Paper Citation Record · LEDGER

Learning to Act from Actionless Videos through Dense Correspondences

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 33 inbound Pith citation observations for arXiv:2310.08576.

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

pith.paper-citation-record.v1
2310.08576 v1

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 33 of 33 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T22:10:13.371215Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T04:09:35.261339Z

Reference resolution

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External citation measurements

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Outbound references

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Pith citing papers

Observation 3aeba27b-6d61-45e8-823f-61b7b3fc3d7e · inbound

Any-point Trajectory Modeling for Policy Learning cites this paper.

Any-point Trajectory Modeling for Policy Learning Learning to Act from Actionless Videos through Dense Correspondences

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:32:50.976352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8c4d3cbd-e360-410f-8c90-b25e27309042 · inbound

Agent AI: Surveying the Horizons of Multimodal Interaction cites this paper.

Agent AI: Surveying the Horizons of Multimodal Interaction Learning to Act from Actionless Videos through Dense Correspondences

Reference 130

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T14:25:59.387306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bb09e5b1-9a2a-4aa0-a4e6-37be6eeaa1ea · inbound

DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning cites this paper.

DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning Learning to Act from Actionless Videos through Dense Correspondences

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-17T16:06:09.770224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b85776dd-69b6-4ec7-b660-b452a7b2d027 · inbound

GEVRM: Goal-Expressive Video Generation Model For Robust Visual Manipulation cites this paper.

GEVRM: Goal-Expressive Video Generation Model For Robust Visual Manipulation Learning to Act from Actionless Videos through Dense Correspondences

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T22:10:13.371215Z

Source-reported events for the cited work

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Observation 24052ae1-99a5-4399-beeb-237e10d3fe62 · inbound

Self-Consistent Model-based Adaptation for Visual Reinforcement Learning cites this paper.

Self-Consistent Model-based Adaptation for Visual Reinforcement Learning Learning to Act from Actionless Videos through Dense Correspondences

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-07T20:08:49.465586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d561e5f9-dc15-4e00-ab53-803e0186867c · inbound

Unified Video Action Model cites this paper.

Unified Video Action Model Learning to Act from Actionless Videos through Dense Correspondences

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:50:29.752426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 038e05b4-7263-4fee-b217-7f22183a200c · inbound

3DFlowAction: Learning Cross-Embodiment Manipulation from 3D Flow World Model cites this paper.

3DFlowAction: Learning Cross-Embodiment Manipulation from 3D Flow World Model Learning to Act from Actionless Videos through Dense Correspondences

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T06:03:33.248255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8b07aa3-b76d-49fb-8b1d-8c119778b33f · inbound

AMPLIFY: Actionless Motion Priors for Robot Learning from Videos cites this paper.

AMPLIFY: Actionless Motion Priors for Robot Learning from Videos Learning to Act from Actionless Videos through Dense Correspondences

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T00:25:28.228345Z

Source-reported events for the cited work

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Observation b68a66b7-ebec-4d1c-b25d-4d87d2dafbc4 · inbound

RoboEnvision: A Long-Horizon Video Generation Model for Multi-Task Robot Manipulation cites this paper.

RoboEnvision: A Long-Horizon Video Generation Model for Multi-Task Robot Manipulation Learning to Act from Actionless Videos through Dense Correspondences

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:13.262031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a768ac1-4892-4d08-9cdf-6ec5cdfac315 · inbound

Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations cites this paper.

Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations Learning to Act from Actionless Videos through Dense Correspondences

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-19T06:37:07.533841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f1065572-00dc-42ae-aabf-adf95f136f96 · inbound

Precise Action-to-Video Generation Through Visual Action Prompts cites this paper.

Precise Action-to-Video Generation Through Visual Action Prompts Learning to Act from Actionless Videos through Dense Correspondences

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-05T19:15:31.867862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5de35d0b-5b26-46dd-a4a7-6f8f71c7f439 · inbound

Generative Visual Foresight Meets Task-Agnostic Pose Estimation in Robotic Table-Top Manipulation cites this paper.

Generative Visual Foresight Meets Task-Agnostic Pose Estimation in Robotic Table-Top Manipulation Learning to Act from Actionless Videos through Dense Correspondences

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T13:46:41.955554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be557eca-5c0d-4c5a-90cd-45f52f3f0f7c · inbound

IGen: Scalable Data Generation for Robot Learning from Open-World Images cites this paper.

IGen: Scalable Data Generation for Robot Learning from Open-World Images Learning to Act from Actionless Videos through Dense Correspondences

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-17T02:58:54.982338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f627f937-feab-4a91-a22a-6683e1f63285 · inbound

MIND-V: Hierarchical World Model for Long-Horizon Robotic Manipulation with RL-based Physical Alignment cites this paper.

MIND-V: Hierarchical World Model for Long-Horizon Robotic Manipulation with RL-based Physical Alignment Learning to Act from Actionless Videos through Dense Correspondences

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T18:09:49.386975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eaa3dc8e-31ce-4fb1-9ffb-ea2304b89dfd · inbound

Large Video Planner Enables Generalizable Robot Control cites this paper.

Large Video Planner Enables Generalizable Robot Control Learning to Act from Actionless Videos through Dense Correspondences

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-16T21:28:33.873937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fff2440d-9bb9-4e7c-b318-be2b2652f23c · inbound

On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning cites this paper.

On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Learning to Act from Actionless Videos through Dense Correspondences

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:36.377047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a2ec42a-bc32-4d94-940e-41693131aded · inbound

Multi-View Video Diffusion Policy: A 3D Spatio-Temporal-Aware Video Action Model cites this paper.

Multi-View Video Diffusion Policy: A 3D Spatio-Temporal-Aware Video Action Model Learning to Act from Actionless Videos through Dense Correspondences

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:58:08.919862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9c0a6822-3700-4022-8b99-9a83c66f8544 · inbound

From Video to Control: A Survey of Learning Manipulation Interfaces from Temporal Visual Data cites this paper.

From Video to Control: A Survey of Learning Manipulation Interfaces from Temporal Visual Data Learning to Act from Actionless Videos through Dense Correspondences

Reference 50

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T17:03:01.081202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8a90e2a2-5dcc-41ab-b71c-37bfb2ec9c51 · inbound

Action Images: End-to-End Policy Learning via Multiview Video Generation cites this paper.

Action Images: End-to-End Policy Learning via Multiview Video Generation Learning to Act from Actionless Videos through Dense Correspondences

Reference 30

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T23:50:53.642876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fb396572-8724-4147-8c5f-50bbdb92c5a6 · inbound

ComSim: Building Scalable Real-World Robot Data Generation via Compositional Simulation cites this paper.

ComSim: Building Scalable Real-World Robot Data Generation via Compositional Simulation Learning to Act from Actionless Videos through Dense Correspondences

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T09:11:01.576249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ecd53afa-9db9-4640-934c-4e50eb86db95 · inbound

VADF: Vision-Adaptive Diffusion Policy Framework for Efficient Robotic Manipulation cites this paper.

VADF: Vision-Adaptive Diffusion Policy Framework for Efficient Robotic Manipulation Learning to Act from Actionless Videos through Dense Correspondences

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T08:32:52.223880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bfa1d90c-26cf-42e0-ade1-c92096b052c7 · inbound

Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling cites this paper.

Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling Learning to Act from Actionless Videos through Dense Correspondences

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:16:28.801872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9bf3b0d6-1ab6-4762-80b9-821ca37e7586 · inbound

Bridging the Embodiment Gap: Disentangled Cross-Embodiment Video Editing cites this paper.

Bridging the Embodiment Gap: Disentangled Cross-Embodiment Video Editing Learning to Act from Actionless Videos through Dense Correspondences

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-12T11:01:31.562222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 19837ab5-291c-43bd-9549-e7b647ed0adf · inbound

ForgeVLA: Federated Vision-Language-Action Learning without Language Annotations cites this paper.

ForgeVLA: Federated Vision-Language-Action Learning without Language Annotations Learning to Act from Actionless Videos through Dense Correspondences

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:20:55.452734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 610ccdbc-e91b-4615-b336-d6e68066a720 · inbound

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

World Action Models: The Next Frontier in Embodied AI Learning to Act from Actionless Videos through Dense Correspondences

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:02:17.821723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 14845897-427a-4a84-836f-04c454d00f3a · inbound

TrackCraft3R: Repurposing Video Diffusion Transformers for Dense 3D Tracking cites this paper.

TrackCraft3R: Repurposing Video Diffusion Transformers for Dense 3D Tracking Learning to Act from Actionless Videos through Dense Correspondences

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:29:28.946912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 27f43554-28ff-4d10-9a90-9533a7886926 · inbound

World Action Models: A Survey cites this paper.

World Action Models: A Survey Learning to Act from Actionless Videos through Dense Correspondences

Reference 81

Resolution: verified exact; arxiv_id, observed 2026-07-04T04:09:35.263071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e39ff17c-3763-4afc-996a-7e299882002b · inbound

From World Models to World Action Models: A Concise Tutorial for Robotics cites this paper.

From World Models to World Action Models: A Concise Tutorial for Robotics Learning to Act from Actionless Videos through Dense Correspondences

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T11:26:53.592737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2575fae4-6302-4315-b8a9-346cb712e54e · inbound

From World Models to World Action Models: A Concise Tutorial for Robotics cites this paper.

From World Models to World Action Models: A Concise Tutorial for Robotics Learning to Act from Actionless Videos through Dense Correspondences

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T20:38:55.067037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b9c137da-9f8d-486f-8ef3-1faab3442251 · inbound

From World Models to World Action Models: A Concise Tutorial for Robotics cites this paper.

From World Models to World Action Models: A Concise Tutorial for Robotics Learning to Act from Actionless Videos through Dense Correspondences

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-12T09:15:00.615740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40bfbb25-6c19-4d74-a68b-b17e16312023 · inbound

From World Models to World Action Models: A Concise Tutorial for Robotics cites this paper.

From World Models to World Action Models: A Concise Tutorial for Robotics Learning to Act from Actionless Videos through Dense Correspondences

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T09:18:01.832054Z

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Observation 1c28cbb1-fc72-4ba6-8969-a804d8b5f6f5 · inbound

Structured 4D Latent Predictive Model for Robot Planning cites this paper.

Structured 4D Latent Predictive Model for Robot Planning Learning to Act from Actionless Videos through Dense Correspondences

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T11:06:52.499232Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d97b8c95-b61e-4c8d-854f-b13625b305e5 · inbound

Masked Visual Actions for Unified World Modeling cites this paper.

Masked Visual Actions for Unified World Modeling Learning to Act from Actionless Videos through Dense Correspondences

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-01T12:49:03.044032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.