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Paper Citation Record · LEDGER

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models

As of 5 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2606.07107.

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

pith.paper-citation-record.v1
2606.07107 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-27T21:57:19.195413Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+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

41 of 41 outbound references displayed

  • verified exact27
  • verified fuzzy0
  • unresolved5
  • parse uncertain0
  • malformed identifier3
  • metadata mismatch6

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a9bb6995-54bd-44cb-9ff1-96059cd9f1ad · outbound

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

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models RT-1: Robotics Transformer for Real-World Control at Scale

Reference 1

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local_arxiv, observed 2026-06-27T22:01:20.703357Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 4897fa89-cbff-45be-87a6-f46d47f3e0a6 · outbound

This paper cites RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Reference 2

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local_arxiv, observed 2026-06-27T22:01:20.725705Z

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Observation 9a2e5326-7c02-4ac6-b128-7b4c6cbb397d · outbound

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

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models Octo: An Open-Source Generalist Robot Policy

Reference 3

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local_arxiv, observed 2026-06-27T22:01:20.768630Z

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Observation 16016c91-8334-4dd2-91e2-ffb59c3081df · outbound

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

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 5

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local_arxiv, observed 2026-07-02T17:37:14.623922Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 38c9908d-a2b6-464c-870c-1bb929500512 · outbound

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

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 6

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source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:818e1a37ec77a5c996272a14f38072d3c042d73a1e93bbe153b4a5c3a453e308

Observation d008da2a-006b-461b-a7ce-379f144634a9 · outbound

This paper cites an unresolved cited work.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models Unresolved cited work

Reference 7

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source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:a59952089a3544d4d0e36d4c9a3ba468cfb12391353baa21d185136b6ca26369

Observation 99307e44-80f7-406a-8eb8-74061ffd298f · outbound

This paper cites an unresolved cited work.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models Unresolved cited work

Reference 8

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source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:0f3d8550683109364b189b86ba3ca13cf33f6d0534665f7d1453afbdd377849e

Observation 6279852b-6c4a-4993-9ba4-7daba0e6d855 · outbound

This paper cites an unresolved cited work.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models Unresolved cited work

Reference 9

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source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:3e7b8616731a4cc54313f08fb8b964d955ca81885daebe09fe7f0209aa13fe00

Observation 9645646a-0ddf-4871-88aa-a1bf17a65926 · outbound

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

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models Robotic Control via Embodied Chain-of-Thought Reasoning

Reference 10

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local_arxiv, observed 2026-07-02T17:37:14.626466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:4f8877aaac64ac4ffaf4f246ed0f3de259351f3fc2f4d9c910cf50f0e59327ff

Observation 4edeea87-c77f-445b-bad2-aabcdd6115b2 · outbound

This paper cites ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning

Reference 11

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Observation 44f3d4b8-227c-4aec-ac46-5a2a1eeb1b4f · outbound

This paper cites CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models

Reference 12

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local_arxiv, observed 2026-07-02T17:37:14.619272Z

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source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:a1b567d5c589e4e2030a0317b64b283ff4d57ec8f76d5a322aa76ffbdc5d7e9a

Observation 91d61383-15fe-4595-9ede-964d610e8358 · outbound

This paper cites DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge

Reference 13

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local_arxiv, observed 2026-06-27T22:01:20.764920Z

Source-reported events for the cited work

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source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:e248dc4a0235ff6cf18f723c4f57a9f8079d120603186f643a843a085af5bd6a

Observation 1e31fea4-f51e-4b12-8c63-cfc7eb6a24bf · outbound

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

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model

Reference 14

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local_arxiv, observed 2026-06-27T22:01:20.743184Z

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source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:a6bc59c2d46c6c5671fe06246f99298d43b35d4d586935cd67cf5e371fd1cb04

Observation 8af57b5b-6e93-4cc6-9456-acc96e48fe0f · outbound

This paper cites GraphCoT-VLA: A 3D Spatial-Aware Reasoning Vision-Language-Action Model for Robotic Manipulation with Ambiguous Instructions.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models GraphCoT-VLA: A 3D Spatial-Aware Reasoning Vision-Language-Action Model for Robotic Manipulation with Ambiguous Instructions

Reference 16

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arxiv_id, observed 2026-06-27T22:01:20.749485Z

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source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:d0c8b418fd7e3ab7317e70a9b39530616b56b2732ed958f97350ad7994da8f72

Observation f4fa4270-f393-406b-97a6-4c89cde07725 · outbound

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

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models FAST: Efficient Action Tokenization for Vision-Language-Action Models

Reference 17

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local_arxiv, observed 2026-06-27T22:01:20.762517Z

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source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:5f010c2d77a7ac91b7b5898ded1e30280bd2670a225a24dce743b9aabc8933bb

Observation ec8a25c0-aee4-49ad-8533-127a21b188e5 · outbound

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

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success

Reference 18

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local_arxiv, observed 2026-06-27T22:01:20.746406Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 7f25fbfc-b49d-4ba4-883e-d50f60f590e1 · outbound

This paper cites $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization

Reference 19

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local_arxiv, observed 2026-06-27T22:01:20.773449Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:9c6c153bcfe238ea42f173beb65f075cc3c1bf80afe9d4303d639f136d08c5bd

Observation 8f122590-c670-496b-a2a2-14824805ff12 · outbound

This paper cites Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment

Reference 20

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doi_truncated, observed 2026-06-27T22:01:20.772681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:cc217c7d53a490e7fa8c5a60492341cc67bc59d3ecb6f8e57a655dd8331fb4b4

Observation 926e4efc-8352-42cc-a1fe-1c4dbb09b2af · outbound

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

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models MolmoAct: Action Reasoning Models that can Reason in Space

Reference 21

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local_arxiv, observed 2026-06-27T22:01:20.737020Z

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Observation 04e7d929-150b-4cb2-a099-c465f3a27b73 · outbound

This paper cites MolmoAct2: Action Reasoning Models for Real-world Deployment.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models MolmoAct2: Action Reasoning Models for Real-world Deployment

Reference 22

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source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:2cf26436cb99abe0f24354076a947ac71673556e88cc72ecc548aae0e409898d

Observation bf1a8ed5-9f26-48af-9c64-eebdc85e1e95 · outbound

This paper cites arXiv preprint arXiv:2601.11404 (2026).

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models arXiv preprint arXiv:2601.11404 (2026)

Reference 23

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arxiv_id, observed 2026-06-27T22:01:20.761649Z

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source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:7af5bf29fb11e76d0ddb7bef62bd85583a55bc899f1f3b6443ba8295b17a6f38

Observation f41e98b5-53e6-4130-bd62-7feefc8246ad · outbound

This paper cites Faster: Toward efficient autoregressive vision language action modeling via neural action tokenization.ArXiv, abs/2512.04952.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models Faster: Toward efficient autoregressive vision language action modeling via neural action tokenization.ArXiv, abs/2512.04952

Reference 24

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source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:67d9ef49c8d5fb805b1a8136e5fce9f084f5bb2aadfb8a61ac9bda40f3114e7d

Observation 232efa58-117f-4077-bde8-4426a6e8855b · outbound

This paper cites Actioncodec: What makes for good action tokenizers.arXiv preprint arXiv:2602.15397.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models Actioncodec: What makes for good action tokenizers.arXiv preprint arXiv:2602.15397

Reference 25

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source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:e99716f168b6ea9366dc293ff727b4acf014abbe46aae67dc7fd6af399923a26

Observation 01a4335b-2900-401b-8c06-630d8b31b941 · outbound

This paper cites an unresolved cited work.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models Unresolved cited work

Reference 26

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:581a72f468e802084e323557a744864e5b0fc32a56ed8c3e13c7ff4c8242348e

Observation 37fba301-a5d6-47aa-a04d-16a4e0c5ced1 · outbound

This paper cites an unresolved cited work.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models Unresolved cited work

Reference 27

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source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:3085c18d6782fd4481429a132bd86fbc9f578e9f14cf1794aa54389770a515c0

Observation 040e54ee-2b64-4b1b-9e13-d34a780fe6d5 · outbound

This paper cites Evaluating Real-World Robot Manipulation Policies in Simulation.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models Evaluating Real-World Robot Manipulation Policies in Simulation

Reference 28

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local_arxiv, observed 2026-07-02T17:37:14.615015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:2e3dec38178c67bbfba664dadb7296bfd6709822f5f6274f9fc690204ace0a1d

Observation f19d74f4-2ba4-4ce4-81e1-5cc6b53c9472 · outbound

This paper cites SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics

Reference 29

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local_arxiv, observed 2026-06-27T22:01:20.777960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:d7c9d09b66246228cea5031c7edc23355f32ba81d39dad3008455ce9b0b1ca82

Observation c965ed27-7ec9-4dfb-b139-1d319a49130c · outbound

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

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:07362f8823666f2b960298547a4400b4e9dbd2b2c2f3c4e70abbf227d9d2c3ff

Observation ef511a5a-743d-4a74-a90c-d9e233d771e4 · outbound

This paper cites WorldVLA: Towards Autoregressive Action World Model.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models WorldVLA: Towards Autoregressive Action World Model

Reference 31

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local_arxiv, observed 2026-06-27T22:01:20.718614Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:d22c360c200411cabd7b08047f2cc13a803fac066408177154e6053b2a058c23

Observation d2d9b6ec-0354-4764-9f33-6aa42cd3b73a · outbound

This paper cites K., Garcia Lambas, D., Ruiz, A.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models K., Garcia Lambas, D., Ruiz, A

Reference 32

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doi, observed 2026-06-27T22:01:20.726052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:fe71c5b56a5f93b85137b4679179aaf581a29e4c25a2d93c83097543fea456cd

Observation 7005dc0b-6eb1-4b6d-8f74-24dde036a48c · outbound

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

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models F1: A Vision-Language-Action Model Bridging Understanding and Generation to Actions

Reference 33

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local_arxiv, observed 2026-06-27T22:01:20.782776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:572a3889f5ce563657a5984a5b4196536588f505d63a848757de785dc144cf8b

Observation 917dd0ad-218a-45aa-bc73-c05360fef635 · outbound

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

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models Unified diffusion vla: Vision-language-action model via joint discrete denoising diffusion process

Reference 34

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arxiv_id, observed 2026-06-27T22:01:20.764361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:77e0c2fe835adc0868917f16f318a8f8b20433afd7bc23a096e1197526740bb9

Observation 821a1f20-b7a2-439b-b412-9ad8fa46ab38 · outbound

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

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation

Reference 35

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:b10679ef637e4ff947c3ea4daf79c7f1081e08304167b38bc97cc0d91d9cbc01

Observation ea87c927-a87d-41f3-94bf-c292af01f3ba · outbound

This paper cites Quantum error thresholds for gauge-redundant digitiza- tions of lattice field theories.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models Quantum error thresholds for gauge-redundant digitiza- tions of lattice field theories

Reference 36

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verified exact
doi, observed 2026-06-27T22:01:20.721570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:7cda264b69d558d93fcade66e25cdb448654e770629738ba53e78beed2fd1976

Observation aca34776-cf08-404e-9da6-0cec71cc4f5a · outbound

This paper cites Show Your Work: Scratchpads for Intermediate Computation with Language Models.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models Show Your Work: Scratchpads for Intermediate Computation with Language Models

Reference 37

Resolution
verified exact
local_arxiv, observed 2026-06-27T22:01:20.748977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:e2c43140475a72586bcb7218913e496d490a6d3c85c2684dbd49cbaad4d6dcb9

Observation 4f2499e8-72ad-42ee-af9b-32b77c4e75f0 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 38

Resolution
metadata mismatch
local_arxiv, observed 2026-06-27T22:01:20.766938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:e3eb77a26cd6ac28c0908834ccfdbbcf32ec597a8b2451d08eea0486f6e36fd6

Observation 7a9eac4a-74fb-4c34-8054-3bb219dba706 · outbound

This paper cites Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-07-02T17:37:14.616849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:5f499bd00b59eeed9e187700d03a8dca74b35928eff9a1ad3971e6220839ffe6

Observation bd2c1745-37b5-4354-a252-71231dd433c7 · outbound

This paper cites BridgeData V2: A Dataset for Robot Learning at Scale.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models BridgeData V2: A Dataset for Robot Learning at Scale

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-06-27T22:01:20.775416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:396acbd36dd105ad43051f1ae5bbbfa5c18044ed4849958c175c46279e48c1a2

Observation bb9e507e-cf42-4f5c-b0f5-634804e9c5ab · outbound

This paper cites QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation

Reference 41

Resolution
verified exact
local_arxiv, observed 2026-06-27T22:01:20.745662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:6911a5516aa36b88516bbdda301c7def2858a25d60a6f787c5579e12d1a43f78

Observation 3004bb29-fde7-40c0-a8a4-c2a449acc20c · outbound

This paper cites DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

Reference 42

Resolution
verified exact
local_arxiv, observed 2026-07-02T17:37:14.614096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:e305820863c66e73cdc6a13724cc667672d9ea2b18a86771b6b9b401fc8e2053

Observation 7f0660c6-30ee-4e8e-bc1f-ce78cd106158 · outbound

This paper cites DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset.

Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

Reference 43

Resolution
metadata mismatch
local_arxiv, observed 2026-06-27T22:01:20.770758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-27T21:57:19.195413Z digest=sha256:64b636e85938e9c1f5e6f0d82ec4855cc61491b4992c6ca7d3acde63a76c5407

Pith citing papers

No inbound Pith citation observations are available.