environment Curriculum Generation via Large Language Models

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:21:26.349343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-12T04:21:44.087943Z digest=sha256:507c1834972c15909733ace53c7ba6f603ebb40b67851a13f7ea88634e3b57ee

Observation 20b9c883-549a-4e28-85b6-e04e524fb33b · inbound

SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning cites this paper.

SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning Eurekaverse: Environment Curriculum Generation via Large Language Models

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-05-14T21:32:59.680199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-14T21:30:42.766390Z digest=sha256:16d47e8b01168d1c4d384d08a7ac9ecd96f8d014884dab6f1bb77c39efc7825c

Observation d581ab46-8349-4970-aad7-dedfcabfe274 · inbound

Robots Need More than VLA and World Models cites this paper.

Robots Need More than VLA and World Models Eurekaverse: Environment Curriculum Generation via Large Language Models

Reference 157

Resolution
verified exact
arxiv_id, observed 2026-06-28T01:11:28.917901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-28T01:01:33.530167Z digest=sha256:4f3692d689c2c6775a1f05797947d734c27337ea7653c841301415974958b656

Observation 962e5f01-ab28-4e1e-825e-ff49790468d4 · inbound

A Model-Driven Approach for Developing Families of Reinforcement Learning Environments cites this paper.

A Model-Driven Approach for Developing Families of Reinforcement Learning Environments Eurekaverse: Environment Curriculum Generation via Large Language Models

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-07-04T05:09:37.075367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-26T16:12:49.695786Z digest=sha256:889cc599544fe5f24a8bb02bdeb3d0c9911c86d1072e641bdc530ab14013c2cf

Observation fda75777-0c8f-47fc-ab77-58906fa8a99c · inbound

A Model-Driven Approach for Developing Families of Reinforcement Learning Environments cites this paper.

A Model-Driven Approach for Developing Families of Reinforcement Learning Environments Eurekaverse: Environment Curriculum Generation via Large Language Models

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-03T00:52:19.742547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T00:52:19.742547Z digest=sha256:c55b30080c97639b55524c439e83dec3b6911f1d6f6a70d0951bdf551e1dea7e

Observation 042e9e22-97ba-42be-ae09-c0f47abd1d8f · inbound

Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL cites this paper.

Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL Eurekaverse: Environment Curriculum Generation via Large Language Models

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-02T14:50:12.886975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T14:50:12.886975Z digest=sha256:e17fdeda005652066c7bdad6b670191bbafb8d30baebc7bc08e55d98e901a75f

Observation e075bbba-c7b8-4282-beda-bbdf3e4e2b5d · inbound

LEACL: LLM-Enhanced Automatic Curriculum Learning for Reinforcement Learning in Long-Horizon Manipulation Tasks cites this paper.

LEACL: LLM-Enhanced Automatic Curriculum Learning for Reinforcement Learning in Long-Horizon Manipulation Tasks Eurekaverse: Environment Curriculum Generation via Large Language Models

Reference 25

Resolution
unresolved
no resolver link, observed 2026-07-30T20:27:56.614512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T20:27:56.614512Z digest=sha256:dd3b359ab125d990d41ec84648e7a856a065ecc3b2261c1ae22e2eee84533a13

Observation fb94c566-b7ad-46cc-8cbe-92821e30c64d · inbound

Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills cites this paper.

Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills Eurekaverse: Environment Curriculum Generation via Large Language Models

Reference 146

Resolution
unresolved
no resolver link, observed 2026-08-04T19:45:34.926808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T19:45:34.926808Z digest=sha256:603b5f77e42406849a890210d04ec762467764607ce5e277c1993e5f15dc0f2f