Paper Citation Record · LEDGER

Eurekaverse: Environment Curriculum Generation via Large Language Models

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 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 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:45:34.926808Z

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 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 2026-08-05T06:32:48.257954+00:00.

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 2026-08-05T06:32:48.257954+00:00.

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 2026-08-05T06:32:48.257954+00:00.

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 2026-08-05T06:32:48.257954+00:00.

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.

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.

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.

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.