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Paper Citation Record · LEDGER

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments

As of 8 August 2026, this Paper Citation Record lists 98 of 98 outbound references and 0 inbound Pith citation observations for arXiv:2607.23969.

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

pith.paper-citation-record.v1
2607.23969 v2

Coverage vector

measured 98 of 98 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-31T23:29:15.643608Z

measured 98 of 98 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

98 of 98 outbound references displayed

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  • verified fuzzy0
  • unresolved98
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  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation 9aff173a-cffe-41b2-aac0-242cf82c2341 · outbound

This paper cites World Models.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments World Models

Reference 1

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source=arxiv_source observed=2026-07-31T23:29:08.487323Z digest=sha256:ada734be82261b9651855f7344c2308b0bf7e1b607d4a3194928deb97d3337d9

Observation f8ade7d5-abc8-49c6-93ce-2f5af0c5865a · outbound

This paper cites 2, 2022-06-27 , author=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments 2, 2022-06-27 , author=

Reference 2

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source=arxiv_source observed=2026-07-31T23:29:08.539120Z digest=sha256:c90e5fc9b6e632765cf20ebf71a6f66ce58733e2836f59c34e84a1cc660bc309

Observation e9d560b5-3f26-4b70-bb7c-cec86433b3af · outbound

This paper cites Mastering Diverse Domains through World Models.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Mastering Diverse Domains through World Models

Reference 3

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source=arxiv_source observed=2026-07-31T23:29:08.629120Z digest=sha256:7333bd1680094634aac457885e745ec87ea16d16ca553e345299d77027410187

Observation 2e38f635-3de2-4ebd-a6bd-60dafbbec58a · outbound

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

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments World Action Models are Zero-shot Policies

Reference 4

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source=arxiv_source observed=2026-07-31T23:29:08.701529Z digest=sha256:7e826750bf56fb8dcf31736badbd515c3e366622245047bd073c7f5ef9e24c30

Observation ca650434-0e48-4899-907d-2d7c16a96f58 · outbound

This paper cites A Comprehensive Survey on World Models for Embodied AI.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments A Comprehensive Survey on World Models for Embodied AI

Reference 5

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source=arxiv_source observed=2026-07-31T23:29:08.784100Z digest=sha256:72928c0996865b285a93de384db71b888b469465721a11935f7b5f44496c5ea7

Observation 592bd980-22c4-4a8e-b128-40f099c00b37 · outbound

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

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments World Action Models: The Next Frontier in Embodied AI

Reference 6

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source=arxiv_source observed=2026-07-31T23:29:08.872529Z digest=sha256:79866c1ea1c7fb69466810c53f44abea9e2f61539f768a4f4fed2a07b64605db

Observation c44d2b7e-8205-4087-bb94-478f1ec11ba7 · outbound

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

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Fast-WAM: Do World Action Models Need Test-time Future Imagination?

Reference 7

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source=arxiv_source observed=2026-07-31T23:29:08.958122Z digest=sha256:fbcaf7f44bb85f02fbe7e90b110b0b0988e97097bc8235eac8662bae014f7950

Observation f8e5d28b-23fd-4785-974e-ee95fc68cdce · outbound

This paper cites Being-H0.7: A Latent World-Action Model from Egocentric Videos.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Being-H0.7: A Latent World-Action Model from Egocentric Videos

Reference 8

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source=arxiv_source observed=2026-07-31T23:29:09.068666Z digest=sha256:bcdcbc0ccafb63370905df6f6613dbee8a8dc0566bbd42ac658f405c55e86719

Observation b05ffa71-cbb4-4d0b-a0a5-8bc971ecb44d · outbound

This paper cites 2024 IEEE International Conference on Robotics and Automation (ICRA) , pages=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments 2024 IEEE International Conference on Robotics and Automation (ICRA) , pages=

Reference 9

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source=arxiv_source observed=2026-07-31T23:29:09.156371Z digest=sha256:135f4aa070ac5e6f60b21d935461b2946cb21de7356107ceaf65dc7f65cfda57

Observation b9442ec6-bef4-4c0c-a402-c7d2175e9849 · outbound

This paper cites arXiv preprint arXiv:2512.05230 , year=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments arXiv preprint arXiv:2512.05230 , year=

Reference 10

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source=arxiv_source observed=2026-07-31T23:29:09.207983Z digest=sha256:0b660c6cad6ce5f1f00f5e60a0ef7abaf8e166bcf495963a3732ce4bb9d4ae4c

Observation b6377b33-8772-4ac9-81ea-d41e72ba9f84 · outbound

This paper cites Neural Processing Letters , volume=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Neural Processing Letters , volume=

Reference 11

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source=arxiv_source observed=2026-07-31T23:29:09.285517Z digest=sha256:f87d99ab4c2e85b49a31094b6cb88fefaa7c925bd61a36422d7d1acb2e477ebc

Observation bce56086-4343-4cd1-be26-21ca97ff959b · outbound

This paper cites The Essential Role of Causality in Foundation World Models for Embodied AI.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments The Essential Role of Causality in Foundation World Models for Embodied AI

Reference 12

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source=arxiv_source observed=2026-07-31T23:29:09.364817Z digest=sha256:b0c5bf2ee01fdf6ee72759747c2c062c2c4a34f6075ec0c9d6c4ff2bc7fe7fac

Observation fc252f21-92a7-47bc-a17e-e9abef623254 · outbound

This paper cites LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels

Reference 13

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source=arxiv_source observed=2026-07-31T23:29:09.447648Z digest=sha256:ebee40859c7be09a709ecbb9e9186ac3f768fa5ea34b5c8af6909ec516c8a9df

Observation 1316bebe-084d-4828-87c7-341cd7514b87 · outbound

This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 14

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source=arxiv_source observed=2026-07-31T23:29:09.502380Z digest=sha256:59685c7cfde8c9db7d42893295220519e598f0a764e9c87d8099280343d4f036

Observation 5257fef1-1b3d-4027-82e0-d3327da60c9a · outbound

This paper cites an unresolved cited work.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Unresolved cited work

Reference 15

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source=arxiv_source observed=2026-07-31T23:29:09.549040Z digest=sha256:e0fd513b8f4139b798f1590e8ed74fd0b971084383103b8b72f2fa430e89e831

Observation 9f387056-8794-4540-8385-91c32c0f6db0 · outbound

This paper cites arXiv preprint arXiv:2602.10098 , year=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments arXiv preprint arXiv:2602.10098 , year=

Reference 16

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source=arxiv_source observed=2026-07-31T23:29:09.627175Z digest=sha256:172a05069dabe33414d7021179fad0a948180698c78f09238030cfdb1e028d9e

Observation 2a34f9db-fab6-43ff-afe8-bf52eaa08840 · outbound

This paper cites 2019 IEEE/RSJ international conference on intelligent robots and systems (IROS) , pages=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments 2019 IEEE/RSJ international conference on intelligent robots and systems (IROS) , pages=

Reference 17

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source=arxiv_source observed=2026-07-31T23:29:09.682458Z digest=sha256:954005bc53d91163a84aca1bd9ee54040b273f6ad6305cbf0bed2751154012a9

Observation b497753b-9553-4b5f-b3a8-52015657d192 · outbound

This paper cites RoboNet: Large-Scale Multi-Robot Learning.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments RoboNet: Large-Scale Multi-Robot Learning

Reference 18

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source=arxiv_source observed=2026-07-31T23:29:09.743324Z digest=sha256:6e3c2ad7a8d6b2c69610918330184d379771384bcda53d8a18f5988b967b1b9b

Observation 04620409-6d04-4f5b-9d78-80dbd7025ffb · outbound

This paper cites 2024 IEEE International Conference on Robotics and Automation (ICRA) , pages=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments 2024 IEEE International Conference on Robotics and Automation (ICRA) , pages=

Reference 19

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source=arxiv_source observed=2026-07-31T23:29:09.804901Z digest=sha256:4f23226f1ff053a34da7aa882d442d157f2ce60df79c55589a34e0809ba5439c

Observation 1cc235e7-8466-4e5e-81be-1ccfb4d1c422 · outbound

This paper cites On Bringing Robots Home.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments On Bringing Robots Home

Reference 20

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source=arxiv_source observed=2026-07-31T23:29:09.863046Z digest=sha256:a211a8e1f946a4fc218ffcce23c4ab947206428b02d08180a5b1a34d30688bf8

Observation 94bb5d9a-4606-4814-b50b-45c06a939c4e · outbound

This paper cites Conference on Robot Learning , pages=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Conference on Robot Learning , pages=

Reference 21

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source=arxiv_source observed=2026-07-31T23:29:09.937945Z digest=sha256:7fd0ac1385d8f7a2df861f7ffdeaa9694d87fcffc7df6479108d287e2f471e13

Observation 0b28176d-abae-434c-84da-f67c9b9a1eff · outbound

This paper cites RT-1: Robotics Transformer for Real-World Control at Scale.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments RT-1: Robotics Transformer for Real-World Control at Scale

Reference 22

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source=arxiv_source observed=2026-07-31T23:29:10.035777Z digest=sha256:21e4a50d187ce4be2d3df679b815669dac009754b3e3acc851735246df3e81d7

Observation 46c3a552-a2a3-45f4-8c1c-54715abb1827 · outbound

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

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments OpenVLA: An Open-Source Vision-Language-Action Model

Reference 23

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source=arxiv_source observed=2026-07-31T23:29:10.097057Z digest=sha256:9f5093d42f144069e05816e2f510320fefcf160683e1ab45ee83e0f170bf3a67

Observation b9ee239f-4d54-45fe-90f9-603c9dbc0b92 · outbound

This paper cites PaliGemma 2: A Family of Versatile VLMs for Transfer.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments PaliGemma 2: A Family of Versatile VLMs for Transfer

Reference 24

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source=arxiv_source observed=2026-07-31T23:29:10.176384Z digest=sha256:5c7a0e885e0e9d5dfa9a35d3459d243af09bc032f7396623545966d6b3eca67e

Observation f2480c1d-4f46-46f1-8207-fa855b5c8fbd · outbound

This paper cites Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models

Reference 25

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source=arxiv_source observed=2026-07-31T23:29:10.232317Z digest=sha256:b8aa52f3e75f5d6c68a9c6896d5de9570f0c446800ef9276d933aaa919699e09

Observation deb53c9f-d945-40f5-9f2a-8a9b3b7013dc · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 26

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source=arxiv_source observed=2026-07-31T23:29:10.290017Z digest=sha256:7a78f1ac9609ddde949705ef2f6f5b7abd2b7f0a93d28900e491acfdbbd7d485

Observation a1774f11-72f9-483c-bb90-7f581f2d4827 · outbound

This paper cites International Conference on Learning Representations , volume=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments International Conference on Learning Representations , volume=

Reference 27

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source=arxiv_source observed=2026-07-31T23:29:10.356380Z digest=sha256:54d657b2395a82b2dc461a11b2ba7725205284730a3bc5e42af6d2c3496c6c20

Observation bcaf1dce-c122-49b5-bce8-6713156d8164 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Advances in Neural Information Processing Systems , volume=

Reference 28

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source=arxiv_source observed=2026-07-31T23:29:10.414184Z digest=sha256:1df5f33ed66940ac606645e191bc89f15accea89edf19bd54565e06065efb3f9

Observation 7b80d216-ffd2-488e-9e42-99deddea74db · outbound

This paper cites Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

Reference 29

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source=arxiv_source observed=2026-07-31T23:29:10.487635Z digest=sha256:1e4f0357bfc0ec282506bd3355d10c54ce44320b042cca388f22def0fd0be14e

Observation 633cbba2-7f07-4798-9534-79342ba3ad9f · outbound

This paper cites SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model

Reference 30

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source=arxiv_source observed=2026-07-31T23:29:10.547817Z digest=sha256:fd750ebf7a14d0b1cb9265a48f4f901b51dd2a949658360a088190d83f91215a

Observation 540a43cb-a0a5-4639-8722-5567ea48b787 · outbound

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

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

Reference 31

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source=arxiv_source observed=2026-07-31T23:29:10.626611Z digest=sha256:c72141b4a2ddbecbc152a748dd28e561be8d7b2c79fb3ae7a31ad1663d5ebc1f

Observation baf6ea43-c392-463e-9ea5-59d2c684839b · outbound

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

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos

Reference 32

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source=arxiv_source observed=2026-07-31T23:29:10.712814Z digest=sha256:7d17fc944dc88b5ea8d7fe07cdcb9c0cd77c9162da17edbe70e43e23fe5a2cf4

Observation b26ba6a8-4c94-48dd-9b1d-952ca755f278 · outbound

This paper cites Advances in neural information processing systems , volume=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Advances in neural information processing systems , volume=

Reference 33

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source=arxiv_source observed=2026-07-31T23:29:10.754206Z digest=sha256:b53fc047a7d3a03e5b18a0c62ca5238c021fc8b76b2caa1d0fd482d1d8da5e00

Observation ce6ceb5a-de04-48ff-8ea9-3a47d9ba1756 · outbound

This paper cites CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation

Reference 34

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source=arxiv_source observed=2026-07-31T23:29:10.806179Z digest=sha256:b1774be0fa41f7e46df349e6f0ddab95d287d090cdb0b2b036d86285a7802fe6

Observation 6bee72de-6074-4110-b49e-f341aa860f36 · outbound

This paper cites Discrete Diffusion VLA: Bringing Discrete Diffusion to Action Decoding in Vision-Language-Action Policies.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Discrete Diffusion VLA: Bringing Discrete Diffusion to Action Decoding in Vision-Language-Action Policies

Reference 35

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source=arxiv_source observed=2026-07-31T23:29:10.917183Z digest=sha256:7193af8447b19221cb46814a2fa1725a0938e25de9e52e1ae330144849b4533c

Observation 8e8c1220-ad05-4df1-9c15-daa20713eac0 · outbound

This paper cites an unresolved cited work.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Unresolved cited work

Reference 36

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source=arxiv_source observed=2026-07-31T23:29:10.996779Z digest=sha256:de3a3ceb1ca21942a5100c33e4cfe4d7f452143df85d4e3117f3bf0acb421aac

Observation b14307ff-a8ba-4faa-85eb-e5be23bd6177 · outbound

This paper cites DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control

Reference 37

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source=arxiv_source observed=2026-07-31T23:29:11.038884Z digest=sha256:730c41151637aa3b1c6efa71a9fa0c24bdd86f0fece26afd3acf64c9a3fa9594

Observation c5f0c356-c974-4be1-ab9f-2fe91aaa650e · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 38

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source=arxiv_source observed=2026-07-31T23:29:11.103303Z digest=sha256:406c153d83fde9f3d28ebfc1b422bc61cfc65f4da8e175a3a9604c862b08151c

Observation 9a174fc2-2bb8-4c1e-be67-b2896b226c41 · outbound

This paper cites Advances in neural information processing systems , volume=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Advances in neural information processing systems , volume=

Reference 39

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source=arxiv_source observed=2026-07-31T23:29:11.172016Z digest=sha256:da9c30e80d4e191d333921da5a6a26d687231437121ee360c2588289e7a517e0

Observation 4791e7da-433e-4373-8941-436fefa8aca3 · outbound

This paper cites Robotic Control via Embodied Chain-of-Thought Reasoning.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Robotic Control via Embodied Chain-of-Thought Reasoning

Reference 40

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source=arxiv_source observed=2026-07-31T23:29:11.223743Z digest=sha256:0fee8a5f2eea3bbafcfcabc9041eba3f71698fcdf97a466b3318001c427e3f92

Observation 7b2df35d-bb80-4ec3-a3bc-a7b102b7579f · outbound

This paper cites arXiv preprint arXiv:2505.11917 , year=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments arXiv preprint arXiv:2505.11917 , year=

Reference 41

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source=arxiv_source observed=2026-07-31T23:29:11.281446Z digest=sha256:a1302aec491fd78c2606d4eff46e0c08c49bdadd3ad2c77bdde79821242e23f8

Observation a0a6036c-4741-403c-8cff-b6b1e00e4028 · outbound

This paper cites Action-Free Reasoning for Policy Generalization.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Action-Free Reasoning for Policy Generalization

Reference 42

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source=arxiv_source observed=2026-07-31T23:29:11.330797Z digest=sha256:f8bd0069be1a453cf2ce07c94a01a08dca0e45b9f966d5cc8b5ed07555f5b4ee

Observation 6e7d83b2-e3d0-4783-a15f-f3cdf5a3900e · outbound

This paper cites arXiv preprint arXiv:2510.12276 , year=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments arXiv preprint arXiv:2510.12276 , year=

Reference 43

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source=arxiv_source observed=2026-07-31T23:29:11.402428Z digest=sha256:f8edec02c7d600b4e1028729100d5af07c9b417c3af98cf64cfef7958dd41d27

Observation 47e1f208-e676-4de5-9039-c428583ad788 · outbound

This paper cites Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages=

Reference 44

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source=arxiv_source observed=2026-07-31T23:29:11.455466Z digest=sha256:746787275f4aa103c32bee6f99d16f69b7448b91f2847e40000f4aaa33a3ee05

Observation c05cde0f-6701-44d0-b750-a099caf9b46b · outbound

This paper cites International conference on machine learning , pages=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments International conference on machine learning , pages=

Reference 45

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source=arxiv_source observed=2026-07-31T23:29:11.513635Z digest=sha256:b3fdc24e20a699ce405b320a25f738df0ea61e26fdec0778de9eaa3d313ae9fa

Observation 65a4736d-5364-429b-b4e2-d6f8c40c1018 · outbound

This paper cites Robotics research: volume 1 , pages=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Robotics research: volume 1 , pages=

Reference 46

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source=arxiv_source observed=2026-07-31T23:29:11.583499Z digest=sha256:9c6d11e13db94ec387d78b9c6ee49a130d49f4c62c5f5266149740f7cf74b801

Observation f6e052ac-a83c-430f-a307-8207af62625b · outbound

This paper cites MolmoAct: Action Reasoning Models that can Reason in Space.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments MolmoAct: Action Reasoning Models that can Reason in Space

Reference 47

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source=arxiv_source observed=2026-07-31T23:29:11.642958Z digest=sha256:4667e9c062f300d205e4b886f1d915985a62d885b267d4fd7edf09238277fc7e

Observation d127496d-5879-4a55-988c-0c5293ff1fdb · outbound

This paper cites Advancing Open-source World Models.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Advancing Open-source World Models

Reference 48

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source=arxiv_source observed=2026-07-31T23:29:11.734740Z digest=sha256:9488b91dabd00ec16ba8cdfbe433ebb2eeb7a73bf51fdcb3648bc3cee6fd7a8a

Observation b43486b3-2ca8-43ae-baf7-428e434791cc · outbound

This paper cites Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations

Reference 49

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source=arxiv_source observed=2026-07-31T23:29:11.777313Z digest=sha256:dcedad1d57c61f6fa3ecaff97bb8a48dc0e54ad22cb3fd76dab21463614cd924

Observation 52d2263b-5a80-4648-b2c6-c6a21b28f331 · outbound

This paper cites mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs

Reference 50

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source=arxiv_source observed=2026-07-31T23:29:11.861166Z digest=sha256:6cad37c045001cf33141b8fd9ac499d4daf98e2def501e5514b4359c10488033

Observation a9e56a06-1f2e-487e-bd9e-5f20e4c0aee8 · outbound

This paper cites Vidar: Embodied Video Diffusion Model for Generalist Manipulation.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Vidar: Embodied Video Diffusion Model for Generalist Manipulation

Reference 51

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source=arxiv_source observed=2026-07-31T23:29:11.912385Z digest=sha256:80ec542eb0fe0e79cd95d6b82b9e2807e05df2b85d20a0488c21996a1b532c46

Observation 55f901e4-ba58-4b7c-96b3-995629d11039 · outbound

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

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation

Reference 52

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source=arxiv_source observed=2026-07-31T23:29:12.001466Z digest=sha256:9a338a56e5eabddbbefcf051b59f610e5a9f86554762cdb1e59ad221600c6d33

Observation 6c145d1f-da87-40bf-81a5-32ff43a9ed75 · outbound

This paper cites Unified Video Action Model.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Unified Video Action Model

Reference 53

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source=arxiv_source observed=2026-07-31T23:29:12.065410Z digest=sha256:d898db824e1627be43792cd733bc8f78361831f2a21a01f5401cf979bae3e17e

Observation 6f7a5459-6d18-4623-9f02-c9b4cb7907e9 · outbound

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

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets

Reference 54

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source=arxiv_source observed=2026-07-31T23:29:12.130740Z digest=sha256:d8b65a1f1c0f7132d38384b7cf15f2be984561e0b6175fb99da9a09ac4f22494

Observation f84571dd-c72b-4b10-9976-8574fe66b7aa · outbound

This paper cites Video Generators are Robot Policies.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Video Generators are Robot Policies

Reference 55

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source=arxiv_source observed=2026-07-31T23:29:12.206357Z digest=sha256:29f6d4f429d689db561604be353a50a8f9442457f23ab85fd9dba4d761b9c5a8

Observation 7ea2fea6-c798-4885-9833-69c3d9f4c792 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 56

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source=arxiv_source observed=2026-07-31T23:29:12.302698Z digest=sha256:ee3efe7637e157da57843a92ad490926f2281d12f8f3fafada189b6041dd729c

Observation e04f0503-1fb7-4e65-aa96-579073383b62 · outbound

This paper cites Advances in neural information processing systems , volume=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Advances in neural information processing systems , volume=

Reference 57

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source=arxiv_source observed=2026-07-31T23:29:12.357742Z digest=sha256:f24dd08155793ae4da3a1434f764d8765b202ce5e9fc75c70df01ddd7401c53b

Observation b0f11994-c9be-4065-9db3-700f62ffc450 · outbound

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

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning

Reference 58

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source=arxiv_source observed=2026-07-31T23:29:12.473260Z digest=sha256:0bbb6e73753c2be8ca5b02aa561dfab03358bc3c2b932ebe5956db2bd3de86be

Observation fe3a07d4-e677-4e72-b750-f791f17eb3d6 · outbound

This paper cites Causal World Modeling for Robot Control.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Causal World Modeling for Robot Control

Reference 59

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source=arxiv_source observed=2026-07-31T23:29:12.535002Z digest=sha256:4254b45dcf406a7370edffa42a99a9bc3c6829445209154eb562659a5988fdfe

Observation f2e9edac-babb-4e70-99e7-ce64fbaeee98 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Advances in Neural Information Processing Systems , volume=

Reference 60

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source=arxiv_source observed=2026-07-31T23:29:12.622839Z digest=sha256:17f665742f6bc890a203c5afa32156e618c71177034f6c9e6715ca648d7ca1c9

Observation 82b0b169-6333-4ebe-85e5-29f67b301cad · outbound

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

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation

Reference 61

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source=arxiv_source observed=2026-07-31T23:29:12.680623Z digest=sha256:4a46c021303685d545f4d7431ae4ccb17207a58b355746c318dbd37c737b40d5

Observation 3c3df148-5809-48ff-8f42-f9548005a7f4 · outbound

This paper cites Forty-first International Conference on Machine Learning , year=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Forty-first International Conference on Machine Learning , year=

Reference 62

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source=arxiv_source observed=2026-07-31T23:29:12.779049Z digest=sha256:1c69d4a4b6a4ee2543d0bbfab20a4b80dc5881eee89ccb7658f91f15e54a54f7

Observation 368ae7db-1e29-4b2d-a3a5-e977e317cd82 · outbound

This paper cites RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation

Reference 63

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source=arxiv_source observed=2026-07-31T23:29:12.862507Z digest=sha256:54f927816351c39ee82fee202b7f2c6fbf8d682e18c1a3a5ea56203e6f4a4b70

Observation 08b1164e-9f3f-4ef8-a680-bade7b797e1f · outbound

This paper cites WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens

Reference 64

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source=arxiv_source observed=2026-07-31T23:29:12.915500Z digest=sha256:40c13245bb772e8031daa8ee243cbba9258078f0af8a1caedd8c5fd301d91752

Observation 80aaf03e-e55d-4701-a1eb-1ac4d5d2bab4 · outbound

This paper cites Conference on Robot Learning , pages=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Conference on Robot Learning , pages=

Reference 65

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source=arxiv_source observed=2026-07-31T23:29:13.014425Z digest=sha256:e77d21d948e1221d838da96298a6836fb42bdea55863ac77c7e83e57d4411df0

Observation 6553ad93-e38e-42b2-84d3-91595c74ce81 · outbound

This paper cites International Conference on Learning Representations , volume=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments International Conference on Learning Representations , volume=

Reference 66

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source=arxiv_source observed=2026-07-31T23:29:13.089413Z digest=sha256:f768fd3b1ccaae5ca03c063636fbdfbae0bff419d7f0af8cc1c0f2d56b27a0cb

Observation d9fd9978-ad21-4f9e-a293-01d4bb706956 · outbound

This paper cites Revisiting Feature Prediction for Learning Visual Representations from Video.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Revisiting Feature Prediction for Learning Visual Representations from Video

Reference 67

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source=arxiv_source observed=2026-07-31T23:29:13.143581Z digest=sha256:84550df3b849c87b6fd1c7d0f4d8c4cae7a0a0d367d498af84e7a7c99754d169

Observation 9733fde8-614c-461b-817f-41e025874933 · outbound

This paper cites 2024 IEEE International Conference on Robotics and Automation (ICRA) , pages=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments 2024 IEEE International Conference on Robotics and Automation (ICRA) , pages=

Reference 68

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source=arxiv_source observed=2026-07-31T23:29:13.245678Z digest=sha256:f20add4570d2d36085c770d9b24025c3231a044890cbb3cd8587195c6010e1db

Observation 764fb515-1074-4f22-ac24-8d367375fe88 · outbound

This paper cites ABot-M0.5: Unified Mobility-and-Manipulation World Action Model.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments ABot-M0.5: Unified Mobility-and-Manipulation World Action Model

Reference 69

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source=arxiv_source observed=2026-07-31T23:29:13.348512Z digest=sha256:762a1e659361e4ccc1af2a13ba40c832fe062c56c023b509d857ed7d50338cf5

Observation 1fffe45e-d672-451c-9adf-8879482907f9 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Advances in Neural Information Processing Systems , volume=

Reference 70

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source=arxiv_source observed=2026-07-31T23:29:13.416529Z digest=sha256:5950c67034339337eef90fcf9bfb91a1f10e878167c0e12d2f9f8d76c60fa770

Observation 3bd135a8-d2ee-4b77-86a5-d593ad63abb1 · outbound

This paper cites R3M: A Universal Visual Representation for Robot Manipulation.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments R3M: A Universal Visual Representation for Robot Manipulation

Reference 71

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source=arxiv_source observed=2026-07-31T23:29:13.476375Z digest=sha256:24aca4f1a359e2efa99e75164db87a02927254854efe7290d7df0354cb5178c3

Observation de823867-7ed9-41cc-9009-9fcca3e65e63 · outbound

This paper cites arXiv preprint arXiv:2512.17909 , year=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments arXiv preprint arXiv:2512.17909 , year=

Reference 72

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source=arxiv_source observed=2026-07-31T23:29:13.536505Z digest=sha256:adbebd35b8c7ed9904239016e732505b73c6c0cbbc9a51647609c85725c2d44e

Observation 20f0f3b4-b174-4473-bab8-fe126e6e5288 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 73

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source=arxiv_source observed=2026-07-31T23:29:13.609223Z digest=sha256:e71061d50569432a54806af3e2a67ee1e584b2f387e0b128828e087b0c15b773

Observation 701e957a-ea96-4280-af8b-aad1ebe8b5e3 · outbound

This paper cites Advances in neural information processing systems , volume=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Advances in neural information processing systems , volume=

Reference 74

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source=arxiv_source observed=2026-07-31T23:29:13.683357Z digest=sha256:2acc6cd94dd5f000f8d086e5365f5137fdd99391dbeb017d6e80f180bfc662e9

Observation 45734156-af5f-46f1-93b3-e5f0d13951f4 · outbound

This paper cites Forty-first international conference on machine learning , year=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Forty-first international conference on machine learning , year=

Reference 75

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source=arxiv_source observed=2026-07-31T23:29:13.733177Z digest=sha256:3010e6f79581651e819874e48b83a39f0767bfd8116cb485a557e938bf4542ca

Observation 67d60bba-339f-4004-9487-d2f4347b3a80 · outbound

This paper cites Learning Interactive Real-World Simulators.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Learning Interactive Real-World Simulators

Reference 76

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source=arxiv_source observed=2026-07-31T23:29:13.827099Z digest=sha256:5be25f66ae3a39fceb10f9144417a39ad85d7733d491be181ffed08ed7bf7f75

Observation 0404c085-dca3-45b9-8cef-b4cc906912fe · outbound

This paper cites The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from Pixels.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from Pixels

Reference 77

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source=arxiv_source observed=2026-07-31T23:29:13.904203Z digest=sha256:3e84b9ecaebde0f738fcf3f4bd52b72e07d237845ca70c71fa2f86a930d19f90

Observation 10d26ad0-1d6a-4f17-a91e-4384c91aa0d1 · outbound

This paper cites 2021 IEEE International Conference on Robotics and Automation (ICRA) , pages=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments 2021 IEEE International Conference on Robotics and Automation (ICRA) , pages=

Reference 78

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source=arxiv_source observed=2026-07-31T23:29:13.945562Z digest=sha256:f337941cb6605b5d5de3fce521188eb8c13a3e00f24793da6531529a1250ea39

Observation 12dbddb7-0f52-4e13-8fd6-1ec5d2bd043d · outbound

This paper cites International Conference on Learning Representations , volume=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments International Conference on Learning Representations , volume=

Reference 79

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no resolver link, observed 2026-07-31T23:29:14.025908Z

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source=arxiv_source observed=2026-07-31T23:29:14.025908Z digest=sha256:f83dd3ac393c78b4ea351d9c3c6ffda75a933e11ece320cddddc734f9e87da08

Observation 465c50b9-4d63-4644-bed8-7388d853ed0f · outbound

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

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments UniVLA: Learning to Act Anywhere with Task-centric Latent Actions

Reference 80

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source=arxiv_source observed=2026-07-31T23:29:14.109837Z digest=sha256:51e1cf8e5e9b9e448cd6c46e659971c6b8f3de61150e86c23d43e20eb7a5c12a

Observation 2a0e341b-a921-4d4a-a6b7-518f8afd5947 · outbound

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

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 81

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no resolver link, observed 2026-07-31T23:29:14.195412Z

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source=arxiv_source observed=2026-07-31T23:29:14.195412Z digest=sha256:4a264aab2f17d64155bb4a0ada26629403b635e2c79d1ac16a1f7e02077c0a53

Observation d6f0231a-4cc4-4036-8567-c03fef4938dd · outbound

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

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments FAST: Efficient Action Tokenization for Vision-Language-Action Models

Reference 82

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no resolver link, observed 2026-07-31T23:29:14.255716Z

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source=arxiv_source observed=2026-07-31T23:29:14.255716Z digest=sha256:59b23ca0cf868fab434212856539b88ad0f71791390339130356c1b568ce5820

Observation 40c254a8-8412-49c0-9de1-4dd4ed2cfd7c · outbound

This paper cites Proceedings of the Computer Vision and Pattern Recognition Conference , pages=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

Reference 83

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no resolver link, observed 2026-07-31T23:29:14.340521Z

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source=arxiv_source observed=2026-07-31T23:29:14.340521Z digest=sha256:c0f1bd2483ff6c3550d21e4323727087441d0c677e4ffbeb2ae8a322a7808a80

Observation c2be8850-3276-46aa-9d01-7ddd151ac2da · outbound

This paper cites WorldVLA: Towards Autoregressive Action World Model.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments WorldVLA: Towards Autoregressive Action World Model

Reference 84

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source=arxiv_source observed=2026-07-31T23:29:14.406155Z digest=sha256:69e0e6303d0d1a7bb27af78f6e9defc86ee1e002b3a10002ddb4bc4456eaa9d7

Observation f8469d52-052c-4331-94a1-f7b0fcdf2e2a · outbound

This paper cites villa-X: Enhancing Latent Action Modeling in Vision-Language-Action Models.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments villa-X: Enhancing Latent Action Modeling in Vision-Language-Action Models

Reference 85

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source=arxiv_source observed=2026-07-31T23:29:14.484130Z digest=sha256:ab2dddb5a3938822c17bf3790f080fab7b12e6691c8edf743b07277385939283

Observation 3047d48c-f629-4355-b454-3f697a086ab9 · outbound

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

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success

Reference 86

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source=arxiv_source observed=2026-07-31T23:29:14.581079Z digest=sha256:69635908d98bc88a61c46a9ebf8b07ba12c32bd68288cd799f5fa056e6d33801

Observation 86840a44-33c9-48bb-9291-b8a0bfb8299d · outbound

This paper cites International conference on learning representations , year=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments International conference on learning representations , year=

Reference 87

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no resolver link, observed 2026-07-31T23:29:14.665371Z

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source=arxiv_source observed=2026-07-31T23:29:14.665371Z digest=sha256:8631d9b3e32f63c6252639527e523845d46fd40668f3f4ad6aae1841ef15763b

Observation c1a8bea2-8c90-4bb2-b7a4-2aaf750495e6 · outbound

This paper cites LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models

Reference 88

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source=arxiv_source observed=2026-07-31T23:29:14.783083Z digest=sha256:9922c7d7ade79d0df45900da2a5d5f1697f1ab6d833fe18964d6c1b33e875fe1

Observation fc76ce2c-d9a3-4b24-857a-8bc1d33b1d8b · outbound

This paper cites an unresolved cited work.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Unresolved cited work

Reference 89

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source=arxiv_source observed=2026-07-31T23:29:14.871766Z digest=sha256:12775be7873f42a18e6f4ee00a8777a2a25bd2e66dee0d6d2c2b61a199dfa934

Observation 4a8e76f4-c3fd-4009-b867-daefbdc0b30a · outbound

This paper cites PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation

Reference 90

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source=arxiv_source observed=2026-07-31T23:29:14.980347Z digest=sha256:fa88da65ca7917e759250d930958797cdabd103f4d1af2e23294d6a7b1162eeb

Observation f1576b06-5cb1-48a3-9f55-0d732a22b789 · outbound

This paper cites ArXiv , year=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments ArXiv , year=

Reference 91

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source=arxiv_source observed=2026-07-31T23:29:15.082517Z digest=sha256:ad484d07e5c773ef7c6d260dab0f5dc9d70cefb3c892f9e6aad923b0a8044822

Observation de8b90de-0320-45ef-a25e-ba7e7feb28fa · outbound

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

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning

Reference 92

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source=arxiv_source observed=2026-07-31T23:29:15.145937Z digest=sha256:b5f8dbabaefc1567c993793e823db938e3fc1e300ae48f8e170882ce644c1e53

Observation ed654650-0329-44d0-ba12-85f5374e20d7 · outbound

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

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments

Reference 93

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source=arxiv_source observed=2026-07-31T23:29:15.208144Z digest=sha256:1e1e476a3e87349f9ed927518192a020332eb1e391b4f934df49a5e1e54b267d

Observation 1d2957e2-16d4-43a3-b7d7-7bcf90ee78af · outbound

This paper cites arXiv preprint arXiv:2602.12062 , year=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments arXiv preprint arXiv:2602.12062 , year=

Reference 94

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no resolver link, observed 2026-07-31T23:29:15.277999Z

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source=arxiv_source observed=2026-07-31T23:29:15.277999Z digest=sha256:5c9f3826ed6db602805bfa1887ebaa7235559598db6a43d912d54e8164a45994

Observation bf2d4617-930d-4f1e-9159-d16013d3e26b · outbound

This paper cites AttenA+: Rectifying Action Inequality in Robotic Foundation Models.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments AttenA+: Rectifying Action Inequality in Robotic Foundation Models

Reference 95

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source=arxiv_source observed=2026-07-31T23:29:15.346799Z digest=sha256:8a7dba84359687d37eb75293260f44425998183a8f87c053463cd750a01b26ca

Observation 720b3c77-7128-468e-8bed-864145797870 · outbound

This paper cites Qwen-RobotManip Technical Report: Alignment Unlocks Scale for Robotic Manipulation Foundation Models.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments Qwen-RobotManip Technical Report: Alignment Unlocks Scale for Robotic Manipulation Foundation Models

Reference 96

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source=arxiv_source observed=2026-07-31T23:29:15.462624Z digest=sha256:3c95ff2f567ebe1e8b1f1cde70e3c9ef4b949ab72cc7ae7665d747eb6177e595

Observation 95e3db30-fa90-42e0-8294-3544737834fb · outbound

This paper cites ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?

Reference 97

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source=arxiv_source observed=2026-07-31T23:29:15.579570Z digest=sha256:72c2e98961998f39dae6dcd93cd1ad71a1594d567a36caf84e195af40ddc09c9

Observation 98783439-de9c-4186-ac36-0dc7afd5d730 · outbound

This paper cites arXiv preprint arXiv:2602.11832 , year=.

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments arXiv preprint arXiv:2602.11832 , year=

Reference 98

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source=arxiv_source observed=2026-07-31T23:29:15.643608Z digest=sha256:57ff1a9ae5da3ad0ff241f53910e651ee26d4c3b5723d4e5a3ddcf7abfc4186d

Pith citing papers

No inbound Pith citation observations are available.