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Paper Citation Record · LEDGER

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation

As of 14 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2608.06374.

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

pith.paper-citation-record.v1
2608.06374 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:08:34.770206Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

32 of 32 outbound references displayed

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  • unresolved29
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  • malformed identifier1
  • metadata mismatch0

External citation measurements

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Outbound references

Observation 1649fcac-ca18-4e40-8f10-c7adf07e6430 · outbound

This paper cites Qwen3-VL Technical Report.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation Qwen3-VL Technical Report

Reference 1

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source=pdf_text observed=2026-08-07T04:08:30.205321Z digest=sha256:f868ef417469f92b31c7254ebf7c2008d8d45b1db9a276f184cad004171fcccb

Observation 2a6b57db-92ad-4772-adb5-67ec67d28af0 · outbound

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

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

Reference 3

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source=pdf_text observed=2026-08-07T04:08:30.467744Z digest=sha256:78a9ab2dbadbed6b7895656fc3c43717dc92f590c74d1ca513abccef762679cc

Observation 7bf3de5f-7d1e-49da-a6a3-0549bdefa25f · outbound

This paper cites π0.5: a vision-language-action model with open-world generalization.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation π0.5: a vision-language-action model with open-world generalization

Reference 4

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source=pdf_text observed=2026-08-07T04:08:30.589081Z digest=sha256:e47993f97af031a3276fe2c4ebeb913cc211d17e6f4a0165970fa5b1c9efae35

Observation 8cd17faf-f616-489c-84c8-76eb8df3f413 · outbound

This paper cites WorldVLA: Towards Autoregressive Action World Model.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation WorldVLA: Towards Autoregressive Action World Model

Reference 6

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source=pdf_text observed=2026-08-07T04:08:30.854406Z digest=sha256:f5943c62824ed7f309a8ab95d3f49859a1510e551b0d15856ddcb4528e384c23

Observation 95c66a4c-f7db-42ef-a2cd-ca379b979706 · outbound

This paper cites Unified diffusion vla: Vision-language-action model via joint discrete denosing diffusion process.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation Unified diffusion vla: Vision-language-action model via joint discrete denosing diffusion process

Reference 7

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T04:08:30.980745Z digest=sha256:0f4d036bae41ed30fec45ddce8a8e450ed06d24e583f1dc53bcac2d8acc64912

Observation d8ec08ed-8be9-40d9-9570-3cefb9d7195a · outbound

This paper cites Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer

Reference 10

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source=pdf_text observed=2026-08-07T04:08:31.446237Z digest=sha256:305fb7db66b88be6238d8a01cbc923d1a7fd91551b8981e14874d515220939a3

Observation ca4eac97-f468-4da1-9944-bd469dff7e5d · outbound

This paper cites EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video

Reference 11

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source=pdf_text observed=2026-08-07T04:08:31.585050Z digest=sha256:350844202ab1d265f6128c4f42322a7438659c974296b7001111c636c0b616cd

Observation 661c8415-e32d-42f7-a21e-379b73e1f64a · outbound

This paper cites X-DiffVLA: X-Embodied Diffusion Action Heads for Vision-Language-Action Models.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation X-DiffVLA: X-Embodied Diffusion Action Heads for Vision-Language-Action Models

Reference 12

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local_arxiv, observed 2026-08-07T04:08:35.494187Z

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source=pdf_text observed=2026-08-07T04:08:31.733430Z digest=sha256:b522495e7e643b3b0c33104091560dadf57778c7688eb2bf67f5335614b4c41c

Observation 6aaeeb94-8408-434b-a01d-86026369bf5d · outbound

This paper cites Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation

Reference 13

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source=pdf_text observed=2026-08-07T04:08:31.904125Z digest=sha256:2f3db237032fa65229aa783a2811cfc93ff64a9ce9e0ac707d915abfdf03c817

Observation e890f8c1-332d-4d7d-8640-910bbff428c1 · outbound

This paper cites Flow Matching for Generative Modeling.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation Flow Matching for Generative Modeling

Reference 14

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source=pdf_text observed=2026-08-07T04:08:32.026849Z digest=sha256:7f5f86f6f470232d6294d2437364ee4508eb5990565de37919440b53e3ddfc86

Observation 0627c0d9-437d-46fd-a078-75aa4482b4e3 · outbound

This paper cites RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation

Reference 15

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source=pdf_text observed=2026-08-07T04:08:32.170479Z digest=sha256:71535f68274c400af52d3a1c9f607f92f9769ebc6a177f4dc14cddbbf2bd9d66

Observation 9f04f079-c9f0-46b7-aebf-1a9248facb2f · outbound

This paper cites Rdt2: Exploring the scaling limit of umi data towards zero-shot cross- embodiment generalization.arXiv preprint arXiv:2602.03310,.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation Rdt2: Exploring the scaling limit of umi data towards zero-shot cross- embodiment generalization.arXiv preprint arXiv:2602.03310,

Reference 16

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source=pdf_text observed=2026-08-07T04:08:32.319686Z digest=sha256:82418fee67718e9c487d4c34dc61447926fe55c7e286b5a32fdd757b2a1a42e0

Observation 36d4981d-d08b-4f6a-85bd-92b8aed13ce4 · outbound

This paper cites Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos

Reference 17

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source=pdf_text observed=2026-08-07T04:08:32.515534Z digest=sha256:f3343eb1a360fc1cfbe5520c3ec4eafede0edef66418c1e50847ec0f930c4110

Observation e3727072-d7ea-42fa-950a-547676b04672 · outbound

This paper cites F1: A Vision-Language-Action Model Bridging Understanding and Generation to Actions.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation F1: A Vision-Language-Action Model Bridging Understanding and Generation to Actions

Reference 18

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source=pdf_text observed=2026-08-07T04:08:32.698704Z digest=sha256:f32517588556bdd3b668316e9fdf76d98e005574e7c0130ba0685ed5492480c1

Observation 35567722-aa66-41f9-845d-a8cff86b20ef · outbound

This paper cites LDA-1B: Scaling Latent Dynamics Action Model via Universal Embodied Data Ingestion.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation LDA-1B: Scaling Latent Dynamics Action Model via Universal Embodied Data Ingestion

Reference 19

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source=pdf_text observed=2026-08-07T04:08:32.864862Z digest=sha256:9bd82d8c51ed1b2d780024d56212fc196fc8ee8061e35a6d7f951779222f112d

Observation b48ceb03-990a-46ad-be06-d8aa7137b986 · outbound

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

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation Dit4dit: Jointly modeling video dynamics and actions for generalizable robot control.arXiv preprint arXiv:2603.10448,

Reference 20

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source=pdf_text observed=2026-08-07T04:08:32.992667Z digest=sha256:fc94ad1f6a699b4558ae2db0f1722c90c2a1a68670ca962858c1352981c24cb5

Observation 1a4e4caf-28b9-4714-937d-997d46cbf171 · outbound

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

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation Octo: An Open-Source Generalist Robot Policy

Reference 21

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source=pdf_text observed=2026-08-07T04:08:33.125804Z digest=sha256:bcf070381b7b27ece9b4c591ef05e62d75767daa3381d7eac5ecd34874ee2d1d

Observation b565bdd3-d70a-4935-bbdf-87154a678f5e · outbound

This paper cites Open X-Embodiment: Robotic Learning Datasets and RT-X Models.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Reference 22

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source=pdf_text observed=2026-08-07T04:08:33.290245Z digest=sha256:78e1655cd6ccfcb9b9ed0cab2077d471cdfdbf0ca1d8e1bd27416c820d43cf39

Observation 0f4dcdf4-c9a5-4a80-97d0-ad9853dd29a7 · outbound

This paper cites Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments

Reference 23

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source=pdf_text observed=2026-08-07T04:08:33.495200Z digest=sha256:d296f05a6ad71947af9643d717e4dc1898e002da954f4c95a59d7e64eabdfc8b

Observation df557ac1-f5ed-4d1c-9085-6a0d1b74d5b8 · outbound

This paper cites SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers

Reference 24

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source=pdf_text observed=2026-08-07T04:08:33.762140Z digest=sha256:c22541da9446184871d54802c2bb67f87535985e4bf8147d26f60ed9e6315477

Observation 08c26dfa-6788-40a1-a319-f17e8b986f04 · outbound

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

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation S-VAM: Shortcut video-action model by self-distilling geometric and semantic foresight.arXiv preprint arXiv:2603.16195,

Reference 25

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source=pdf_text observed=2026-08-07T04:08:33.872178Z digest=sha256:d18277757c5fd0459eef13bf8270dab9dc1c25a439663643fa83084e82dfebd7

Observation 8497af07-401d-4b51-837e-68ca3ab74b7f · outbound

This paper cites EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos

Reference 26

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source=pdf_text observed=2026-08-07T04:08:33.990715Z digest=sha256:fb6e6b20648c5d60ed76d4f9a11c91025cfe485e0148f06fad1b8c24b9dc4527

Observation 4f32dc7b-762f-477d-8103-39e7c570d5d1 · outbound

This paper cites ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning

Reference 27

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source=pdf_text observed=2026-08-07T04:08:34.107020Z digest=sha256:751e7fd349017b4752c1335ae65a938890241764cf072f93b68df54379faee4d

Observation e7098613-4f50-45e8-b1c5-36b68c2c9667 · outbound

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

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation Fast-WAM: Do World Action Models Need Test-time Future Imagination?

Reference 28

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source=pdf_text observed=2026-08-07T04:08:34.194886Z digest=sha256:67a04e863e27493dc1893dbaaa16ab2ecbdbc5fd4e6c64ae0b9c9faaa438e754

Observation f19e7637-c24c-4a27-a3b8-a8b371bd3d28 · outbound

This paper cites X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model

Reference 29

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source=pdf_text observed=2026-08-07T04:08:34.328037Z digest=sha256:0058477d2df2299114f905224b3395727917b0d3276a7c1b8604e04c0bcac6d8

Observation 9de9e644-1520-4f75-b9a0-4f49416a8123 · outbound

This paper cites Dualcot-vla: Visual-linguistic chain of thought via parallel reasoning for vision-language-action models.arXiv preprint arXiv:2603.22280,.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation Dualcot-vla: Visual-linguistic chain of thought via parallel reasoning for vision-language-action models.arXiv preprint arXiv:2603.22280,

Reference 30

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source=pdf_text observed=2026-08-07T04:08:34.454382Z digest=sha256:142e981bce7f16e4cba198ba9c5f4bd00ab148e8e970c00c25fe221d1cac1872

Observation dff9f64b-0ab7-4687-b3bf-6ca0ce99c360 · outbound

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

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets

Reference 31

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source=pdf_text observed=2026-08-07T04:08:34.605130Z digest=sha256:aa4a69ecc16bc54ed02a5659e28ba01f67041eefb26434e8fab2806a9cbce76c

Observation 4b1325a6-460c-460c-ae7f-6ddbc9686921 · outbound

This paper cites Pour water from the cup into the bowl.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation Pour water from the cup into the bowl

Reference 32

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verified fuzzy
raw_fallback, observed 2026-08-07T04:08:35.818773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T04:08:34.770206Z digest=sha256:4e600ad8ba7df6ede21660cc44f3a2d9bd46234fd899586eee64aedcb244b30c

Observation 8820e162-4e63-4253-a374-b4892f8b70f2 · outbound

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

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation UniVLA: Learning to Act Anywhere with Task-centric Latent Actions

Reference 2023

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source=pdf_text observed=2026-08-07T04:08:30.741535Z digest=sha256:6de7eb2b259e5fa93093243aa91ef3c879392a93463ae1176837b32dc5178088

Observation 007ffccb-8d24-4cf9-b888-6ada2a48fe4c · outbound

This paper cites HiMoE-VLA: Hierarchical Mixture-of-Experts for Generalist Vision-Language-Action Policies.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation HiMoE-VLA: Hierarchical Mixture-of-Experts for Generalist Vision-Language-Action Policies

Reference 2024

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source=pdf_text observed=2026-08-07T04:08:31.338877Z digest=sha256:a6481fc92ec410095cbe7f8a632409ce9cf71ee10efef4eb042c2c19cdee7545

Observation d42209f7-9bd3-4862-89a5-bf9b552a7448 · outbound

This paper cites Motus: A Unified Latent Action World Model.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation Motus: A Unified Latent Action World Model

Reference 2025

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source=pdf_text observed=2026-08-07T04:08:30.343015Z digest=sha256:6a537d36ba63b1c023757ba2800997be81e0a0babfbb5fb0b317000d9b2806ac

Observation 10140715-8be0-4680-87c4-b1fcde1c48f8 · outbound

This paper cites RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation

Reference 2026

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source=pdf_text observed=2026-08-07T04:08:31.186529Z digest=sha256:05eb2c970ace74c2ffae60dc029334d12a4f27f4c9055fc1c8ba5489e87f29e5

Pith citing papers

No inbound Pith citation observations are available.