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Paper Citation Record · LEDGER

GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert

As of 21 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 2026-08-20T06:33:59.587034+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=sha256:062942070de82388c4883fdc8ed1de0407e8f95f0ff39e7682199c70a8cca2c6

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=sha256:7c483e8a1526d70edefb00f4bbb9732d6eea601b898f3cca680c9c8c97eedf23

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=sha256:7615b7de0339f770b26b814be902cd10c3d57a34f4950a06e5c56ec9c8aa5ffe

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=sha256:7e12528abab41a85f590f5a269b7c28e68428da4d8cbe6ee60dcd516161b4f85

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:ab7dd1d16527d9c13ddad45e805b7921a3225cf150bb387dd1ddebca1b1982c7

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=sha256:7e2e0d5012da95336f1564acd1709add7f5e3dec82e46c7b3a1dc43ae27d67f7

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:fdbb2f1d3595bd20e161de61497926f7b0691abd683f264925bc34ed45b17c83

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=sha256:22058e12254b5c41b43584d5f822e46dfaccb22802c0395bedb6552510a8251e

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:d48dd09d84692bb78004fc309c815881ac42a303adbacfc4913dc74792206401

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=sha256:44c50b2f4064fba217f22c295fdcddedf12a28277ee7c9f5609d7fd80f3b3052

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:d1a5fbd557c22daf85cb764641b1d40b72894192d1dc865972ef5f38d942750e

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=sha256:366b2518576eb745833be03d181b91f6be726343e1c18a01d67f4de53191ebd5

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:ca536fc8239ffca034534cd18a4d04fcce79595f752e528c260cd46556537fff

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:a955ea7d9a25b4d41bf1b6f5578885ec3b07a7809de6b6b860c596f4890396ea

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:a35522cce1022e16afd50d45eab3eb540b483ed01d8258260bda27eff94e3edd

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:ceaa1b11c243293ffcaeea7f48d0aee4ef2a872dddc9e6d9526e2d390f06a93d

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=sha256:56db17ba5a72bbbe2920ed976a9f42e7613876707205556e0ca1ab6c28c7ade2

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=sha256:2453422d24f7f3e65a9ce8add525efbdb23221aa69de7680a4aca8123430d2a4

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=sha256:84f069619c6b47921e999268e6c02a10ad40e385c7925e66e20d5a4d959d0914

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=sha256:7a2b3c6feff9112ef2e3893051ac7c757feafc94ceed82a96f90d1199f069300

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=sha256:22ef34d91d90073d88eaca9b6f8250b87e23912f03d321b6c203fe7eca55b6aa

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=sha256:7febb72bc15f093d76f6e423cc281c88f56341f881039c4014d3a5dbe21ce890

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=sha256:48d2eba4314122dc12befd65f8aa017e7b92b54687edb558e7f608281bdd9b80

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:df478bb1e569e73847c0a736a3e6e8736ff60eac1f1e674c7f1c22fa2a4739b9

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=sha256:5a5caf976520a866a5016b1bd7eb796b1a10bbaf38175c7137edf58893f6d3d1

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:b492d1e726a7bd5ff3cefb78caf6e05c343a73e4c6076d22b5392198644d9f24

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:a892eb96a661d3759e329b262c8cac1f0027ebbb36cd9d5145342abd53e2dd2e

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:df0dfe6d563026cf4104c4beb356fed15d1efa14faa180c7e7592aaaac53168a

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:c1887bfb600afe1f309c5bbd5b4fab91720a0e83eb599156e7ef79bb624ef97b

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:a24833bffc3deab3cef377559e1349f738e8744e06ae501dd40619db7978a4d2

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=sha256:3973904531904b3a62a7dbebc91efe8e7a3660fb3b792db5b41963f1188d5514

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=sha256:00cd3391d640362e6b9a321524936131bd78d2e1f10333ffb00cd8ebc2f84d5e

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=sha256:2752ca3f99204bf6f26bcb1587a1366f6d16acce55890cf44e16e7cab3e25e53

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=sha256:61651e7af3f9957254438eabea4c2fbea31ce1c65d58d899c0501f7ebc8ea020

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=sha256:22c698f22c0fed146eda36fddb5de53f2b08f16958de17f76b7c306faa77e73d

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:a092b293b7c70e8887d38872908729ba41e73307404af28a15fa8df2a314ab5b

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:e1823415882553e2b99ceb9338431199291096ef49cb36ce4ca84535fb52666d

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=sha256:831225792592fe7d33b8929c9b43edddc1e7a72cb7c2da56e001af2d6b425f4e

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:d7b36f274c79034b163cb965f90540ef32722daa7d6536b54909afaeada8b10a

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=sha256:44659e0c6c670663b9beb0b33ad094853171beb8a083e4663e67fa8b25020c94

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:f811c915a7a936bb571210db795c078ece36234caed2f581b25dacf0e36cc30b

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:c80fcce742e2fa7558605ce0d0b5765871a0803cea722f987434c53ee8cdca0f

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=sha256:3586bd75165e373e0e6673a7397da68bcbd38fd13e5cd3ed4f2410a9c7faa326

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=sha256:7e16f143755b8a100cec8ebef649b8e8b844866782da01d58a1a756b573e60ac

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=sha256:537c976c18e0fbeb284e20e0f683c0046faecdf076565d964621dedd141e29bf

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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source=arxiv_source observed=2026-08-04T11:38:13.454234Z digest=sha256:02e7a6ad17ea42d42a638d5b04b319be57ce746212f07755a5f9de0ed3fcc514

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:fb37b076b8563ca5e8dd5d893cc38f7f68805a611ddc9786a39116cbd8afc583

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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source=arxiv_source observed=2026-08-04T11:38:13.463077Z digest=sha256:12a9ed6d91a38bf446eb7deb0e5e62ff3600f54db5b6a6267d42c608325cefa5

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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source=arxiv_source observed=2026-08-04T11:38:13.467839Z digest=sha256:aa7c0ecfc8f317cfe3ae5a5f34093fdfee66b552a5b4d69a01a6dfe4c24a135c

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:cda410c3c82df9fc2ef5bf48f371736238c77e585dddb6c5fca0b09a92cb20e6

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=sha256:3c42b822eec0839a35608d3faa2c7bf0aa33de41fa2b36aebc31df1b64ef64a8

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=sha256:162859f5e5498ce466162dd2f666dcbfdc237828985afd0950da9ee41f3af8e4

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=sha256:6d5d394b2cdaeb8aada7cd889b2302bd9ec020a9b8bf52f0a9c5ddbf628b0866

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=sha256:62f113c2d59550e2af9050b893065711086bac1287df923ba35a4bd03d802011

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=sha256:79ea5fd4009e1ee16a541af7b16adccd3993beca884c060884a29d79d0cb8315

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=sha256:7acbb4e19ecd0ebe5eaf7ffa0ba58d2688a2c664081110f41a7793658748a448