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Paper Citation Record · LEDGER

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch

As of 17 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 3 inbound Pith citation observations for arXiv:2607.13960.

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

pith.paper-citation-record.v1
2607.13960 v3

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T03:16:53.139337Z

measured 71 of 71 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:52:22.262106Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T14:52:36.051441Z

Reference resolution

68 of 68 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved68
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  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 81faaba5-0763-4ce7-bbe1-9128ae36e20e · outbound

This paper cites Cosmos World Foundation Model Platform for Physical AI.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Cosmos World Foundation Model Platform for Physical AI

Reference 1

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source=pdf_text observed=2026-08-02T03:16:51.468627Z digest=sha256:276b1f4f7d52db1dd3f00b1c8179500e712a3cd5155686239f3f0674bfc45f1d

Observation 1d25b611-2925-4a85-b2b2-b76883d1024e · outbound

This paper cites Cosmos 3: Omnimodal World Models for Physical AI.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Cosmos 3: Omnimodal World Models for Physical AI

Reference 2

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source=pdf_text observed=2026-08-02T03:16:51.556914Z digest=sha256:1702c5992922230ddac4bccbc8459b9ce70de1c03e3bf71f7d97d118f3405175

Observation babce225-27ae-4451-b350-a6b723bbe9cf · outbound

This paper cites Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control

Reference 3

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source=pdf_text observed=2026-08-02T03:16:51.693644Z digest=sha256:427a31425cfb6b67806c049ad6f5a351190b6abba4eb390c2a228d3993ae3434

Observation d8e77484-50ff-43b8-9933-97ce411d7847 · outbound

This paper cites Motus: A unified latent action world model.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Motus: A unified latent action world model

Reference 4

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source=pdf_text observed=2026-08-02T03:16:51.899948Z digest=sha256:d649b75f10e81caa22afd0679538ae10e3a7102d350f11415f5698c92eb7f6f6

Observation 5a4b76ed-6203-4d2c-8562-57f4e686a7a2 · outbound

This paper cites GR00T N1: An Open Foundation Model for Generalist Humanoid Robots.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

Reference 5

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source=pdf_text observed=2026-08-02T03:16:52.108500Z digest=sha256:4b4c37f0ce3a0b57318a74064c5225fb964ac3e0bba4c422ca8699c4f79c8ad1

Observation 60c9e266-4eb8-445b-ae5d-ea01856f42d6 · outbound

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

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 6

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source=pdf_text observed=2026-08-02T03:16:52.225686Z digest=sha256:e5c84f206948e0ebd86da84d2f7334098237d989d63cb37f1db10d9dea522a23

Observation 77976c29-3576-485f-8742-f29c62330568 · outbound

This paper cites AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems

Reference 7

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source=pdf_text observed=2026-08-02T03:16:52.333915Z digest=sha256:3f62289ae1e472ad033bc7ddaf9625e6fd7aa6e3f7485494b360ee4f8e0a1a09

Observation 8f28fe47-9861-4ba3-b6f8-e716e3451c1b · outbound

This paper cites WorldVLA: Towards Autoregressive Action World Model.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch WorldVLA: Towards Autoregressive Action World Model

Reference 8

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source=pdf_text observed=2026-08-02T03:16:52.499150Z digest=sha256:a1880c98e03eafeb1913f8169dfaaf7cab9227acf74d81ca55b6069ab9699ec8

Observation 80e94dfd-9b4c-4b44-8e76-1396f1a68e56 · outbound

This paper cites Lawam: Latent world action models for efficient dynamics-aware robot policies.arXiv preprint arXiv:2606.15768, 2026.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Lawam: Latent world action models for efficient dynamics-aware robot policies.arXiv preprint arXiv:2606.15768, 2026

Reference 9

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source=pdf_text observed=2026-08-02T03:16:52.575487Z digest=sha256:e2bbc3ca77426cb2731cde3dad87185cbfc4772a783394c8633d9101a881a6a3

Observation 5cdc9b33-c63c-4668-bd50-fa32edd63f59 · outbound

This paper cites Unimax: Fairer and more effective language sampling for large-scale multilingual pretraining.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Unimax: Fairer and more effective language sampling for large-scale multilingual pretraining

Reference 10

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source=pdf_text observed=2026-08-02T03:16:52.653824Z digest=sha256:def043e12f3c1949eb61777e8796edc0fe3b9e80aa84403be661726d8f65b36d

Observation c07ed61b-df65-4235-b0ff-9d5e146efd88 · outbound

This paper cites Emma: Generalizing real-world robot manipulation via generative visual transfer.arXiv preprint arXiv:2509.22407, 2025.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Emma: Generalizing real-world robot manipulation via generative visual transfer.arXiv preprint arXiv:2509.22407, 2025

Reference 11

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source=pdf_text observed=2026-08-02T03:16:52.735830Z digest=sha256:d8050fc6f070d368637d299ac83ba92931f6b71795a081e42beb708d7e8caf36

Observation fa5e265c-d13c-4a96-b1bf-b72221d64e80 · outbound

This paper cites Learning universal policies via text-guided video generation.Advances in neural information processing systems, 36:9156–9172, 2023.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Learning universal policies via text-guided video generation.Advances in neural information processing systems, 36:9156–9172, 2023

Reference 12

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source=pdf_text observed=2026-08-02T03:16:52.800361Z digest=sha256:c828d81c0ba1c6ca16e2e492b0939eaaa884ef61defc74c3b19fd09bcc32a115

Observation 82a573c0-c1bb-4763-9889-8c269e849b00 · outbound

This paper cites Scaling rectified flow transformers for high-resolution image synthesis.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Scaling rectified flow transformers for high-resolution image synthesis

Reference 13

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source=pdf_text observed=2026-08-02T03:16:52.871383Z digest=sha256:0c8acab469e82b55326fc6642bc53a5e80a6869847f076022e1ed47f2b13e699

Observation f5994737-809d-4e45-ad7e-2d7e255c052e · outbound

This paper cites $\pi^{*}_{0.6}$: a VLA That Learns From Experience.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch $\pi^{*}_{0.6}$: a VLA That Learns From Experience

Reference 14

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source=pdf_text observed=2026-08-02T03:16:52.931575Z digest=sha256:36272e1752b0d52e7be31b3e3143aa9ae05bdbfd9d365c08488ee2c9cfbcbb00

Observation 2468ae23-d88c-4741-9bbd-173d0461a61f · outbound

This paper cites $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization

Reference 15

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source=pdf_text observed=2026-08-02T03:16:52.979043Z digest=sha256:4bd09962bb60b1db8d4c37efa8c41e1655735ecdb999bfecf00f164034f7be38

Observation ba7bc83f-654d-4bc5-9c77-c9215aa66c3f · outbound

This paper cites ${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch ${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities

Reference 16

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source=pdf_text observed=2026-08-02T03:16:52.982239Z digest=sha256:9f4081f26d94251ceacc07ff85a76036a0e9cb15e9b361bfdbf21585bacb9152

Observation ab16b418-1055-4637-92fe-4f92feb62da8 · outbound

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

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL

Reference 17

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source=pdf_text observed=2026-08-02T03:16:52.985410Z digest=sha256:c9e600d85980699993623722ae30c937701e95304a2a425c3e5bbf7d8d0ea493

Observation 22e95b37-6ee4-43eb-a173-e1b20b5f41f5 · outbound

This paper cites autoresearch, 2026.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch autoresearch, 2026

Reference 18

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source=pdf_text observed=2026-08-02T03:16:52.988893Z digest=sha256:400758d83693bbe71ab8b980acffdce491893760e492c2c35357cc3f5464e9c8

Observation 5e11643f-9ba0-43a2-b39f-4f0f9ee238ff · outbound

This paper cites Freeaction: Training-free techniques for enhanced fidelity of trajectory-to-video generation.arXiv preprint arXiv:2509.24241, 2025.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Freeaction: Training-free techniques for enhanced fidelity of trajectory-to-video generation.arXiv preprint arXiv:2509.24241, 2025

Reference 19

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source=pdf_text observed=2026-08-02T03:16:52.991539Z digest=sha256:672a566e83112903db2a1556a9aa738c7c205500413c97e32daf3328fb129050

Observation 045f3ec5-38d5-4e8d-a152-7c4839b664f7 · outbound

This paper cites A path towards autonomous machine intelligence version 0.9.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch A path towards autonomous machine intelligence version 0.9

Reference 20

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source=pdf_text observed=2026-08-02T03:16:52.994236Z digest=sha256:48d7999a45253d65e3c54a3091f0e0019d631f734a2e62258d055129ffdd1673

Observation 4e7d7400-9f16-4fb5-a35b-1f1e02d55093 · outbound

This paper cites Mimicdreamer: Aligning human and robot demonstrations for scalable vla training.arXiv preprint arXiv:2509.22199, 2025.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Mimicdreamer: Aligning human and robot demonstrations for scalable vla training.arXiv preprint arXiv:2509.22199, 2025

Reference 21

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source=pdf_text observed=2026-08-02T03:16:52.997448Z digest=sha256:51149ac586ec89945763e2b93169ade3d830cee1c6d72698f8bbbd9978f3c5b1

Observation f7463fcd-5db7-4323-b88e-2d205fdac65e · outbound

This paper cites Causal World Modeling for Robot Control.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Causal World Modeling for Robot Control

Reference 22

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source=pdf_text observed=2026-08-02T03:16:53.000407Z digest=sha256:b3ed8609dd73c2cd4717181bbe15d3b1df6de2055dca101edca5d46800c8803c

Observation 6515e2db-737a-42b4-901d-1e096fb72f73 · outbound

This paper cites Flow Matching for Generative Modeling.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Flow Matching for Generative Modeling

Reference 23

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source=pdf_text observed=2026-08-02T03:16:53.003738Z digest=sha256:a68593ab0e1117aa2293200c1765f9a6b5748581aa428f140223817c0d1d1262

Observation ac94fcc8-2632-403f-8649-9758b09b65dd · outbound

This paper cites Timestep embedding tells: It’s time to cache for video diffusion model.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Timestep embedding tells: It’s time to cache for video diffusion model

Reference 24

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source=pdf_text observed=2026-08-02T03:16:53.007000Z digest=sha256:355832880ac07dcb0f004398cf8bbb1051e8eeaa6f4b8d96e53e296e62f61123

Observation 222a2b9b-792b-42a4-8523-e7d1b5fdc4b2 · outbound

This paper cites Robotransfer: Controllable geometry-consistent video diffusion for manipulation policy transfer.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Robotransfer: Controllable geometry-consistent video diffusion for manipulation policy transfer

Reference 25

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source=pdf_text observed=2026-08-02T03:16:53.009824Z digest=sha256:22ad56bcd351f047583b99a6cb4c1d55ead1c434c1046a172a5625ba83bbb5e2

Observation c7cf7d4b-ef9f-416b-8dd3-c10caa3b1530 · outbound

This paper cites Rdt-1b: a diffusion foundation model for bimanual manipulation.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Rdt-1b: a diffusion foundation model for bimanual manipulation

Reference 26

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source=pdf_text observed=2026-08-02T03:16:53.013067Z digest=sha256:91798a0667aef4ec55e59a3915f547428e2a36fd555bc0778f90006ebf7eaa84

Observation e65b3ed6-65b7-459c-be83-84e080132ed3 · outbound

This paper cites Being-H0.7: A Latent World-Action Model from Egocentric Videos.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Being-H0.7: A Latent World-Action Model from Egocentric Videos

Reference 27

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source=pdf_text observed=2026-08-02T03:16:53.016000Z digest=sha256:89bb97102f5eb550fd5265c5c6fc70b38ca2ef865f2fe17a8a7f120c14dfb6a7

Observation 159fbbe0-8ae9-4750-8623-802e11e201f7 · outbound

This paper cites ViVa: A Video-Generative Value Model for Robot Reinforcement Learning.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch ViVa: A Video-Generative Value Model for Robot Reinforcement Learning

Reference 28

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source=pdf_text observed=2026-08-02T03:16:53.019586Z digest=sha256:4c8d41147a2d65f57167e3398b339627c164ae444baa856c35b48bee9e4f3ef4

Observation 84001fe0-c77a-4c4b-9298-2e1b46168bff · outbound

This paper cites Dit4dit: Jointly modeling video dynamics and actions for generalizable robot control.arXiv preprint arXiv:2603.10448,.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Dit4dit: Jointly modeling video dynamics and actions for generalizable robot control.arXiv preprint arXiv:2603.10448,

Reference 29

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source=pdf_text observed=2026-08-02T03:16:53.022701Z digest=sha256:34c4c51e95b1a38b7764bc00e1700a81039e8fcd7340f8d82e750fafb22cbbca

Observation 59813907-6f9a-448b-bee4-86275d0e5643 · outbound

This paper cites ReconDreamer-RL: Enhancing Reinforcement Learning via Diffusion-based Scene Reconstruction.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch ReconDreamer-RL: Enhancing Reinforcement Learning via Diffusion-based Scene Reconstruction

Reference 30

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source=pdf_text observed=2026-08-02T03:16:53.025755Z digest=sha256:69b1865f59ff10c2436ef1fed661d1e2d85bb8ed46802c8e6da9f6606af1eb92

Observation 7f33f67e-8385-4438-a9f9-1609f7d39d74 · outbound

This paper cites Swiftvla: Unlocking spatiotemporal dynamics for lightweight vla models at minimal overhead.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Swiftvla: Unlocking spatiotemporal dynamics for lightweight vla models at minimal overhead

Reference 31

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source=pdf_text observed=2026-08-02T03:16:53.029636Z digest=sha256:81709eb0ba81672f0c841fcda014dc6113f1b990d68cc8cef08bed3894b36e44

Observation 41af6501-2b9c-4a8f-8cfe-8d0f919a750f · outbound

This paper cites mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs

Reference 32

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source=pdf_text observed=2026-08-02T03:16:53.032905Z digest=sha256:a95bacef4bbcb84b188b7b21492cbf51bd9b7fa06915fb6c3ea5fb399373844a

Observation 30ece4c3-f35d-461f-afc8-a7b7f14a1335 · outbound

This paper cites Videovla: Video generators can be generalizable robot manipulators.Advances in neural information processing systems, 38:95597–95621, 2026.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Videovla: Video generators can be generalizable robot manipulators.Advances in neural information processing systems, 38:95597–95621, 2026

Reference 33

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source=pdf_text observed=2026-08-02T03:16:53.036196Z digest=sha256:0690f1722899397758538f410242f8824c12b105eb3ef95b9fa98394d3e30a94

Observation ea5750a5-3377-49a2-824d-8ff8f7b4e8ad · outbound

This paper cites World guidance: World modeling in condition space for action generation.arXiv preprint arXiv:2602.22010, 2026.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch World guidance: World modeling in condition space for action generation.arXiv preprint arXiv:2602.22010, 2026

Reference 34

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source=pdf_text observed=2026-08-02T03:16:53.039159Z digest=sha256:3926f9b183d09fc6876e245f946d5eedae121d3d9e795927af71f390595433d4

Observation c3adf8a5-f273-422d-af66-69b6facc424a · outbound

This paper cites AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation

Reference 35

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source=pdf_text observed=2026-08-02T03:16:53.041846Z digest=sha256:31aaa42a1b8078f974107739d33926f3ba3afa7e3d7c6f4fda8339624a93d329

Observation 81fcf3f0-f34e-41e1-a23a-8b16f32775ea · outbound

This paper cites Gigabrain-0: A world model-powered vision-language-action model.arXiv preprint arXiv:2510.19430, 2025.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Gigabrain-0: A world model-powered vision-language-action model.arXiv preprint arXiv:2510.19430, 2025

Reference 36

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source=pdf_text observed=2026-08-02T03:16:53.045406Z digest=sha256:03ab992b322676603e8e589a408671dcfefaa1f0a1567935383c77a8437786b3

Observation 7cbb8625-d910-46b4-8299-afe91d875d3d · outbound

This paper cites Gigabrain-0.5 m*: a vla that learns from world model-based reinforcement learning.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Gigabrain-0.5 m*: a vla that learns from world model-based reinforcement learning

Reference 37

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source=pdf_text observed=2026-08-02T03:16:53.048225Z digest=sha256:7c42c46e587d25d363ed1cd186f4482b3726dfd6394ece69c47790bb28bd7115

Observation db5c560a-1848-43f8-bfd4-a6672ac7135f · outbound

This paper cites Gigaworld-0: World models as data engine to empower embodied ai.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Gigaworld-0: World models as data engine to empower embodied ai

Reference 38

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source=pdf_text observed=2026-08-02T03:16:53.051352Z digest=sha256:fece86f5a89e2acbe00fd86523f9915ab09d66676b6b0f5bb49911ac813aefaa

Observation 828fb644-3e25-4b96-acba-6bdf3bb4c945 · outbound

This paper cites GigaWorld-1: A Roadmap to Build World Models for Robot Policy Evaluation.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch GigaWorld-1: A Roadmap to Build World Models for Robot Policy Evaluation

Reference 39

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source=pdf_text observed=2026-08-02T03:16:53.054370Z digest=sha256:6828aefab0d096e56e52c9fbd8a490add34d441ea4cc3b8a464a2b821e239dc7

Observation ca3b4035-31c5-4568-997f-bb7713de020d · outbound

This paper cites Motubrain: An Advanced World Action Model for Robot Control.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Motubrain: An Advanced World Action Model for Robot Control

Reference 40

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source=pdf_text observed=2026-08-02T03:16:53.057505Z digest=sha256:f280f4101a7ccb8a1eddb840935187e3820c4d43338ff200beafa203bac0b95d

Observation 99d73420-2d6b-4919-8202-08f2e4b6ac0f · outbound

This paper cites Wan: Open and Advanced Large-Scale Video Generative Models.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Wan: Open and Advanced Large-Scale Video Generative Models

Reference 41

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source=pdf_text observed=2026-08-02T03:16:53.060585Z digest=sha256:c295d8781652b36bb1d664149b8066b1c4cdd725327e4d65183b4d6d2b82f5da

Observation 5b1d2416-3b42-4457-8444-9e3082bb482e · outbound

This paper cites EmbodieDreamer: Advancing Real2Sim2Real Transfer for Policy Training via Embodied World Modeling.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch EmbodieDreamer: Advancing Real2Sim2Real Transfer for Policy Training via Embodied World Modeling

Reference 42

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source=pdf_text observed=2026-08-02T03:16:53.063727Z digest=sha256:f51c3df5cb13e107adc35a888e9d9550e74eb904e4cb8671aaf6501102d9e398

Observation bc282f01-ba83-4c0a-8d79-19f53ef1c47c · outbound

This paper cites Humandreamer-x: Photorealistic single-image human avatars reconstruction via gaussian restoration.arXiv preprint arXiv:2504.03536, 2025.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Humandreamer-x: Photorealistic single-image human avatars reconstruction via gaussian restoration.arXiv preprint arXiv:2504.03536, 2025

Reference 43

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source=pdf_text observed=2026-08-02T03:16:53.066630Z digest=sha256:7df61bf6b3aee356c0982c0c931cc73758b631be8bb1a7a0991cd95d25f35fe1

Observation 65930336-6fa1-4fb0-845b-e68251452a6a · outbound

This paper cites ReconPhys: Reconstruct Appearance and Physical Attributes from Single Video.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch ReconPhys: Reconstruct Appearance and Physical Attributes from Single Video

Reference 44

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source=pdf_text observed=2026-08-02T03:16:53.069617Z digest=sha256:57f6ed9df3c97cbc268fb8e5104188fdfe2ddf412b4c4cc5070d178e35ad9b2e

Observation 38d97754-3db9-429b-80f5-58f896e35cbb · outbound

This paper cites Drivedreamer: Towards real-world-drive world models for autonomous driving.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Drivedreamer: Towards real-world-drive world models for autonomous driving

Reference 45

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source=pdf_text observed=2026-08-02T03:16:53.072452Z digest=sha256:552c503dd9c4f6c38f47829c68d005e20d78bebde350d06f4c8f31da851fe6c7

Observation 0f539b69-ceb2-4482-bd59-174de125e61f · outbound

This paper cites WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens

Reference 46

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source=pdf_text observed=2026-08-02T03:16:53.075596Z digest=sha256:e8b30a88b02bb23518e720bf5c404e63dd48fde7b839b01c93fc8bb82f46b264

Observation 2dfd3ecc-55a7-4184-9968-c72f9945879b · outbound

This paper cites Egovid-5m: A large-scale video-action dataset for egocentric videos generation.Advances in Neural Information Processing Systems, 38, 2026.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Egovid-5m: A large-scale video-action dataset for egocentric videos generation.Advances in Neural Information Processing Systems, 38, 2026

Reference 47

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source=pdf_text observed=2026-08-02T03:16:53.078807Z digest=sha256:8fca29b904e3793e4a48732255b854fafdf5604dbc1c86da75b83d804a8dd4bd

Observation c951ba54-fe9f-4c5e-b278-e6954a80343d · outbound

This paper cites Unleashing large-scale video generative pre-training for visual robot manipulation.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Unleashing large-scale video generative pre-training for visual robot manipulation

Reference 48

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source=pdf_text observed=2026-08-02T03:16:53.081537Z digest=sha256:6c53a8a3197dc903a595339b09ee480d72a630f37612098496061a62d3da3fe9

Observation 07de1111-fb3f-4882-8fe0-9c06efbab001 · outbound

This paper cites RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation

Reference 49

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source=pdf_text observed=2026-08-02T03:16:53.084538Z digest=sha256:bd9c94c262bff31a96a8a7b231b834d748676159fd8ad06d0158a5abeeaf9a11

Observation acc6125a-cb03-47c6-8bec-9d1dbbd6b11e · outbound

This paper cites A Pragmatic VLA Foundation Model.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch A Pragmatic VLA Foundation Model

Reference 50

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source=pdf_text observed=2026-08-02T03:16:53.087819Z digest=sha256:dab8e0f16e4c1753bbc3894ac09a20ab80cb70901ec6627c8848e00e568bd0bd

Observation 62a1fd9c-bbcc-4b8a-a679-944154b2e172 · outbound

This paper cites Pandora: Towards General World Model with Natural Language Actions and Video States.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Pandora: Towards General World Model with Natural Language Actions and Video States

Reference 51

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source=pdf_text observed=2026-08-02T03:16:53.090709Z digest=sha256:6c79282e43b68b015f590ce5ca8193a1de9866b06bc9898d57d8432d2895324f

Observation 6c82ef15-1974-4bfc-b7f2-e010d10b3570 · outbound

This paper cites S-vam: Shortcut video-action model by self-distilling geometric and semantic foresight.arXiv preprint arXiv:2603.16195, 2026.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch S-vam: Shortcut video-action model by self-distilling geometric and semantic foresight.arXiv preprint arXiv:2603.16195, 2026

Reference 52

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source=pdf_text observed=2026-08-02T03:16:53.093687Z digest=sha256:3bafea5db303d184d4171e09464bd1343021f380cbbe85a9fd95c9509f5e1516

Observation 8f8d4b39-a49b-4f45-84c4-3dd93d6a9a55 · outbound

This paper cites Vla-r1: Enhancing reasoning in vision-language-action models.arXiv preprint arXiv:2510.01623, 2025.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Vla-r1: Enhancing reasoning in vision-language-action models.arXiv preprint arXiv:2510.01623, 2025

Reference 53

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source=pdf_text observed=2026-08-02T03:16:53.096363Z digest=sha256:885a92bdef88cddda0027f230703d7be4a8a8dc69ce1a32e948cb922920c8b4a

Observation 0e2be43d-316a-4b64-bad9-c41302c51ee2 · outbound

This paper cites HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models

Reference 54

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source=pdf_text observed=2026-08-02T03:16:53.099063Z digest=sha256:5c1d6a5e885f051954c0fe57dcda5f0f4d144eec82d0500db7dbbddd80c0e283

Observation b487279f-f53f-4c5c-b432-2b1d050e526d · outbound

This paper cites Gigaworld-policy: An efficient action-centered world–action model.arXiv preprint arXiv:2603.17240, 2026.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Gigaworld-policy: An efficient action-centered world–action model.arXiv preprint arXiv:2603.17240, 2026

Reference 55

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source=pdf_text observed=2026-08-02T03:16:53.101931Z digest=sha256:4d2df7b0aa17c72b22aa4ba7a85aeff33a361622b33e3edef05de1d164463d13

Observation 777d1df7-f0ff-4dba-b766-65dada147621 · outbound

This paper cites World Action Models are Zero-shot Policies.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch World Action Models are Zero-shot Policies

Reference 56

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source=pdf_text observed=2026-08-02T03:16:53.104585Z digest=sha256:334d2446f2cea9ac97e167b9ffc28aa87a2139f51aa0bf56bfb8007de606b77f

Observation 8e586a8d-d2b7-4bc0-a9a8-ac2f7f8195a1 · outbound

This paper cites Fast-WAM: Do World Action Models Need Test-time Future Imagination?.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Fast-WAM: Do World Action Models Need Test-time Future Imagination?

Reference 57

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source=pdf_text observed=2026-08-02T03:16:53.107666Z digest=sha256:ea955dc98cd9575472e5e3ae9ae2e34f78d8f5401f05473c6798add3ea003b8e

Observation 7b72f99b-1fd9-434e-aefa-3bba96e397d8 · outbound

This paper cites Igniting vlms toward the embodied space.arXiv preprint arXiv:2509.11766, 2025.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Igniting vlms toward the embodied space.arXiv preprint arXiv:2509.11766, 2025

Reference 58

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source=pdf_text observed=2026-08-02T03:16:53.110746Z digest=sha256:3e3f4cd549fbd89c76e77c69e53d72f81e1b692d6e091099c803bcfcb678735d

Observation c9bd6962-2532-4ba5-b804-06f33509fbf1 · outbound

This paper cites Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation

Reference 59

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source=pdf_text observed=2026-08-02T03:16:53.113554Z digest=sha256:02111834c37cd482ef342dbfcb24416d5ef4f733bc7e40338a1262e94865c045

Observation 17dce28c-202d-4eb2-9602-11fbc2fd2ff2 · outbound

This paper cites Drivedreamer4d: World models are effective data machines for 4d driving scene representation.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Drivedreamer4d: World models are effective data machines for 4d driving scene representation

Reference 60

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source=pdf_text observed=2026-08-02T03:16:53.116430Z digest=sha256:597bd4af6803c4cb0ba50717946c8cc6aa38ea4dcea01fa5a33b08efeafa3eac

Observation 600fa4ed-481e-44d1-830a-5dda5b12f0a1 · outbound

This paper cites Recondreamer++: Harmonizing generative and reconstructive models for driving scene representation.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Recondreamer++: Harmonizing generative and reconstructive models for driving scene representation

Reference 61

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source=pdf_text observed=2026-08-02T03:16:53.119201Z digest=sha256:74274c94e9befe03d283842c0c3adc858027bc4fb321526a3d293b0a035eb0e5

Observation 2c8cc1bc-e571-4309-b386-c466cf81b9d0 · outbound

This paper cites Drivedreamer-2: Llm-enhanced world models for diverse driving video generation.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Drivedreamer-2: Llm-enhanced world models for diverse driving video generation

Reference 62

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source=pdf_text observed=2026-08-02T03:16:53.121822Z digest=sha256:f4d7aa7cd999107f65a55e1cb3e0a1eff408e98915812d15146ffb62452be18d

Observation 86ba7ecb-287c-494c-96b9-57ef67030699 · outbound

This paper cites Unidrivedreamer: A single-stage multimodal world model for autonomous driving.arXiv preprint arXiv:2602.02002, 2026.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Unidrivedreamer: A single-stage multimodal world model for autonomous driving.arXiv preprint arXiv:2602.02002, 2026

Reference 63

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source=pdf_text observed=2026-08-02T03:16:53.124492Z digest=sha256:62c5da6c552ca0e0d1c3072f6b19a2da43824232852c02811b90748f285be4e2

Observation 0067d6ff-ed34-47cd-974c-ce5b0cfb1df7 · outbound

This paper cites Robodreamer: learning compositional world models for robot imagination.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Robodreamer: learning compositional world models for robot imagination

Reference 64

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source=pdf_text observed=2026-08-02T03:16:53.127533Z digest=sha256:4ca721a39323c10ec2df17cc39666862e0f272f28767a13c4bf037d21d94fe00

Observation fc1fda4b-8a03-457a-adac-fc12d0523c6c · outbound

This paper cites Drivedreamer-policy: A geometry-grounded world-action model for unified generation and planning.arXiv preprint arXiv:2604.01765, 2026.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Drivedreamer-policy: A geometry-grounded world-action model for unified generation and planning.arXiv preprint arXiv:2604.01765, 2026

Reference 65

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source=pdf_text observed=2026-08-02T03:16:53.130193Z digest=sha256:e9bc639e2db6cb59683028daa1ba74958ae772b0771d0ac9698382fa2a1ed090

Observation 327c4baf-ae85-4e77-b149-0a71b3414ab9 · outbound

This paper cites Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets

Reference 66

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source=pdf_text observed=2026-08-02T03:16:53.133370Z digest=sha256:ff0143def2d7d688f79168494ebba104a602d898271d38874ddac243801e80f7

Observation 2818f538-239b-43af-9ef7-d7f8fb425105 · outbound

This paper cites Aether: Geometric-aware unified world modeling.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Aether: Geometric-aware unified world modeling

Reference 67

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source=pdf_text observed=2026-08-02T03:16:53.136626Z digest=sha256:c029565b5205f15f78efbb15880c4745cd974827f0e420b825a43b16fe204a6c

Observation 222db407-7b16-42ae-b6bc-f76c00cbccde · outbound

This paper cites Is sora a world simulator? a comprehensive survey on general world models and beyond.arXiv preprint arXiv:2405.03520, 2024.

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch Is sora a world simulator? a comprehensive survey on general world models and beyond.arXiv preprint arXiv:2405.03520, 2024

Reference 68

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source=pdf_text observed=2026-08-02T03:16:53.139337Z digest=sha256:2f50282ae1a5008582cc0c10c757a81db208a9a3609ff086dff69c7a2d032ffe

Pith citing papers

Observation da858a85-9002-4215-b1cb-ba70cb99ce5c · inbound

PhyAI: Real-Time Physical AI at the Edge, Scalable Rollouts in the Cloud cites this paper.

PhyAI: Real-Time Physical AI at the Edge, Scalable Rollouts in the Cloud GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch

Reference 20

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metadata mismatch
local_arxiv, observed 2026-08-05T14:52:36.054142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-05T14:52:35.416323Z digest=sha256:d29fdb0d4bf59b6c07f131d51726ae6c124aa0edfaf12be512c3d8500cc558ea

Observation 5d3d1349-ee89-46d3-925d-cc90bf716bb0 · inbound

PhyAI: Real-Time Physical AI at the Edge, Scalable Rollouts in the Cloud cites this paper.

PhyAI: Real-Time Physical AI at the Edge, Scalable Rollouts in the Cloud GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-15T14:52:22.262106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T14:52:22.262106Z digest=sha256:5256b3b701b4c6ccf9c207763d97da7f052918ba4c264703f18ec8e50cb1ca89

Observation ea8658ba-0d32-45b6-871b-340b39faf206 · inbound

DreamWAM: Beyond RGB Future Prediction for World Action Models cites this paper.

DreamWAM: Beyond RGB Future Prediction for World Action Models GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T11:59:27.939168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:59:27.939168Z digest=sha256:c27047eee9a3920b8d3e11c85eb2bfe103de33bd2a21adae10eab146836819e5