Paper Citation Record · LEDGER

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making

As of 19 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 2 inbound Pith citation observations for arXiv:2507.12496.

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

pith.paper-citation-record.v1
2507.12496 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:12:30.939320Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T08:13:40.975340Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T08:14:03.166265Z

Reference resolution

48 of 48 outbound references displayed

verified exact3
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 393ff599-eb2f-415e-9f3d-295793eb7661 · outbound

This paper cites Vision-Language Models as a Source of Rewards.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Vision-Language Models as a Source of Rewards

Reference 1

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Observation cde3a652-d87c-4464-bf58-e0a85a65d3f3 · outbound

This paper cites RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Reference 2

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Observation c53b73d1-ba7a-45f7-af22-9a063a0df22c · outbound

This paper cites Do as i can, not as i say: Grounding language in robotic affordances.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Do as i can, not as i say: Grounding language in robotic affordances

Reference 3

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Observation 3ef6e875-2802-42fa-ba09-f14cd05c5355 · outbound

This paper cites GROOT: Learning to Follow Instructions by Watching Gameplay Videos.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making GROOT: Learning to Follow Instructions by Watching Gameplay Videos

Reference 4

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Observation 9fa63600-8be8-4b11-91e1-f4c63f30750d · outbound

This paper cites Cross-modal domain adaptation for cost-efficient visual reinforcement learning.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Cross-modal domain adaptation for cost-efficient visual reinforcement learning

Reference 5

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Observation 4f4daad8-3ba7-4a41-8dee-feb468dc0f1e · outbound

This paper cites Can foundation models perform zero-shot task specification for robot manipulation? In Learning for dynamics and control conference, pp.\ 893--905.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Can foundation models perform zero-shot task specification for robot manipulation? In Learning for dynamics and control conference, pp.\ 893--905

Reference 6

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Observation 57fe9b80-d4cd-401c-a9f3-6a2904981052 · outbound

This paper cites Guiding pretraining in reinforcement learning with large language models.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Guiding pretraining in reinforcement learning with large language models

Reference 7

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Observation 1e132207-e817-48cc-9223-0233bc5dcec7 · outbound

This paper cites Minedojo: Building open-ended embodied agents with internet-scale knowledge.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Minedojo: Building open-ended embodied agents with internet-scale knowledge

Reference 8

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Observation 0e8f48ee-2d86-4fdb-b837-61014d6434b3 · outbound

This paper cites and Gu, S.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making and Gu, S

Reference 9

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Observation 6bb2c2f5-6f1b-4806-9f91-763329d08b22 · outbound

This paper cites and Kong, R.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making and Kong, R

Reference 10

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Observation 70a90a27-ee19-4c55-90ab-040b1ee96e71 · outbound

This paper cites Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning

Reference 11

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Observation 6b97252d-e342-487f-8f6f-429f3c351d1a · outbound

This paper cites Learning latent dynamics for planning from pixels.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Learning latent dynamics for planning from pixels

Reference 12

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Observation 731b9d18-0fd5-488a-89f2-ba9117f486bf · outbound

This paper cites Deep hierarchical planning from pixels.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Deep hierarchical planning from pixels

Reference 13

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Observation 732811af-9d09-4692-8c73-22b6654ae172 · outbound

This paper cites Mastering Diverse Domains through World Models.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Mastering Diverse Domains through World Models

Reference 14

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Observation 39d27f75-d8dd-4317-88d2-126795e750cd · outbound

This paper cites Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery

Reference 15

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Observation d373c343-182a-4564-8762-c9f0ca2b5658 · outbound

This paper cites and Sadigh, D.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making and Sadigh, D

Reference 16

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Observation e5908c2e-54f0-45b5-b2aa-339ee7a24d33 · outbound

This paper cites SENSOR: Imitate Third-Person Expert's Behaviors via Active Sensoring.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making SENSOR: Imitate Third-Person Expert's Behaviors via Active Sensoring

Reference 17

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Observation 64ea682b-9d72-4056-a1c4-55684cacf274 · outbound

This paper cites P., Littman, M.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making P., Littman, M

Reference 18

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Observation 4eeb02b1-76de-49a5-af09-5122d2f86184 · outbound

This paper cites Motif: Intrinsic Motivation from Artificial Intelligence Feedback.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Motif: Intrinsic Motivation from Artificial Intelligence Feedback

Reference 19

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Observation 9948b07d-d513-4c71-bac3-f9821c0a0714 · outbound

This paper cites Offline Reinforcement Learning with Implicit Q-Learning.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Offline Reinforcement Learning with Implicit Q-Learning

Reference 21

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Observation 1c00c216-2a6c-4e00-bfd4-e78c84b992a8 · outbound

This paper cites Reward Design with Language Models.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Reward Design with Language Models

Reference 22

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Observation e301952f-11b8-46d9-b10b-f87b4e761d2b · outbound

This paper cites R., Mesnard, T., Ferret, J., Bishop, C., Hall, E., Carbune, V., and Rastogi, A.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making R., Mesnard, T., Ferret, J., Bishop, C., Hall, E., Carbune, V., and Rastogi, A

Reference 23

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Observation 194f0667-1d72-4552-9e9f-c49024d10bd5 · outbound

This paper cites RL-GPT: Integrating Reinforcement Learning and Code-as-policy.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making RL-GPT: Integrating Reinforcement Learning and Code-as-policy

Reference 24

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Observation 6b995397-c9a1-41cd-af16-1afddfa6abc0 · outbound

This paper cites Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations

Reference 25

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Observation 6efd49a3-65cb-448a-8795-8cf918c68c57 · outbound

This paper cites A Survey on Model-based Reinforcement Learning.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making A Survey on Model-based Reinforcement Learning

Reference 26

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Observation 25c55c59-a52d-40eb-8d77-b7a5da1bb2dd · outbound

This paper cites HarmonyDream: Task Harmonization Inside World Models.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making HarmonyDream: Task Harmonization Inside World Models

Reference 27

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This paper cites Choreographer: Learning and Adapting Skills in Imagination.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Choreographer: Learning and Adapting Skills in Imagination

Reference 28

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Observation d3742bf7-789e-44df-ad78-c06b4f37c4e7 · outbound

This paper cites GenRL: Multimodal-foundation world models for generalization in embodied agents.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making GenRL: Multimodal-foundation world models for generalization in embodied agents

Reference 29

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This paper cites Discovering and achieving goals via world models.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Discovering and achieving goals via world models

Reference 30

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Observation 126dc188-70fc-472b-8528-810bde1a90bc · outbound

This paper cites M., Broekens, J., Plaat, A., Jonker, C.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making M., Broekens, J., Plaat, A., Jonker, C

Reference 31

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Observation 45cf5505-9317-475a-b6b1-0f095341abf4 · outbound

This paper cites A., Maccarini, M., Forgione, M., Piga, D., Spahiu, B., and Roveda, L.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making A., Maccarini, M., Forgione, M., Piga, D., Spahiu, B., and Roveda, L

Reference 32

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Observation 4e7085c3-6b9a-4dc7-b1a4-68abcff1be6f · outbound

This paper cites Foundation policies with hilbert representations.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Foundation policies with hilbert representations

Reference 33

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Observation 424adbee-797f-42f2-9075-55babfc83952 · outbound

This paper cites Mp5: A multi-modal open-ended embodied system in minecraft via active perception.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Mp5: A multi-modal open-ended embodied system in minecraft via active perception

Reference 34

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Observation e8732b67-dd5e-4cdb-ba09-daf077b9ee65 · outbound

This paper cites Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning

Reference 35

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This paper cites Planning to explore via self-supervised world models.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Planning to explore via self-supervised world models

Reference 36

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Observation 2d5a1dab-767e-46df-bd83-8d8cd68c1f21 · outbound

This paper cites Roboclip: One demonstration is enough to learn robot policies.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Roboclip: One demonstration is enough to learn robot policies

Reference 37

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Observation da74f2ca-0392-4b27-b122-422d1bf578d6 · outbound

This paper cites Third-Person Imitation Learning.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Third-Person Imitation Learning

Reference 38

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FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making DeepMind Control Suite

Reference 39

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Observation db5c832c-fde3-4b28-af48-6e0b8a96293c · outbound

This paper cites Open-Ended Learning Leads to Generally Capable Agents.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Open-Ended Learning Leads to Generally Capable Agents

Reference 40

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FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Semail: eliminating distractors in visual imitation via separated models

Reference 41

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Observation 1ad1568e-4602-4f15-99ec-8dec845ddf65 · outbound

This paper cites SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets

Reference 42

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Observation 9dd3189e-ec2d-4766-a1a5-ac07e3942e89 · outbound

This paper cites Internvideo2: Scaling foundation models for multimodal video understanding.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Internvideo2: Scaling foundation models for multimodal video understanding

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:32.999439Z

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Observation 9341a3ae-f10b-4994-aaa5-f008912652d8 · outbound

This paper cites DayDreamer: World Models for Physical Robot Learning.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making DayDreamer: World Models for Physical Robot Learning

Reference 44

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FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Y., Levine, S., Finn, C., and Ma, T

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:32.807333Z

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Observation dc6f4ff9-ae4c-465a-b17e-3898491b302c · outbound

This paper cites Whale-x: Learning scalable embodied world models with enhanced generalizability.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Whale-x: Learning scalable embodied world models with enhanced generalizability

Reference 46

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Observation 8b615564-fd1a-46a0-bdd5-4f24cd7a0ee0 · outbound

This paper cites Learning from visual observation via offline pretrained state-to-go transformer.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making Learning from visual observation via offline pretrained state-to-go transformer

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:32.472993Z

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Observation 2a6dd4b2-f3ee-411e-bf89-e37d50389509 · outbound

This paper cites and Liu, X.-Y.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making and Liu, X.-Y

Reference 48

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Observation cd48085a-29dd-4901-8fcf-a98606ec9533 · outbound

This paper cites write newline.

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making write newline

Reference 49

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Pith citing papers

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Reinforcing VLAs in Task-Agnostic World Models cites this paper.

Reinforcing VLAs in Task-Agnostic World Models FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making

Reference 35

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Observation 9502d7c9-8a17-479e-904f-0e23f6c42965 · inbound

Reinforcing VLAs in Task-Agnostic World Models cites this paper.

Reinforcing VLAs in Task-Agnostic World Models FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making

Reference 35

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