Paper Citation Record · LEDGER

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

As of 5 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 31 inbound Pith citation observations for arXiv:2509.24948.

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

pith.paper-citation-record.v1
2509.24948 v6

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-18T12:48:32.123998Z

measured 52 of 52 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 31 of 31 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T09:43:32.425225Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T08:36:59.803669Z

Reference resolution

21 of 21 outbound references displayed

verified exact19
verified fuzzy0
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9339c109-9901-4ac2-aca9-fc16b18e7e73 · outbound

This paper cites Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-18T12:51:23.546120Z

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 7d0e224a-43b3-4916-99fb-1379ff8754b7 · outbound

This paper cites V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-18T12:51:23.537135Z

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 709af896-4e96-4d20-b238-97efbbb5bbdc · outbound

This paper cites Qwen Technical Report.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training Qwen Technical Report

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-18T12:51:23.561834Z

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 88a4699e-ac36-4b88-b175-bdf47f50ee3d · outbound

This paper cites $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-18T12:51:23.555450Z

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 e54d6d5a-6128-45e4-bc90-617321a1110f · outbound

This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-18T12:51:23.551034Z

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 0e2f8e2a-0fd3-4d05-8f46-cd7ebcb787fd · outbound

This paper cites DiWA: Diffusion Policy Adaptation with World Models.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training DiWA: Diffusion Policy Adaptation with World Models

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-18T12:51:23.541634Z

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 0b5bd41e-3c99-44ec-a815-beaad8d3931f · outbound

This paper cites Reinforcement Learning for Long-Horizon Interactive LLM Agents.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training Reinforcement Learning for Long-Horizon Interactive LLM Agents

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-18T12:51:23.567751Z

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 d692c99c-0a00-4d60-82e3-9baf5b1a97cc · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-18T12:51:23.507791Z

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 16b59c80-6efc-44d0-b78a-97f47693ec61 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-18T12:51:23.513586Z

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 1a755f0a-c73b-4890-8ce8-acbb321cbc23 · outbound

This paper cites Mastering Atari with Discrete World Models.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training Mastering Atari with Discrete World Models

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-05-18T12:51:23.493368Z

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 0bf917a1-ce2c-4b1c-9be6-5d5b4f46a41b · outbound

This paper cites IRL-VLA: Training an Vision-Language-Action Policy via Reward World Model.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training IRL-VLA: Training an Vision-Language-Action Policy via Reward World Model

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-18T12:51:23.519299Z

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 892fc72e-fa05-42b6-925e-a9e40d79836a · outbound

This paper cites Improved Baselines with Visual Instruction Tuning.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training Improved Baselines with Visual Instruction Tuning

Reference 12

Resolution: metadata mismatch; local_arxiv, observed 2026-05-18T12:51:23.487852Z

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 8d181353-c753-4fc7-86e6-b7d7f83f3802 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training DINOv2: Learning Robust Visual Features without Supervision

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-18T12:51:23.532665Z

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 193d796d-07f2-4d6b-aee5-002dfeeb6fd2 · outbound

This paper cites FAST: Efficient Action Tokenization for Vision-Language-Action Models.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training FAST: Efficient Action Tokenization for Vision-Language-Action Models

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-18T12:51:23.523541Z

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 90216497-33a5-4286-b5a0-8dcff1a2a255 · outbound

This paper cites Proximal Policy Optimization Algorithms.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training Proximal Policy Optimization Algorithms

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-18T12:51:23.502953Z

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 835a556d-485d-4dac-9d28-64bcf6504dfc · outbound

This paper cites Interactive Post-Training for Vision-Language-Action Models.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training Interactive Post-Training for Vision-Language-Action Models

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-21T14:25:47.344448Z

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 391b67e3-2e37-4c2c-84cf-bcaaefd7d91d · outbound

This paper cites Octo: An Open-Source Generalist Robot Policy.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training Octo: An Open-Source Generalist Robot Policy

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-18T12:51:23.528191Z

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 5133c92a-0385-48c1-8aac-868ba565fc7a · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training LLaMA: Open and Efficient Foundation Language Models

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-05-18T12:51:23.497922Z

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 aa2d2470-dc8d-469f-8fff-fa12c7cf493b · outbound

This paper cites CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-05-18T12:51:23.476018Z

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 1a9473d5-e214-47ed-a5de-786ffe777b96 · outbound

This paper cites A Survey of Autonomous Driving: Common Practices and Emerging Technologies.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training A Survey of Autonomous Driving: Common Practices and Emerging Technologies

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-18T12:51:23.136173Z

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 3a231ac3-6172-4b80-a49c-8436e34c28df · outbound

This paper cites an unresolved cited work.

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-05-18T12:51:24.544937Z

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.

Pith citing papers

Observation a39f2f28-0a23-48f3-ac3b-d2a29ee85cd3 · inbound

World-VLA-Loop: Closed-Loop Learning of Video World Model and VLA Policy cites this paper.

World-VLA-Loop: Closed-Loop Learning of Video World Model and VLA Policy World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T03:56:47.187007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c99e8dc-5061-4671-9e79-1eb6ca5d2a40 · inbound

DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos cites this paper.

DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 104

Resolution: verified exact; local_arxiv, observed 2026-05-16T17:02:34.219230Z

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 3bdffa80-5c52-44b7-9884-71a137853a13 · inbound

Towards Long-Lived Robots: Continual Learning VLA Models via Reinforcement Fine-Tuning cites this paper.

Towards Long-Lived Robots: Continual Learning VLA Models via Reinforcement Fine-Tuning World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-05-21T14:10:13.361844Z

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 58196975-9998-4025-9842-2eca9a277942 · inbound

WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL cites this paper.

WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T23:23:52.562350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71c88fad-a9b5-4a7f-b2be-7ada6c9808c1 · inbound

VLANeXt: Recipes for Building Strong VLA Models cites this paper.

VLANeXt: Recipes for Building Strong VLA Models World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-05-21T13:00:09.868211Z

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 3663fc50-efd7-4ecf-8058-96f63c15af24 · inbound

Video Generation Models as World Models: Efficient Paradigms, Architectures and Algorithms cites this paper.

Video Generation Models as World Models: Efficient Paradigms, Architectures and Algorithms World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 206

Resolution: verified exact; local_arxiv, observed 2026-05-14T01:38:35.951488Z

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 f69f81ce-1e58-498d-8456-ff24ecd6251e · inbound

World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems cites this paper.

World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-10T11:30:19.058538Z

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 9df8fdc2-0534-4963-84c2-95e4ddce97df · inbound

Hi-WM: Human-in-the-World-Model for Scalable Robot Post-Training cites this paper.

Hi-WM: Human-in-the-World-Model for Scalable Robot Post-Training World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-05-11T14:36:07.495875Z

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 5eba779d-c681-40df-8d51-8d7a23f4a924 · inbound

One Token Per Frame: Reconsidering Visual Bandwidth in World Models for VLA Policy cites this paper.

One Token Per Frame: Reconsidering Visual Bandwidth in World Models for VLA Policy World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-05-11T03:40:53.563722Z

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 663c2bd7-4df7-4f41-83ef-9783a5cd42dd · inbound

One Token Per Frame: Reconsidering Visual Bandwidth in World Models for VLA Policy cites this paper.

One Token Per Frame: Reconsidering Visual Bandwidth in World Models for VLA Policy World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-05-12T07:26:29.140872Z

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 0dbd37a9-7ff7-496a-84d2-00cf0e3eed21 · inbound

One Token Per Frame: Reconsidering Visual Bandwidth in World Models for VLA Policy cites this paper.

One Token Per Frame: Reconsidering Visual Bandwidth in World Models for VLA Policy World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-05-15T06:19:49.821725Z

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 4b6f5c79-37d2-4272-af4f-69d874dc3b0e · inbound

ALAM: Algebraically Consistent Latent Action Model for Vision-Language-Action Models cites this paper.

ALAM: Algebraically Consistent Latent Action Model for Vision-Language-Action Models World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-05-12T06:31:24.311493Z

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 3846c8cf-ba24-4056-bd2c-3f3460b8da1a · inbound

ALAM: Algebraically Consistent Latent Action Model for Vision-Language-Action Models cites this paper.

ALAM: Algebraically Consistent Latent Action Model for Vision-Language-Action Models World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-05-14T21:17:59.512459Z

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 0a8ecc01-d235-4f80-9c16-dbda403f7157 · inbound

Learning Action Manifold with Multi-view Latent Priors for Robotic Manipulation cites this paper.

Learning Action Manifold with Multi-view Latent Priors for Robotic Manipulation World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:27:18.555877Z

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 94eda5dc-5e1d-4375-b5d4-7d14f5e7616e · inbound

World Action Models: The Next Frontier in Embodied AI cites this paper.

World Action Models: The Next Frontier in Embodied AI World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:18.222721Z

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 e350c569-99bb-40ab-a15e-6460dc5bb4c4 · inbound

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

Reinforcing VLAs in Task-Agnostic World Models World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-05-13T04:27:14.346952Z

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 83250fa3-dae4-452b-9f49-a028b296c522 · inbound

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

Reinforcing VLAs in Task-Agnostic World Models World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-05-21T08:14:03.137453Z

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 e536e61d-c586-4cd2-9aad-55f909d869e7 · inbound

DyGRO-VLA: Cross-Task Scaling of Vision-Language-Action Models via Dynamic Grouped Residual Optimization cites this paper.

DyGRO-VLA: Cross-Task Scaling of Vision-Language-Action Models via Dynamic Grouped Residual Optimization World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 190

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T12:43:17.190885Z

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 01949bb5-acef-458f-8bd7-702501e7251d · inbound

WorldArena 2.0: Extending Embodied World Model Benchmarking on Modality, Functionality and Platform cites this paper.

WorldArena 2.0: Extending Embodied World Model Benchmarking on Modality, Functionality and Platform World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-05-20T11:03:13.598719Z

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 fe759a5b-3562-459c-8262-e14b7ed8e015 · inbound

World Models for Robotic Manipulation: A Survey cites this paper.

World Models for Robotic Manipulation: A Survey World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 99

Resolution: verified exact; local_arxiv, observed 2026-06-29T12:33:25.086511Z

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 f5372f02-a244-467a-8da8-82c7a1022c6f · inbound

iMaC: Translating Actions into Motion and Contact Images for Embodied World Models cites this paper.

iMaC: Translating Actions into Motion and Contact Images for Embodied World Models World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-07-03T01:47:31.891774Z

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 fefe26b1-47af-45cb-85ed-31a4a97f6ab1 · inbound

World Pilot: Steering Vision-Language-Action Models with World-Action Priors cites this paper.

World Pilot: Steering Vision-Language-Action Models with World-Action Priors World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-07-03T11:18:03.358870Z

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 b85a1ac1-6acc-4769-9ef8-b27965d112bc · inbound

How Should World Models Be Evaluated for Embodied Decision-Making? A Decision-Making-Centric Position cites this paper.

How Should World Models Be Evaluated for Embodied Decision-Making? A Decision-Making-Centric Position World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-06-30T10:34:36.498190Z

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 37a32824-084f-4ae0-9251-8509f1759774 · inbound

SafeDojo: Safe Reinforcement Learning for VLA via Interactive World Model cites this paper.

SafeDojo: Safe Reinforcement Learning for VLA via Interactive World Model World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-07-03T17:18:44.356532Z

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 f43df9bf-1190-4e02-a40f-8b410ea54780 · inbound

DVG-WM: Disentangled Video Generation Enables Efficient Embodied World Model for Robotic Manipulation cites this paper.

DVG-WM: Disentangled Video Generation Enables Efficient Embodied World Model for Robotic Manipulation World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 43

Resolution: metadata mismatch; local_arxiv, observed 2026-07-01T10:55:42.275069Z

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 03cfdaba-45a9-48fa-85d1-6cbcc67ef2f2 · inbound

DVG-WM: Disentangled Video Generation Enables Efficient Embodied World Model for Robotic Manipulation cites this paper.

DVG-WM: Disentangled Video Generation Enables Efficient Embodied World Model for Robotic Manipulation World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 43

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T21:58:58.463141Z

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 0be2ebab-9991-43c7-a4db-9cea96bb218c · inbound

WorldSample: Closed-loop Real-robot RL with World Modelling cites this paper.

WorldSample: Closed-loop Real-robot RL with World Modelling World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-07-03T10:58:02.405856Z

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 8bc31f8b-0971-4e50-b01f-c0085c241635 · inbound

TACO: TActile World Model as a Self-COrrector forScalable VLA Post-Training cites this paper.

TACO: TActile World Model as a Self-COrrector forScalable VLA Post-Training World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 43

Resolution: unresolved; no resolver link, observed 2026-07-12T06:41:57.276146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 034631dc-3fd5-46d0-9294-3f70a4f7aa0d · inbound

WCog-VLA: A Dual-Level World-Cognitive Vision-Language-Action Model for End-to-End Autonomous Driving cites this paper.

WCog-VLA: A Dual-Level World-Cognitive Vision-Language-Action Model for End-to-End Autonomous Driving World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 51

Resolution: metadata mismatch; local_arxiv, observed 2026-07-10T08:36:59.805192Z

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 c25b56c0-ccd4-49ee-a326-1339593967ae · inbound

Wonder: Video World Model Done Better cites this paper.

Wonder: Video World Model Done Better World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T00:52:27.635517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5690d3ed-d876-470b-8fff-4147553cc1c2 · inbound

BWM: A Low-Cost High-Fidelity World Simulator for Robot Learning cites this paper.

BWM: A Low-Cost High-Fidelity World Simulator for Robot Learning World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T09:43:32.425225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.