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Paper Citation Record · LEDGER

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models

As of 14 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2607.04265.

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

pith.paper-citation-record.v1
2607.04265 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T20:32:22.412216Z

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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T03:16:53.099063Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

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  • unresolved31
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  • malformed identifier0
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External citation measurements

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Outbound references

Observation 1899715e-cec5-4a35-9f81-d73a61e7bb05 · outbound

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

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models World Action Models: The Next Frontier in Embodied AI

Reference 1

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source=pdf_text observed=2026-07-11T20:32:22.412216Z digest=sha256:fd13cdf2b69e08d138960d999e9682496ec6622c58fa4aba954380e97b52630f

Observation 81d42cae-d0f2-4494-ad45-182d1c3b2ffa · outbound

This paper cites World Model for Robot Learning: A Comprehensive Survey.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models World Model for Robot Learning: A Comprehensive Survey

Reference 2

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Observation 6c31730c-8af4-47af-ae35-aa0d4e4f1800 · outbound

This paper cites an unresolved cited work.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Unresolved cited work

Reference 3

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source=pdf_text observed=2026-07-11T20:32:22.412216Z digest=sha256:5e4755a292d36aa8d8a5a022f797d4280a964dd17d4e92d75d873e1d5f6765a0

Observation d58553d9-9a38-4c8e-a8fa-ed17c9063c44 · outbound

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

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning

Reference 4

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Observation 4f738922-05ae-4de2-8c4a-f7bb8580e10b · outbound

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

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Motus: A Unified Latent Action World Model

Reference 5

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source=pdf_text observed=2026-07-11T20:32:22.412216Z digest=sha256:d7ee6a932462f9e6f24aa41e710d56b372241667a491e43e23f57e3c616adefb

Observation e9c74d38-0384-4f3e-81cb-9d4407d7b549 · outbound

This paper cites Luo et al.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Luo et al

Reference 6

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Observation b158d5b2-c619-420a-b93f-0b8ba880d0a7 · outbound

This paper cites an unresolved cited work.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Unresolved cited work

Reference 7

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source=pdf_text observed=2026-07-11T20:32:22.412216Z digest=sha256:064ba36837b2b8631205804b6f43e5d6a09d7b342bb7e7fa47b7d1d0155b5449

Observation ce9bad05-4d09-4e77-8520-05ba9bc6ce06 · outbound

This paper cites Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning

Reference 8

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source=pdf_text observed=2026-07-11T20:32:22.412216Z digest=sha256:b7731a12030bd4c053b966dc9b9c65cf420d3d083f85fe20a6afe2f0df3e2bf1

Observation f224192d-bdef-4397-b03f-4d26efaa45dd · outbound

This paper cites an unresolved cited work.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Unresolved cited work

Reference 9

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source=pdf_text observed=2026-07-11T20:32:22.412216Z digest=sha256:a98d99cbdf89c7f60ef7c9fda143c304a271d376a9dca6c5f34b15b1c955aa2a

Observation fc806294-a3d2-4ef1-83aa-3414aeecc8fb · outbound

This paper cites RL Token: Bootstrapping Online RL with Vision-Language-Action Models.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models RL Token: Bootstrapping Online RL with Vision-Language-Action Models

Reference 10

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Observation 3479754b-4242-43bd-9761-7a426a7f8741 · outbound

This paper cites an unresolved cited work.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Unresolved cited work

Reference 11

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source=pdf_text observed=2026-07-11T20:32:22.412216Z digest=sha256:24352aa970a1904fe986b7cc5a336197addf97c59b6f8e9df35ccc17d2cf824d

Observation 4fab7b81-c372-41c5-bab5-ab5298d2d764 · outbound

This paper cites VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning

Reference 12

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Observation 51dfefa8-1fc1-49ea-ac24-163302a5214a · outbound

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

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 13

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Observation cab190fd-afbd-4385-9062-5b5152ae614a · outbound

This paper cites an unresolved cited work.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Unresolved cited work

Reference 14

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Observation a94b7b6e-f7f2-412a-b3ee-a6489b037587 · outbound

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

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models World Action Models are Zero-shot Policies

Reference 15

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Observation 9355e1ff-691d-49a1-8409-6c92dfffe13f · outbound

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

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Fast-WAM: Do World Action Models Need Test-time Future Imagination?

Reference 16

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Observation 984341a5-1e83-49d5-a77c-e7c2f02d8c17 · outbound

This paper cites HarmoWAM: Harmonizing Generalizable and Precise Manipulation via Adaptive World Action Models.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models HarmoWAM: Harmonizing Generalizable and Precise Manipulation via Adaptive World Action Models

Reference 17

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Observation f51892a0-944f-4767-a64e-ef3f7ba1865c · outbound

This paper cites OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation

Reference 18

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Observation 7c758a15-df9a-46c7-b0b1-b63d0bef67ca · outbound

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

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Reconstruction or Semantics? What Makes a Latent Space Useful for Robotic World Models

Reference 19

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Observation 10917a8c-ea85-4f46-a47a-d62809ba1595 · outbound

This paper cites Vaswani, N.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Vaswani, N

Reference 20

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Observation eed135af-6194-4c53-99c2-12536b916edf · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Neural Machine Translation by Jointly Learning to Align and Translate

Reference 21

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Observation 0c73e39b-554c-45a7-a4dc-8022156560e0 · outbound

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

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation

Reference 22

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Observation 69c4fadd-2c07-4f82-9eef-4e60645b0168 · outbound

This paper cites an unresolved cited work.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Unresolved cited work

Reference 23

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Observation 532d333b-68a5-44b2-9f6e-6c0818d3547d · outbound

This paper cites Causal World Modeling for Robot Control.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Causal World Modeling for Robot Control

Reference 24

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Observation c32bebfd-18a8-4124-8c1b-754ef66c6037 · outbound

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

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models EmbodieDreamer: Advancing Real2Sim2Real Transfer for Policy Training via Embodied World Modeling

Reference 25

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Observation f91ebc77-185f-475c-b610-e039a1ac3ff8 · outbound

This paper cites an unresolved cited work.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Unresolved cited work

Reference 26

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Observation 10c85fb8-9224-4b42-aa29-b04638a1793e · outbound

This paper cites Kelly, C.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Kelly, C

Reference 27

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Observation deca2004-37d6-4e14-a962-e0d6bdd19844 · outbound

This paper cites an unresolved cited work.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Unresolved cited work

Reference 28

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Observation 6c8d7aaa-0ba8-4022-a04d-d468bb0413bf · outbound

This paper cites Fujimoto, H.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Fujimoto, H

Reference 29

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Observation 1219f6d1-20b0-4594-a9bc-7a30e6ed42c2 · outbound

This paper cites an unresolved cited work.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Unresolved cited work

Reference 30

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Observation 262433fc-54d1-4a77-a407-e10cb37100ee · outbound

This paper cites LeCun, L.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models LeCun, L

Reference 31

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Pith citing papers

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

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch cites this paper.

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=sha256:4bc38bb13223a2d3a36f6699740457fa67fab327bd5f31ce20330747549514e3