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Paper Citation Record · LEDGER

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models

As of 8 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2608.05903.

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

pith.paper-citation-record.v1
2608.05903 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T21:29:43.716173Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

  • verified exact2
  • verified fuzzy11
  • unresolved31
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dadd3b6d-1d1e-4b6a-b770-ea4bf7d1ae17 · outbound

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

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Reference 1

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source=arxiv_source observed=2026-08-07T21:29:43.488719Z digest=sha256:cfa056658d68c7a41cb8be9bd80ce80ab97723e208c99eae286c72e509913e8c

Observation 8ba1151d-5371-452e-91b1-460c91d3130c · outbound

This paper cites How learning by reconstruction produces uninformative features for perception.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models How learning by reconstruction produces uninformative features for perception

Reference 2

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raw_fallback, observed 2026-08-07T21:29:45.376602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T21:29:43.494465Z digest=sha256:a52117af479eaae006d54c2422a3689aa490d389f491ce3dfcf94d74ef719a2c

Observation 67c7e4a6-42f9-43da-9f18-700d005cdf3e · outbound

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

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models Motus: A unified latent action world model

Reference 3

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verified fuzzy
raw_fallback, observed 2026-08-07T21:29:45.361494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T21:29:43.499416Z digest=sha256:79701309068e143955b6dda76bab50f18274e5a62a9bf68b9c238be66c3d1721

Observation cbcf53da-3b48-4d9c-b612-f85f6d7918b1 · outbound

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

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

Reference 4

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source=arxiv_source observed=2026-08-07T21:29:43.504625Z digest=sha256:bef519357a74ee527b14922808927204d0e38ccbb3704fbff5483c47b6961cf2

Observation b8465368-931f-416f-ae67-7bbf6c8fa5a7 · outbound

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

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 5

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source=arxiv_source observed=2026-08-07T21:29:43.510016Z digest=sha256:5f35fa2d29d8583a4888208a0dfd348a6dff5099112bf65891492247f8b79598

Observation 56b810ca-046e-4980-954c-5b311bcfc8e5 · outbound

This paper cites UniVLA: Learning to Act Anywhere with Task-centric Latent Actions.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models UniVLA: Learning to Act Anywhere with Task-centric Latent Actions

Reference 6

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source=arxiv_source observed=2026-08-07T21:29:43.515393Z digest=sha256:5c57451cada2cdf466be36e0efd26afca6122959e9535f52f3019b582b1d8939

Observation 6f0a9ed5-f562-418b-a675-192b48f97cf1 · outbound

This paper cites WorldVLA: Towards Autoregressive Action World Model.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models WorldVLA: Towards Autoregressive Action World Model

Reference 7

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source=arxiv_source observed=2026-08-07T21:29:43.521672Z digest=sha256:6c8214380f27488cfef327f1382f567ddb64a9c78e8bf0b704be2aa4db6a60e6

Observation 135364b3-2784-41e8-9d24-52bafda93111 · outbound

This paper cites GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation

Reference 8

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source=arxiv_source observed=2026-08-07T21:29:43.527696Z digest=sha256:dcd79d2df65b0665727de53b108e5619d7ef94725af25ddb1e5b884d8f495103

Observation b0b2a8ff-6e24-4b6d-8e96-b6f5e0e67d5f · outbound

This paper cites Lawam: Latent world action models for efficient dynamics-aware robot policies.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models Lawam: Latent world action models for efficient dynamics-aware robot policies

Reference 9

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source=arxiv_source observed=2026-08-07T21:29:43.533071Z digest=sha256:e18de52240d88bc46e1b80bdd30aefa5d677ca2d3f45c8b2617e25f379092916

Observation 2ffacfd1-4482-4b49-a70a-5cd92bd93e35 · outbound

This paper cites RoboTwin 2.0: A scalable data generator and benchmark with strong domain randomization for robust bimanual robotic manipulation.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models RoboTwin 2.0: A scalable data generator and benchmark with strong domain randomization for robust bimanual robotic manipulation

Reference 10

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raw_fallback, observed 2026-08-07T21:29:45.346238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T21:29:43.537993Z digest=sha256:049332d5a68603afe0f20e9226de750c7606bfdfd88e707c54950d6782b5f722

Observation 3bd500e6-51b0-47d0-882e-422d7ff40af0 · outbound

This paper cites Learning Universal Policies via Text-Guided Video Generation.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models Learning Universal Policies via Text-Guided Video Generation

Reference 11

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source=arxiv_source observed=2026-08-07T21:29:43.542728Z digest=sha256:a59931d13ab52ea15c1ffb03f583c0457a2e6199d879e4aa5bd0cfad03f3003b

Observation 18d058b1-3269-4626-80b5-e1637d3d6e1c · outbound

This paper cites LIBERO-Plus : A progressive robustness benchmark for visual-language-action models.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models LIBERO-Plus : A progressive robustness benchmark for visual-language-action models

Reference 12

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raw_fallback, observed 2026-08-07T21:29:45.330846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T21:29:43.548420Z digest=sha256:f83af4a7c77e819a64b973e4d56f34b40020ae6859b151477bca9816af3a210f

Observation e4a5a6e6-f6af-4c24-b4b6-ae5c5c1629d6 · outbound

This paper cites Prediction with action: Visual policy learning via joint denoising process.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models Prediction with action: Visual policy learning via joint denoising process

Reference 13

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doi, observed 2026-08-07T21:29:44.311700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T21:29:43.553241Z digest=sha256:f3ee8767f508247f8debb4ea30ab3a0a0409e791a50277debbdf6944c7e290bc

Observation 69075a04-26df-48e9-8831-73f59973086e · outbound

This paper cites World Models.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models World Models

Reference 14

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source=arxiv_source observed=2026-08-07T21:29:43.558466Z digest=sha256:5245305d5843528eb23fcd5f89f0d0d0e36e99f41483e1ef9f739f76d593c35b

Observation aaf21366-9a54-4ea4-b04b-31ab8df9d148 · outbound

This paper cites NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks

Reference 15

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source=arxiv_source observed=2026-08-07T21:29:43.564353Z digest=sha256:c5261600f16be2d15ce63bcb54c22bc4532db590b70fc9a1f83beb6672fe7c26

Observation e979e249-19c7-4436-af7f-26224d5d80cd · outbound

This paper cites OpenVLA: An Open-Source Vision-Language-Action Model.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models OpenVLA: An Open-Source Vision-Language-Action Model

Reference 16

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source=arxiv_source observed=2026-08-07T21:29:43.569686Z digest=sha256:6631863d4aa85b14d9aabd13600274b599386c37c16b4efcec4ed75472966e36

Observation 2ddaa7be-70ad-45ff-b1e0-ef7034bb8605 · outbound

This paper cites Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success

Reference 17

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source=arxiv_source observed=2026-08-07T21:29:43.574878Z digest=sha256:9616298e2df78287a1bd31da9604313fd5b23ca4f0412c7914030d40a17e6b6e

Observation 6c9121e4-53e6-4af8-a727-a5f60cdb86b7 · outbound

This paper cites Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning

Reference 18

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source=arxiv_source observed=2026-08-07T21:29:43.580212Z digest=sha256:cdb104cd5c43cadc284db93a9aa0318581427c5a8cff9e89b604d361e7a17fe9

Observation 5b3f109b-375b-4acf-9ce0-380e1c369dd7 · outbound

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

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models A path towards autonomous machine intelligence version 0.9

Reference 19

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verified fuzzy
raw_fallback, observed 2026-08-07T21:29:45.315551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T21:29:43.585251Z digest=sha256:c83333bf01baca3223e8ee5fd4b9abbfcaf3579e5f049896dc2f800ebfd8217a

Observation b2bff3e5-3f04-4815-9ee6-cba247f546c2 · outbound

This paper cites Spatial forcing: Implicit spatial representation alignment for vision-language-action model.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models Spatial forcing: Implicit spatial representation alignment for vision-language-action model

Reference 20

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source=arxiv_source observed=2026-08-07T21:29:43.590006Z digest=sha256:7e3740b5659ff1ba8533ac6bc9e98a21ec8427d3a55e5d5d37c6768c729ac26e

Observation 8d18da9d-e4db-420e-a168-b1ae82dfbf52 · outbound

This paper cites Causal World Modeling for Robot Control.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models Causal World Modeling for Robot Control

Reference 21

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source=arxiv_source observed=2026-08-07T21:29:43.594969Z digest=sha256:d525ca611f03a0fcd601c0636297f1c6c5a98f44ddd4e40d47d4993f211faf66

Observation 5d463649-3d59-4d5c-a815-917a523b357b · outbound

This paper cites Genie envisioner: A unified world foundation platform for robotic manipulation.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models Genie envisioner: A unified world foundation platform for robotic manipulation

Reference 22

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verified fuzzy
raw_fallback, observed 2026-08-07T21:29:45.299234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T21:29:43.600028Z digest=sha256:1ea7d934e506c6f6e7b0dabbab264ad44ccb6724baae32a51c6a9e83010ed88c

Observation 484e99f4-7de3-420f-b40b-9a269da36c44 · outbound

This paper cites Chen, Zhenyu Li, Yang Zhao, Sida Peng, Hengkai Guo, Xiaowei Zhou, Guang Shi, Jiashi Feng, and Bingyi Kang.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models Chen, Zhenyu Li, Yang Zhao, Sida Peng, Hengkai Guo, Xiaowei Zhou, Guang Shi, Jiashi Feng, and Bingyi Kang

Reference 23

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verified fuzzy
raw_fallback, observed 2026-08-07T21:29:45.281049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T21:29:43.604606Z digest=sha256:272428078f102178eaa3d0119ac67602252ccfc2ec6b35d2ebb0ac476e656e15

Observation 35228cce-0c13-41cd-b502-66962f22d694 · outbound

This paper cites Evo-0: Vision-language-action model with implicit spatial understanding.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models Evo-0: Vision-language-action model with implicit spatial understanding

Reference 24

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source=arxiv_source observed=2026-08-07T21:29:43.610111Z digest=sha256:045e2c2b59fcd60f1dddd7d1b690668b9225a5d909a212ecf7a3a4dcb179e4f5

Observation c5f95cb0-6da5-40e4-8c07-356643e01408 · outbound

This paper cites LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning

Reference 25

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source=arxiv_source observed=2026-08-07T21:29:43.616014Z digest=sha256:526db7cd710aebb0c0b9301b9d56f6ea09c2779f40ec3aeea0622be09fc312a4

Observation bba2533d-7393-4b83-bee3-32a207305913 · outbound

This paper cites LDA-1B : Scaling latent dynamics action model via universal embodied data ingestion.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models LDA-1B : Scaling latent dynamics action model via universal embodied data ingestion

Reference 26

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verified fuzzy
raw_fallback, observed 2026-08-07T21:29:45.265287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T21:29:43.621120Z digest=sha256:ffc4b231dbbdde09f044e8fbf2dec319d7c4653252d5e74c8cc16d1c33456236

Observation 6e6c2f75-f6ba-4715-a0b7-6ec689ab69e9 · outbound

This paper cites Reconstruction or Semantics? What Makes a Latent Space Useful for Robotic World Models.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models Reconstruction or Semantics? What Makes a Latent Space Useful for Robotic World Models

Reference 27

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source=arxiv_source observed=2026-08-07T21:29:43.626179Z digest=sha256:3309640ed7224d1a537cd940fe1a71b4c6dee81a29e4ea8d4ca104c3675b77a4

Observation 8a6b5351-b1b3-4634-a68d-ee386ca31903 · outbound

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

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models Cosmos World Foundation Model Platform for Physical AI

Reference 28

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source=arxiv_source observed=2026-08-07T21:29:43.632134Z digest=sha256:0b683944212bee933ea457bd18bf3ec4bb1d7de8bb2b6bce827011892458c455

Observation a7c5c887-27b3-42af-8531-54ab99f2afb5 · outbound

This paper cites mimic-video : Video-action models for generalizable robot control beyond VLA s.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models mimic-video : Video-action models for generalizable robot control beyond VLA s

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-07T21:29:45.250028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T21:29:43.637880Z digest=sha256:0f8046be79b8e8f185c1e5b05668a62250e99f85a71680731ad516bdd0179c6e

Observation 45e213d6-5519-44f7-b774-b0ffba8cc5b8 · outbound

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

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models FAST: Efficient Action Tokenization for Vision-Language-Action Models

Reference 30

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source=arxiv_source observed=2026-08-07T21:29:43.642917Z digest=sha256:51c7735bd3d3068cfec8c7531e97eb3207ae336a4541e9cacf282cdf8671a917

Observation 1d5ba225-53c0-4b10-acb7-8643c2b614ff · outbound

This paper cites World Action Models: A Survey.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models World Action Models: A Survey

Reference 31

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verified exact
local_arxiv, observed 2026-08-07T21:29:44.227018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T21:29:43.648773Z digest=sha256:9a32212bc6a90956d3ea7862328c209697e3677a9123278044ec27c182ed60fc

Observation f9fc0cc1-0cf8-45a8-82fe-d6c7cfa93588 · outbound

This paper cites DINOv3.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models DINOv3

Reference 32

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source=arxiv_source observed=2026-08-07T21:29:43.653607Z digest=sha256:9d7cae5eba83460a958dabeacfa1bddb17050d8f1da5843ba8e5c7a270e2b233

Observation 4c9ccf9c-871d-480e-8855-c032bec94dbf · outbound

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

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models Wan: Open and Advanced Large-Scale Video Generative Models

Reference 33

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source=arxiv_source observed=2026-08-07T21:29:43.659139Z digest=sha256:f55f6ed1777229f72b8148247d6c9e9898b15174889921cbe3d447dc73763271

Observation 70242a4d-714c-4206-8fc4-75da2e9c50d3 · outbound

This paper cites RepWAM: World Action Modeling with Representation Visual-Action Tokenizers.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models RepWAM: World Action Modeling with Representation Visual-Action Tokenizers

Reference 34

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no resolver link, observed 2026-08-07T21:29:43.664306Z

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source=arxiv_source observed=2026-08-07T21:29:43.664306Z digest=sha256:9a1877598bbb88c047fc7e4a8a2a72b336c018c92c96bcd6267639a534baa4fd

Observation c0f16bc5-cec3-45d6-a1d0-3ae8a8e9c9dc · outbound

This paper cites Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation

Reference 35

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Observation bc730da9-3530-4efa-a002-acdfeaf777a3 · outbound

This paper cites FutureVLA : Joint visuomotor prediction for vision-language-action model.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models FutureVLA : Joint visuomotor prediction for vision-language-action model

Reference 36

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Observation c9e95fc0-2622-4818-b152-dc9f2caa9c45 · outbound

This paper cites StarVLA-$\alpha$: Reducing Complexity in Vision-Language-Action Systems.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models StarVLA-$\alpha$: Reducing Complexity in Vision-Language-Action Systems

Reference 37

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no resolver link, observed 2026-08-07T21:29:43.679501Z

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Observation d783d34e-8678-412a-9584-b9dd2fee7951 · outbound

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

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models World Action Models are Zero-shot Policies

Reference 38

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source=arxiv_source observed=2026-08-07T21:29:43.684315Z digest=sha256:338d5d7db8d3e8ba9b06b01020ff8876af25f669862d431f5410fb5e21c93206

Observation f0731e72-1a6e-4ac6-beac-77035d03771e · outbound

This paper cites Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Reference 39

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no resolver link, observed 2026-08-07T21:29:43.689632Z

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source=arxiv_source observed=2026-08-07T21:29:43.689632Z digest=sha256:1406928270ece01c23ef1fe7a5049e1c722d2786c157dfbbe489fe6873e0eb2c

Observation 0eb8c335-7f3d-4abd-ad28-b1d3514f7ac1 · outbound

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

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models Fast-WAM: Do World Action Models Need Test-time Future Imagination?

Reference 40

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no resolver link, observed 2026-08-07T21:29:43.694843Z

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source=arxiv_source observed=2026-08-07T21:29:43.694843Z digest=sha256:691061623b5305cfd9ba13ace13c41c2db4d109eb154a146a7d102c2d95ccbd2

Observation 972d75d3-a521-4eec-ad7b-3cbac8e87c5d · outbound

This paper cites Do World Action Models Generalize Better than VLAs? A Robustness Study.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models Do World Action Models Generalize Better than VLAs? A Robustness Study

Reference 41

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no resolver link, observed 2026-08-07T21:29:43.700343Z

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source=arxiv_source observed=2026-08-07T21:29:43.700343Z digest=sha256:82337798d3eb2fed22375564a743ad589278bbadf6fa59cd7e673c28bbf767c8

Observation 1aa7da12-0faa-48c7-9e0c-df2d820ee8ab · outbound

This paper cites FRAPPE : Infusing world modeling into generalist policies via multiple future representation alignment.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models FRAPPE : Infusing world modeling into generalist policies via multiple future representation alignment

Reference 42

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no resolver link, observed 2026-08-07T21:29:43.706111Z

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source=arxiv_source observed=2026-08-07T21:29:43.706111Z digest=sha256:2b93d185926d99476560d55f295aea2cd9183ba2d3c8ce1de0659d20b55a917c

Observation 950b22d0-2f92-41c5-be7a-dd5a3ef26c9c · outbound

This paper cites FLARE : Robot learning with implicit world modeling.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models FLARE : Robot learning with implicit world modeling

Reference 43

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verified fuzzy
raw_fallback, observed 2026-08-07T21:29:45.234084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T21:29:43.711111Z digest=sha256:c438a9aa0b03d3b58985db1a3bf570f039216f80029e12379440be8ddfff66fc

Observation 184a69f5-7483-4c00-8570-8027a257c82f · outbound

This paper cites DINO-WM : World models on pre-trained visual features enable zero-shot planning.

Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models DINO-WM : World models on pre-trained visual features enable zero-shot planning

Reference 44

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verified fuzzy
raw_fallback, observed 2026-08-07T21:29:45.217189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T21:29:43.716173Z digest=sha256:c975e7e8f46347a78fc9b628c26c0d0e48e62a0f47a530b03aa4eca92360eb7d

Pith citing papers

No inbound Pith citation observations are available.