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Paper Citation Record · LEDGER

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert

As of 6 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2510.03896.

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

pith.paper-citation-record.v1
2510.03896 v2

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T11:38:13.498305Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T11:22:18.092130Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

55 of 55 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved54
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2d900af5-13e9-4ed3-aca9-08bb3fb1440e · outbound

This paper cites AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems

Reference 1

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source=arxiv_source observed=2026-08-04T11:38:10.868380Z digest=sha256:4520273a46112ca9ad84376fbde40b4d17952ae9459e25822d0539d66fcda140

Observation 6705dc5a-d02e-4859-a0be-361af795ee73 · outbound

This paper cites Scanqa: 3d question answering for spatial scene understanding.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Scanqa: 3d question answering for spatial scene understanding

Reference 2

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source=arxiv_source observed=2026-08-04T11:38:10.969501Z digest=sha256:215c2a2b32a6a3b1d73c90628fa712e96372e33d33ad5cbab9d35fbbe0ac510e

Observation 22d43650-fc58-4a76-a549-742549b60f62 · outbound

This paper cites Qwen2.5-VL Technical Report.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Qwen2.5-VL Technical Report

Reference 3

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source=arxiv_source observed=2026-08-04T11:38:11.153441Z digest=sha256:f4408eb846b298559bca9c4365487f224c39b1a00a904a86bd21ff2207b389a0

Observation 0f4400bf-11f6-448c-80b1-8118337a3ae2 · outbound

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

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

Reference 4

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source=arxiv_source observed=2026-08-04T11:38:11.242201Z digest=sha256:f4e1952fb9d1773ad77f71eb00c3cf8b01fbb4c90632de824f6808fc0519cefa

Observation af236d71-a32a-46f2-817b-9cfa4657666f · outbound

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

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 5

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no resolver link, observed 2026-08-04T11:38:11.335522Z

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source=arxiv_source observed=2026-08-04T11:38:11.335522Z digest=sha256:ad653090444a59ad96b28c58af817b0eadd400586b19dbffb103f4a106ec0b96

Observation a001cdba-79fd-4e4c-a41f-6d415ef4c3ce · outbound

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

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert RT-1: Robotics Transformer for Real-World Control at Scale

Reference 6

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source=arxiv_source observed=2026-08-04T11:38:11.436484Z digest=sha256:a875560f882b1a7124c54ca4f39c9130d89aa1fb4aceaa393e9f82462d6ed28c

Observation 82cd167f-a1ad-452d-a6d8-9646c13e431c · outbound

This paper cites SpatialBot: Precise Spatial Understanding with Vision Language Models.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert SpatialBot: Precise Spatial Understanding with Vision Language Models

Reference 7

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source=arxiv_source observed=2026-08-04T11:38:11.546062Z digest=sha256:21ecd627df251325d7945ef1bbc06276f606d79a37b6b3a504212a418914fc3d

Observation 05fd20a6-dd03-4bbc-9684-6b345a15f7fa · outbound

This paper cites GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation

Reference 8

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source=arxiv_source observed=2026-08-04T11:38:11.692188Z digest=sha256:854b23496643cb727f5d2d2486862a2ec8c02e0104433807794c11d322350844

Observation 8df2d820-c8ec-48a7-8ca2-f4c336bedee6 · outbound

This paper cites Spatialvlm: Endowing vision-language models with spatial reasoning capabilities.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Spatialvlm: Endowing vision-language models with spatial reasoning capabilities

Reference 9

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source=arxiv_source observed=2026-08-04T11:38:11.808245Z digest=sha256:d4e9b228b7ca84f63adb22be706851bd9189b04a51345011cfd17727e611b8a7

Observation 9ad3a46d-2789-40c0-b865-6db929e8ff25 · outbound

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

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation

Reference 10

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source=arxiv_source observed=2026-08-04T11:38:11.918624Z digest=sha256:c005286d19a17ff49230f943e7d837cb8b6101a9a3c28115886a4b68aa4ba362

Observation 279af389-e07f-402e-bdd3-e0b5bfc62529 · outbound

This paper cites Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks

Reference 11

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source=arxiv_source observed=2026-08-04T11:38:11.993007Z digest=sha256:f48e40793d22bb8472de40f6886c456d4cf1ac71e7d30bac503707be39338fe4

Observation 7e675eba-69f5-4df3-bb12-2f2943dcfa79 · outbound

This paper cites Diffusion policy: Visuomotor policy learning via action diffusion.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Diffusion policy: Visuomotor policy learning via action diffusion

Reference 12

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source=arxiv_source observed=2026-08-04T11:38:12.100181Z digest=sha256:c0716874992e43be11b8e146afcfc69861e3fc2a8e27c817d943b92f4865e23c

Observation 29a74409-cc0b-4786-930c-808c8faed2bf · outbound

This paper cites Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots

Reference 13

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source=arxiv_source observed=2026-08-04T11:38:12.265273Z digest=sha256:0d7aacffe8ba0f1e68b2966eb48f309f372388a83b5efc43dbd057ffb8b6d111

Observation 08cb888e-ccde-436b-ae5b-2e8a66ba4363 · outbound

This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 14

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source=arxiv_source observed=2026-08-04T11:38:12.335682Z digest=sha256:f45a21725d3df6304aeb0d02526b6eef1f250ac9e80b5c15daf51aa741c7f4b4

Observation 32f7666a-c50a-4aa0-b068-6f52022e29fe · outbound

This paper cites Scaffolding dexterous manipulation with vision-language models.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Scaffolding dexterous manipulation with vision-language models

Reference 15

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source=arxiv_source observed=2026-08-04T11:38:12.493377Z digest=sha256:925966fa3a4bb0e9a07b96cf9cdd36c14271cedeb61ceed2f071587fd457ebad

Observation 919b4223-84d1-41d7-ab4a-ef74e918ee6a · outbound

This paper cites Self-supervised 6d object pose estimation for robot manipulation.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Self-supervised 6d object pose estimation for robot manipulation

Reference 16

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source=arxiv_source observed=2026-08-04T11:38:12.497568Z digest=sha256:862b5fb0ef2412ea77fe9cb8b0a81dd2655731921d2e361c7b15020df610628f

Observation 1f8e2b77-a258-4feb-bfd2-716c672679e3 · outbound

This paper cites Scaling up and distilling down: Language-guided robot skill acquisition.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Scaling up and distilling down: Language-guided robot skill acquisition

Reference 17

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source=arxiv_source observed=2026-08-04T11:38:12.558690Z digest=sha256:84bed6f93f73b06ce3418e8038bf8e2e7c4da00577c33f1d866df5e40f384187

Observation 17140e0d-69c7-4021-b9ea-cb64e3f452a5 · outbound

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

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning

Reference 18

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source=arxiv_source observed=2026-08-04T11:38:12.597166Z digest=sha256:734a2d0cdc10d5f1c66b5b24ff45cb992bb7c978cecc07d90e79a7a0ba7cfaee

Observation 13847b29-9fb7-4c7a-8aea-0ed219205bcd · outbound

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

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization

Reference 19

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source=arxiv_source observed=2026-08-04T11:38:12.607785Z digest=sha256:37fd1e9994cbaafeb541898bffce8488f5629164fa14dad7745b5f2dcee0c17d

Observation 0a948c24-6221-4b87-8eb5-5b8394f2e3e1 · outbound

This paper cites RLBench: The Robot Learning Benchmark & Learning Environment.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert RLBench: The Robot Learning Benchmark & Learning Environment

Reference 20

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source=arxiv_source observed=2026-08-04T11:38:12.683587Z digest=sha256:6796be3ac116304aec827f192941cb5b81202b96d160680321323d012ffac1a4

Observation 734ae0d1-e69f-47f5-bbf1-55a898605f32 · outbound

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

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

Reference 21

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source=arxiv_source observed=2026-08-04T11:38:12.748225Z digest=sha256:78e41ab19c415d257a3a89116ff7d685f792745c0dc95f62a84ff65edd830c67

Observation 1eebb37f-6828-4727-a160-afaa9ffe3db8 · outbound

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

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert OpenVLA: An Open-Source Vision-Language-Action Model

Reference 22

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source=arxiv_source observed=2026-08-04T11:38:12.801414Z digest=sha256:7430f947a9382859045b68beb70c750396f1e6761d99ede595282ab70d48b040

Observation 52c4124a-de9b-46dc-ba94-7af6e168a164 · outbound

This paper cites Bridgevla: Input-output alignment for efficient 3d manipulation learning with vision-language models.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Bridgevla: Input-output alignment for efficient 3d manipulation learning with vision-language models

Reference 23

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source=arxiv_source observed=2026-08-04T11:38:12.894192Z digest=sha256:9365729c300b92fd84f2ba565f7a9e596b992ee942e98ad03f6803c363aaf5eb

Observation a54cd10d-8683-4f48-8073-6f64ff67f654 · outbound

This paper cites HAMSTER : Hierarchical action models for open-world robot manipulation.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert HAMSTER : Hierarchical action models for open-world robot manipulation

Reference 24

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source=arxiv_source observed=2026-08-04T11:38:12.951626Z digest=sha256:4198cf6c3c686d00cef214027d99f1fdc6e94a958199df6099740bd212ee85dd

Observation 195ee994-4ee7-483f-ba29-b6c5f258fcda · outbound

This paper cites Prompting depth anything for 4k resolution accurate metric depth estimation.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Prompting depth anything for 4k resolution accurate metric depth estimation

Reference 25

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source=arxiv_source observed=2026-08-04T11:38:13.025352Z digest=sha256:a8fa2e10dba12231639e3784ef623fe50a5e1f8916f963e5d500e8270ce579bd

Observation 576ef1ba-c230-49ed-831c-ed82bcd84797 · outbound

This paper cites Libero: Benchmarking knowledge transfer for lifelong robot learning.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Libero: Benchmarking knowledge transfer for lifelong robot learning

Reference 26

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source=arxiv_source observed=2026-08-04T11:38:13.098297Z digest=sha256:bef0da6fb63beea5c1072d07d1c9b4d0e3aa93378062892d50260630b7193e42

Observation 959ea317-4073-4a7c-9901-74d0171b7331 · outbound

This paper cites SQA3D: Situated Question Answering in 3D Scenes.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert SQA3D: Situated Question Answering in 3D Scenes

Reference 27

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source=arxiv_source observed=2026-08-04T11:38:13.152895Z digest=sha256:867248cfd884fc40ccd8578bf091f564cb6e149c3ce4872e346b0d816e7e08d8

Observation 0a397153-fde4-4e7b-a501-db2898e6790e · outbound

This paper cites kpam: Keypoint affordances for category-level robotic manipulation.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert kpam: Keypoint affordances for category-level robotic manipulation

Reference 28

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source=arxiv_source observed=2026-08-04T11:38:13.225909Z digest=sha256:9f449cf65cdb1033238cc423116f25fba85eebdcc13895f475952dc890071f2c

Observation 9926a2c0-886b-478c-9937-5def87db0754 · outbound

This paper cites Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks

Reference 29

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source=arxiv_source observed=2026-08-04T11:38:13.276311Z digest=sha256:c2553dc2a11bb2e2689c2e1240c2b7bda55fe6314d0c03799b20aafe7136e8fb

Observation daa0b88f-8771-4e6e-a980-9cd149283765 · outbound

This paper cites ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations

Reference 30

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source=arxiv_source observed=2026-08-04T11:38:13.289266Z digest=sha256:1937474385650c53f425a063d057d44a6ef60691a089c6ac2bb1c5af8e1802d9

Observation 387ae6f5-2860-4825-a704-bace5ed2cee4 · outbound

This paper cites Robotwin: Dual-arm robot benchmark with generative digital twins.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Robotwin: Dual-arm robot benchmark with generative digital twins

Reference 31

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source=arxiv_source observed=2026-08-04T11:38:13.306390Z digest=sha256:306d8f9b35d3dec3c3433aabc686c3220b8288baaaec5ea3948a367d41839fd1

Observation e263d66e-97ae-4088-8561-2a0cd2d5ac01 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert SAM 2: Segment Anything in Images and Videos

Reference 32

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source=arxiv_source observed=2026-08-04T11:38:13.368955Z digest=sha256:2df7342b1d0563e6fee19047e93dc106bbc7b74269502c29620f1d0f3e0c2989

Observation 70a06a49-00d6-4534-847b-aa12e09232a0 · outbound

This paper cites Robospatial: Teaching spatial understanding to 2d and 3d vision-language models for robotics.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Robospatial: Teaching spatial understanding to 2d and 3d vision-language models for robotics

Reference 33

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source=arxiv_source observed=2026-08-04T11:38:13.395854Z digest=sha256:ff95eb8200b1c685f6b8d8cf9393455f1f1b6200cd5ada6958ec331055fa8c87

Observation 650977dc-7803-430f-8789-c6633c2bd482 · outbound

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

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Octo: An Open-Source Generalist Robot Policy

Reference 34

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source=arxiv_source observed=2026-08-04T11:38:13.401007Z digest=sha256:936f383b83d312a04e7dd11593bf20916a55610fb802d73f1d7311073aa0ed75

Observation 25ec5aa9-862a-47ef-8dd8-9108bc52f29d · outbound

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

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert BridgeData V2: A Dataset for Robot Learning at Scale

Reference 35

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source=arxiv_source observed=2026-08-04T11:38:13.405332Z digest=sha256:c451e38a9460385124a86e0788bdcdb81e40ad96ad862497949b9045e8985920

Observation 597428a9-45f2-4bd1-802d-fa2d95a161f1 · outbound

This paper cites Moge: Unlocking accurate monocular geometry estimation for open-domain images with optimal training supervision.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Moge: Unlocking accurate monocular geometry estimation for open-domain images with optimal training supervision

Reference 36

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source=arxiv_source observed=2026-08-04T11:38:13.409770Z digest=sha256:aaf70460a7ba625f2e0104342c29463ff61353f3755cfa2ac8c8274818b19e15

Observation 5e109b2d-7d5b-42b7-bdb5-ed74b2153ac5 · outbound

This paper cites MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details

Reference 37

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source=arxiv_source observed=2026-08-04T11:38:13.413876Z digest=sha256:1bd304836a522b69cd77d378d85f5732abd6e802aa38070f555da2c62d14e58b

Observation 138c9e56-7323-4260-8dea-f6e977e5bb73 · outbound

This paper cites InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

Reference 38

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source=arxiv_source observed=2026-08-04T11:38:13.418183Z digest=sha256:1b367c164a1570aa52fd489b1269892f159ebd7ba3187ff4945799e12bec9f11

Observation ddbf931c-423d-421a-a4ca-cfe55191a39c · outbound

This paper cites Foundationstereo: Zero-shot stereo matching.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Foundationstereo: Zero-shot stereo matching

Reference 39

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source=arxiv_source observed=2026-08-04T11:38:13.423043Z digest=sha256:2a9d482fee5b9fbedaf3179d7455ed53d12f836c9b9873cf0bd5ad142de69f57

Observation 1655f0b4-1bce-43e9-9eca-17c7231d6702 · outbound

This paper cites Tinyvla: Towards fast, data-efficient vision-language-action models for robotic manipulation.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Tinyvla: Towards fast, data-efficient vision-language-action models for robotic manipulation

Reference 40

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source=arxiv_source observed=2026-08-04T11:38:13.428374Z digest=sha256:7fab2ae5afd0197b7aed1b73dd128abfe3457a8d348e2604840d6371d467b512

Observation aef902b5-d35d-451b-b1fe-b0ee836e13e7 · outbound

This paper cites Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence

Reference 41

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source=arxiv_source observed=2026-08-04T11:38:13.433037Z digest=sha256:82d9bfe184e2a7fbed0e6ac1441b2d7d1de3bd50cfedec75ec233ed39371e82f

Observation c7ae41c6-2b58-4834-a6a3-0db3b2ad9e46 · outbound

This paper cites Afforddp: Generalizable diffusion policy with transferable affordance.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Afforddp: Generalizable diffusion policy with transferable affordance

Reference 42

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source=arxiv_source observed=2026-08-04T11:38:13.437718Z digest=sha256:290ed3cec004d32435d6725de3c16d5b6885b67930506dd5f23dc60ba37f4ab6

Observation 91e98a71-d931-4be9-bace-b0c105635ad6 · outbound

This paper cites Magma: A foundation model for multimodal ai agents.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Magma: A foundation model for multimodal ai agents

Reference 43

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source=arxiv_source observed=2026-08-04T11:38:13.441892Z digest=sha256:6a9126e4f9e2c62679eadea84e716d977c160c1827fcc683a8b19e09d9188884

Observation fd28d95b-6a5b-4eea-992f-11715e562377 · outbound

This paper cites RoboEngine: Plug-and-Play Robot Data Augmentation with Semantic Robot Segmentation and Background Generation.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert RoboEngine: Plug-and-Play Robot Data Augmentation with Semantic Robot Segmentation and Background Generation

Reference 44

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source=arxiv_source observed=2026-08-04T11:38:13.445945Z digest=sha256:640e5e140a8290370a1680a82129ccd815d3671a169e1dbf581dadeff9dd8a6d

Observation faddb885-6fc6-4180-a6fc-477c51cb433b · outbound

This paper cites RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics

Reference 45

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no resolver link, observed 2026-08-04T11:38:13.449967Z

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source=arxiv_source observed=2026-08-04T11:38:13.449967Z digest=sha256:8865edf9fbdd8c2b583c1be330d27b540e3a7c887f6ceb984f8272667c639090

Observation 9ee241cc-d7f1-4fa1-b556-7415e12d06d2 · outbound

This paper cites 3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert 3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations

Reference 46

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no resolver link, observed 2026-08-04T11:38:13.454234Z

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source=arxiv_source observed=2026-08-04T11:38:13.454234Z digest=sha256:7afdfd763977da2a9396658758b9d13f00f7f3827e0bd9255a4ae8dc436d6253

Observation 2780ba4a-2dee-42a6-9abb-90a094268939 · outbound

This paper cites Vsr: a unified framework for document layout analysis combining vision, semantics and relations.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Vsr: a unified framework for document layout analysis combining vision, semantics and relations

Reference 47

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source=arxiv_source observed=2026-08-04T11:38:13.458794Z digest=sha256:5bb08a79505f02484d2cc604ac026791aa34de43c071c9748ee2a085913369be

Observation 4e2bd3ea-0518-4960-80e2-afdbf464c5ab · outbound

This paper cites Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

Reference 48

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no resolver link, observed 2026-08-04T11:38:13.463077Z

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source=arxiv_source observed=2026-08-04T11:38:13.463077Z digest=sha256:23ef69c4853b2bb276720f5b9a24d3512c4192e22d551fcac306e09fb2421750

Observation d947ec6d-b7b5-4312-a67d-8132fdde69d8 · outbound

This paper cites LLaVA-3D: A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert LLaVA-3D: A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness

Reference 49

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no resolver link, observed 2026-08-04T11:38:13.467839Z

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source=arxiv_source observed=2026-08-04T11:38:13.467839Z digest=sha256:ae58c3cf2d1ec474b9f4a5ef7d5cd319c40ed2e8bfcbd3aec1e8594e5053f1cc

Observation ca7e5a22-c2ec-44ce-a298-943c99ef1ec3 · outbound

This paper cites Learning Generalizable Manipulation Policies with Object-Centric 3D Representations.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Learning Generalizable Manipulation Policies with Object-Centric 3D Representations

Reference 50

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source=arxiv_source observed=2026-08-04T11:38:13.473378Z digest=sha256:54448655d06a67466fee5b70b889296b43dc8805e58e7eb058a3ed09b170ce3f

Observation 874f02d6-8be7-474a-85b9-6ceafd0c4ae5 · outbound

This paper cites Rt-2: Vision-language-action models transfer web knowledge to robotic control.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Rt-2: Vision-language-action models transfer web knowledge to robotic control

Reference 51

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source=arxiv_source observed=2026-08-04T11:38:13.478000Z digest=sha256:ef36ad55a15c3c84fc52a8d93683b5f451c1a57e69f880c275fe5df829130447

Observation f7507841-b0fc-452c-9986-579a0070d8c2 · outbound

This paper cites write newline.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert write newline

Reference 52

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source=arxiv_source observed=2026-08-04T11:38:13.482713Z digest=sha256:3bb2a45a05858fd38ed222ae356532d1ee7549ab095ad06a29311194b453c711

Observation db54393f-33c1-48a8-95f4-49bd12982c21 · outbound

This paper cites @esa (Ref.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert @esa (Ref

Reference 53

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source=arxiv_source observed=2026-08-04T11:38:13.488558Z digest=sha256:c62f92d86dca81320dc9d141d9f4aa8fd961297fa672ebb946807f081665a66a

Observation fcef9501-2455-451c-8794-ae8b3fe7d5ff · outbound

This paper cites an unresolved cited work.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Unresolved cited work

Reference 54

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source=arxiv_source observed=2026-08-04T11:38:13.493503Z digest=sha256:dca7c16e481b4187b4a33ab7981205a32c08d137772c7698ad234699e43164dd

Observation f9151aaa-ff97-49ea-8261-45600eaf1ffe · outbound

This paper cites an unresolved cited work.

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert Unresolved cited work

Reference 55

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source=arxiv_source observed=2026-08-04T11:38:13.498305Z digest=sha256:44bfbef9886996a4bd2527a7acd911801e926fdc66c74c8a7e47fa60874ab4d1

Pith citing papers

Observation fad50f49-4c1f-4da1-b74b-0cb4730dc5e8 · inbound

Perfect Demo Makes Poor Teacher: Learning Robust Alignment from Critical Motion Segments cites this paper.

Perfect Demo Makes Poor Teacher: Learning Robust Alignment from Critical Motion Segments GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert

Reference 10

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source=pdf_text observed=2026-08-02T11:22:18.092130Z digest=sha256:d1b511984c41abf19bc8283644eb472fdfa0ca61185e636135811e2b85f0e0f1