Paper Citation Record · LEDGER

An Optimisation Framework for Unsupervised Environment Design

As of 9 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2505.20659.

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

pith.paper-citation-record.v1
2505.20659 v2

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:59:14.592755Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-09T14:21:34.002785Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T17:01:05.767124Z

Reference resolution

42 of 42 outbound references displayed

verified exact2
verified fuzzy26
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 45ca37ad-4d64-478d-b350-43edcb5d8613 · outbound

This paper cites Deep reinforcement learning at the edge of the statistical precipice.

An Optimisation Framework for Unsupervised Environment Design Deep reinforcement learning at the edge of the statistical precipice

Reference 1

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Observation ada294b3-89b2-47d5-9b4f-070da3fe9c1a · outbound

This paper cites Clutr: curriculum learning via unsupervised task representation learning.

An Optimisation Framework for Unsupervised Environment Design Clutr: curriculum learning via unsupervised task representation learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:19.546959Z

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Observation 0cd35d5d-94f9-4ca2-b00e-5b4f1bbc28d6 · outbound

This paper cites Refining minimax regret for unsupervised environment design.

An Optimisation Framework for Unsupervised Environment Design Refining minimax regret for unsupervised environment design

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:19.421742Z

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Observation f8cf8398-e8db-4a6a-a36a-1f2068138495 · outbound

This paper cites JAX : composable transformations of P ython+ N um P y programs, 2018.

An Optimisation Framework for Unsupervised Environment Design JAX : composable transformations of P ython+ N um P y programs, 2018

Reference 4

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Source-reported events for the cited work

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Observation 986bd1ea-e823-4651-938b-31815d897e4d · outbound

This paper cites Accelerated algorithms for constrained nonconvex-nonconcave min-max optimization and comonotone inclusion.

An Optimisation Framework for Unsupervised Environment Design Accelerated algorithms for constrained nonconvex-nonconcave min-max optimization and comonotone inclusion

Reference 5

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Observation 7734ced4-d71b-46e5-8d30-12d7179bbbb6 · outbound

This paper cites Minigrid & miniworld: Modular & customizable reinforcement learning environments for goal-oriented tasks.

An Optimisation Framework for Unsupervised Environment Design Minigrid & miniworld: Modular & customizable reinforcement learning environments for goal-oriented tasks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:19.001873Z

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Observation 69b594fd-67e8-4c20-9c97-21e320e62cb7 · outbound

This paper cites Adversarial environment design via regret-guided diffusion models.

An Optimisation Framework for Unsupervised Environment Design Adversarial environment design via regret-guided diffusion models

Reference 7

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Observation c29b49b6-08d9-4c8c-b2ec-db2998b0e4f5 · outbound

This paper cites JaxUED: A simple and useable UED library in Jax.

An Optimisation Framework for Unsupervised Environment Design JaxUED: A simple and useable UED library in Jax

Reference 8

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Source-reported events for the cited work

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Observation 944f8ce6-0429-4135-98dd-126f720c94a2 · outbound

This paper cites Last-iterate convergence: Zero-sum games and constrained min-max optimization, 2020.

An Optimisation Framework for Unsupervised Environment Design Last-iterate convergence: Zero-sum games and constrained min-max optimization, 2020

Reference 9

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Observation 9fda1768-f0a8-43b4-8653-e35690083741 · outbound

This paper cites Emergent complexity and zero-shot transfer via unsupervised environment design.

An Optimisation Framework for Unsupervised Environment Design Emergent complexity and zero-shot transfer via unsupervised environment design

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:18.752437Z

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Observation 5c41e082-7c63-4c14-b160-ebe0f672ed9c · outbound

This paper cites Lucas, and Stefano V.

An Optimisation Framework for Unsupervised Environment Design Lucas, and Stefano V

Reference 11

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Source-reported events for the cited work

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Observation c6b2672d-dce6-4994-9582-096f264228e0 · outbound

This paper cites Mirror learning: A unifying framework of policy optimisation.

An Optimisation Framework for Unsupervised Environment Design Mirror learning: A unifying framework of policy optimisation

Reference 12

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Source-reported events for the cited work

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Observation 51a42f59-85a3-4c36-97e9-7cd76d272abc · outbound

This paper cites A two-timescale stochastic algorithm framework for bilevel optimization: Complexity analysis and application to actor-critic.

An Optimisation Framework for Unsupervised Environment Design A two-timescale stochastic algorithm framework for bilevel optimization: Complexity analysis and application to actor-critic

Reference 13

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Observation d948ca9a-e2ed-421d-abbf-ce71266722d3 · outbound

This paper cites Replay-guided adversarial environment design.

An Optimisation Framework for Unsupervised Environment Design Replay-guided adversarial environment design

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:18.336846Z

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Observation 2e041ef6-932d-4fb3-aba4-2147f2babde1 · outbound

This paper cites Prioritized level replay.

An Optimisation Framework for Unsupervised Environment Design Prioritized level replay

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:18.130563Z

Source-reported events for the cited work

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Observation d15ce4fa-db3d-49a0-a718-834ea5440add · outbound

This paper cites u ttler, Edward Grefenstette, Tim Rockt\.

An Optimisation Framework for Unsupervised Environment Design u ttler, Edward Grefenstette, Tim Rockt\

Reference 16

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Source-reported events for the cited work

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Observation 92c27f9c-b253-4fd8-8571-b5e342984f7c · outbound

This paper cites an unresolved cited work.

An Optimisation Framework for Unsupervised Environment Design Unresolved cited work

Reference 17

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Observation d27827ae-fc16-486a-a107-775f5a4c34ca · outbound

This paper cites Learning equilibria in adversarial team markov games: A nonconvex-hidden-concave min-max optimization problem.

An Optimisation Framework for Unsupervised Environment Design Learning equilibria in adversarial team markov games: A nonconvex-hidden-concave min-max optimization problem

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:17.478131Z

Source-reported events for the cited work

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Observation f16aeca1-a981-4b65-b472-4dcc732060dc · outbound

This paper cites Adam: A Method for Stochastic Optimization.

An Optimisation Framework for Unsupervised Environment Design Adam: A Method for Stochastic Optimization

Reference 19

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Source-reported events for the cited work

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Observation 9a288aa5-d2e6-4cad-9d1d-6aba6953f1e1 · outbound

This paper cites Last iterate convergence in no-regret learning: constrained min-max optimization for convex-concave landscapes.

An Optimisation Framework for Unsupervised Environment Design Last iterate convergence in no-regret learning: constrained min-max optimization for convex-concave landscapes

Reference 20

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Source-reported events for the cited work

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Observation ae1812bc-2a81-46ee-878a-c0d69fca5952 · outbound

This paper cites Enhancing the Hierarchical Environment Design via Generative Trajectory Modeling.

An Optimisation Framework for Unsupervised Environment Design Enhancing the Hierarchical Environment Design via Generative Trajectory Modeling

Reference 21

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Observation 129197d0-3282-4dce-a945-3e419beb864b · outbound

This paper cites Tiada: A time-scale adaptive algorithm for nonconvex minimax optimization.

An Optimisation Framework for Unsupervised Environment Design Tiada: A time-scale adaptive algorithm for nonconvex minimax optimization

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:17.070894Z

Source-reported events for the cited work

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Observation f0b11906-d612-46ff-960d-ce38d447c9d0 · outbound

This paper cites On gradient descent ascent for nonconvex-concave minimax problems.

An Optimisation Framework for Unsupervised Environment Design On gradient descent ascent for nonconvex-concave minimax problems

Reference 23

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Source-reported events for the cited work

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Observation cc0f33ef-84da-45c4-9a50-d6f8762473bc · outbound

This paper cites Craftax: A lightning-fast benchmark for open-ended reinforcement learning.

An Optimisation Framework for Unsupervised Environment Design Craftax: A lightning-fast benchmark for open-ended reinforcement learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:16.732021Z

Source-reported events for the cited work

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Observation 70cb860c-feca-41ff-820f-a284257be5b9 · outbound

This paper cites Kinetix: Investigating the training of general agents through open-ended physics-based control tasks.

An Optimisation Framework for Unsupervised Environment Design Kinetix: Investigating the training of general agents through open-ended physics-based control tasks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:16.626931Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 66de8767-e7ae-4c2d-8a51-5e66d11f0a1a · outbound

This paper cites Optimistic mirror descent in saddle-point problems: Going the extra(-gradient) mile.

An Optimisation Framework for Unsupervised Environment Design Optimistic mirror descent in saddle-point problems: Going the extra(-gradient) mile

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:16.494263Z

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Observation 5e02fc73-c8e4-49e1-9a07-b54e5a9f27c9 · outbound

This paper cites Stable recurrent models.

An Optimisation Framework for Unsupervised Environment Design Stable recurrent models

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:16.372486Z

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Observation ed87f572-e213-491f-8bed-5f4f9e76b884 · outbound

This paper cites Robust reinforcement learning.

An Optimisation Framework for Unsupervised Environment Design Robust reinforcement learning

Reference 28

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Observation f2683a6a-ecf0-4633-972b-a007f7d9e3f7 · outbound

This paper cites an unresolved cited work.

An Optimisation Framework for Unsupervised Environment Design Unresolved cited work

Reference 29

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Source-reported events for the cited work

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Observation 0695a52a-79a8-4f20-855b-4449848915e1 · outbound

This paper cites XL and-minigrid: Scalable meta-reinforcement learning environments in JAX.

An Optimisation Framework for Unsupervised Environment Design XL and-minigrid: Scalable meta-reinforcement learning environments in JAX

Reference 30

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Observation 6a374c7d-daa1-4782-b1ec-6927331f77f5 · outbound

This paper cites Solving a class of non-convex min-max games using iterative first order methods.

An Optimisation Framework for Unsupervised Environment Design Solving a class of non-convex min-max games using iterative first order methods

Reference 31

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Observation 0aaa8f87-43d4-449f-96c1-75f18f280b62 · outbound

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

An Optimisation Framework for Unsupervised Environment Design Evolving Curricula with Regret-Based Environment Design

Reference 32

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Observation 8167e1a4-a411-4579-8cd3-2046172a8ce5 · outbound

This paper cites Robust optimization over multiple domains.

An Optimisation Framework for Unsupervised Environment Design Robust optimization over multiple domains

Reference 33

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Observation d5a5d92c-dfd3-4037-a06c-ddcb3e253e99 · outbound

This paper cites No regrets: Investigating and improving regret approximations for curriculum discovery.

An Optimisation Framework for Unsupervised Environment Design No regrets: Investigating and improving regret approximations for curriculum discovery

Reference 34

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Observation 63983b9c-3285-4557-869d-4ade299788cf · outbound

This paper cites Proximal Policy Optimization Algorithms.

An Optimisation Framework for Unsupervised Environment Design Proximal Policy Optimization Algorithms

Reference 35

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Source-reported events for the cited work

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Observation d39efadf-3ba4-4e3b-b733-65fad7461315 · outbound

This paper cites Hessian aided policy gradient.

An Optimisation Framework for Unsupervised Environment Design Hessian aided policy gradient

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:15.717329Z

Source-reported events for the cited work

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Observation 3075b3c5-cab8-480a-8875-0fc3b7e313cb · outbound

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

An Optimisation Framework for Unsupervised Environment Design Domain randomization for transferring deep neural networks from simulation to the real world

Reference 37

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Source-reported events for the cited work

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Observation 581c788d-a6a1-4de2-8341-95af4d932f51 · outbound

This paper cites P roximal C urriculum for R einforcement L earning A gents.

An Optimisation Framework for Unsupervised Environment Design P roximal C urriculum for R einforcement L earning A gents

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:15.595700Z

Source-reported events for the cited work

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Observation 85d3e82b-1fa0-4282-a736-f1286aceaa78 · outbound

This paper cites Lipschitz regularity of deep neural networks: analysis and efficient estimation.

An Optimisation Framework for Unsupervised Environment Design Lipschitz regularity of deep neural networks: analysis and efficient estimation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:59:15.458707Z

Source-reported events for the cited work

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Observation 7bf159ea-5053-4b73-86c9-d215858e5636 · outbound

This paper cites Stabilizing Generative Adversarial Networks: A Survey.

An Optimisation Framework for Unsupervised Environment Design Stabilizing Generative Adversarial Networks: A Survey

Reference 40

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Observation c8e08809-0025-43f7-90cb-dab3fb946529 · outbound

This paper cites Williams.

An Optimisation Framework for Unsupervised Environment Design Williams

Reference 41

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Observation 55eebba2-f222-4ffd-a331-6b6d473f3c27 · outbound

This paper cites write newline.

An Optimisation Framework for Unsupervised Environment Design write newline

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T13:59:14.592755Z

Source-reported events for the cited work

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Pith citing papers

Observation 403859c4-df78-4cf1-80df-81596663fb5e · inbound

PACE: Parameter Change for Unsupervised Environment Design cites this paper.

PACE: Parameter Change for Unsupervised Environment Design An Optimisation Framework for Unsupervised Environment Design

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:01:05.778697Z

Source-reported events for the cited work

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