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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=sha256:21d4e42f7c3ca2ebd528ba218aaab62726dbc0ec2ab264d15a1c8eb2359b7d70

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=sha256:1502c83c991a97ea9bd9440be16f47f7cbab722acc1a7292541c3f0815982719

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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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:d066e86bdbf51db9dc07e99ef542e7edd30b08a655a65250b759443ffa3aa2df

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=sha256:3ad709c95a35891ce0cbdacff8a400e0d0bbe55762cc017f98d0fce95c71e306

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:cca8dbb3efc766f4be73000694bea00ec07eb019fce07ae8583a0e9f7e793f45

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:e671f95b668bb1270842876e638a6a84e29591ffe63b9428c978a45f6813ab07

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=sha256:46d2586790cd73a8babb104147ac71537e23a3fe12058e2b491c0de7b2069947

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:b91c64d3dfc2b8b400de37aa1acda1fc2be8862fbbcbc07435812ef24d2aa846

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:cfe56f3b7f71a435101344ac29812b6cc2f2cda09631ca3fe652e4723fb18c45

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:ba5e29b0cdbea8eef2cd495d7e35d98e4a994759ae7d984b73abb5c2e07b0f24

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:cacbd0d6aceb6ebce5296b8a6878dbd2fe38af92cf7bb8834970a0d88a2e1d0b

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=sha256:0c5763b33dcfa2e96311af1f737095adb72be79f3a889273d0b5f70d310bf70e

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=sha256:50618fa267cb64af0f4a34f79020267bd1272a8ecb7a18b8a6ca77680ab7ec1e

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=sha256:8037da882a8715c9c28170b150951237c86dac89a47e738ee500d2a34f7931ca

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:ad85b67fcff749624f53718c5050802634f2ce79561dc66a77b4030a6d31659e

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:b3c8a9a738e5e1a371ad5c86f3c844d39e4b66a4b97027f3b65e80a53fffd727

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=sha256:788b7307a41d6952d1a07ea2c6671149f021adc9e086f548d1034c54d546630d

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=sha256:75b12012034460f600039d5dccf6eec244045233a060ac4879d78cc5b93119d8

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=sha256:3b405e8a736d39274c143075c346590a50db0f84ae40e47036c20299328a782f

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=sha256:201756728c081c265dfbc98502ee4a98fe3d1e413267e1dc033caa33dd1fa955

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=sha256:828bf16cf696cc6791fda91e88e00c1b29b8d0a3b1db9c3913b27620db09e586

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:d24f76105f1f570a027856dd5887f42256a183630345310f35233cb5e060b37d

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=sha256:0d5cf5ae2b2f253df143861406a2b1f5a1f39814a71f885d847523a43da2338c

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=sha256:6575d727f1376ff173754406a656164dbe53f27c676b2f15cc252060fc052742

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=sha256:617b50fa8c40f4e65e2454b02b8bba8d3da8439acb26ed2203e3d0d4937dd500

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=sha256:115d6b62db22efab9b8be1dc6369e3fe263b90ddbb39246262ff68a45665e522

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:a1ec553bd11f988ef224a258c5b5ab883ea38e66d9f6522a27e33832afa13874

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:da049de2015463d10ebeb5704186c78c209249b1ac06644d6ed4101a0b5f5e57

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:a51fe56a61c0fdf0b95ccdefb6c69bf6c4b044007600e7389e5fced0648107e3

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=sha256:1d034e817f4c9682b81beb8c3a29d1fd36a620559ef25fc5e8569e6ec6a99df9

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:ea4f1095bc3103d81eeb2ac1e96aba938e8fceba3cab127a889b2b47c3a7d26c

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:f3e8638f40b63c8b1cff91828c7f5c01095cedabf31c223b3942e3276781c2aa

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=sha256:2d9de15ec8b9cc5b201daf99ebb04cedc02ad5378346c6b17ceb0c085c62f664

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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source=arxiv_source observed=2026-08-07T21:29:43.664306Z digest=sha256:17ae06cbf224f63e431fa85a6b1020a84b1147b66fdbfcf0a9b440c7968fcf70

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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unresolved
no resolver link, observed 2026-08-07T21:29:43.669451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T21:29:43.669451Z digest=sha256:29db2e0ccf4e287214a7c2ae073d8ccd047f9dba4cb4cbe93d759baf89b43abc

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

Resolution
unresolved
no resolver link, observed 2026-08-07T21:29:43.674610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T21:29:43.674610Z digest=sha256:1f21ce8d7e19ffb32e09d024ed1ca246c680eab3f19756d2bf64542349020f4f

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T21:29:43.679501Z digest=sha256:48211a0ce2f67f166221e641b8586293c06d95be0c5243e0e2dfbe697a5fadc8

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

Resolution
unresolved
no resolver link, observed 2026-08-07T21:29:43.684315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T21:29:43.684315Z digest=sha256:c86ead6188dfec75994be0e4f242c6930de5c0c22cdf1559d10dcd6e8cc501a0

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

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

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T21:29:43.689632Z digest=sha256:237746121cf1b345402a33ced43a43bdc92e8d6311a727e74ac46a8ab8bf2b6a

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T21:29:43.694843Z digest=sha256:86a3ec6333a0996c4ea7dfa302012b044dabc5ec560c01462b03992c45e68442

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T21:29:43.700343Z digest=sha256:ab78678fa6b5f9b66998c3ecfcbf4b02f87095f5dc3456b5997dc4a5cc36bed1

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T21:29:43.706111Z digest=sha256:165978ce684231dfc4f130d24440df98bd2cb9584b944044958ddd4e85555abb

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

Resolution
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=sha256:987efd2e0f6d205494cbac8cbca9643b6cec87c931008be59e75af2745adc032

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

Resolution
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=sha256:9dea18ef93a9d5424948ea73f8313b02b030683ea3d8dc7cf7a0b3f19e941e0e

Pith citing papers

No inbound Pith citation observations are available.