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Paper Citation Record · LEDGER

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory

As of 20 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 0 inbound Pith citation observations for arXiv:2607.18840.

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

pith.paper-citation-record.v1
2607.18840 v1

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T14:13:45.530188Z

measured 68 of 68 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 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

68 of 68 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved67
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

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Outbound references

Observation 55a85e5e-de1d-4c00-a8ae-972fca75eee8 · outbound

This paper cites Worldarena: A unified benchmark for evaluating perception and functional utility of embodied world models.arXiv preprint arXiv:2602.08971, 2026.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Worldarena: A unified benchmark for evaluating perception and functional utility of embodied world models.arXiv preprint arXiv:2602.08971, 2026

Reference 1

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source=pdf_text observed=2026-08-01T14:13:37.815010Z digest=sha256:e0ce83a3c562eedf9c7923a3ac746837553f69b6f9c234a1d7a55598ac1c219f

Observation 33500c5d-d04e-4d5d-9a87-1cc16080ca99 · outbound

This paper cites Robosense: Large-scale dataset and benchmark for egocentric robot perception and navigation in crowded and unstructured environments.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Robosense: Large-scale dataset and benchmark for egocentric robot perception and navigation in crowded and unstructured environments

Reference 2

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source=pdf_text observed=2026-08-01T14:13:37.976274Z digest=sha256:948264b8a8cb40bbc84436fe5f2c7ffc8b1a21ebacc3641007b223c5bb3138cc

Observation 2f4861b9-cb1e-4b88-aef5-386a5d33f29d · outbound

This paper cites an unresolved cited work.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Unresolved cited work

Reference 3

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Observation 569038f2-9972-435f-bd96-ea0b6b62907f · outbound

This paper cites ${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory ${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities

Reference 4

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source=pdf_text observed=2026-08-01T14:13:38.379148Z digest=sha256:6f26f02862db5aca3f21cfa400a991e1d76bfc5707fc4d13771620a9e4019bc5

Observation f6c653f8-6a45-4959-b510-dde65752ffa5 · outbound

This paper cites Drivemoe: Mixture-of-expertsforvision-language-actionmodelinend-to-endautonomousdriving.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Drivemoe: Mixture-of-expertsforvision-language-actionmodelinend-to-endautonomousdriving

Reference 5

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source=pdf_text observed=2026-08-01T14:13:38.541294Z digest=sha256:9d613857026e9fb93e3cfa845bb5f0d691c84f139a4a2c117092ce7833e380a6

Observation af4b93c6-c724-4551-a423-2b3db0117eec · outbound

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

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Fast-WAM: Do World Action Models Need Test-time Future Imagination?

Reference 6

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Observation 425a0b7d-bd8b-4549-856c-322a19016686 · outbound

This paper cites Motus: A unified latent action world model.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Motus: A unified latent action world model

Reference 7

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source=pdf_text observed=2026-08-01T14:13:38.814437Z digest=sha256:7261fd726e58bf3c2e95317834978eac316557f16d4fb789202f55826b7537df

Observation 91b19e59-1abb-4746-902c-37ef6d6fb113 · outbound

This paper cites Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising

Reference 8

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Observation b06e9270-bd1a-4369-bc5c-d054248e615a · outbound

This paper cites Gigaworld-policy: An efficient action-centered world–action model.arXiv preprint arXiv:2603.17240, 2026.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Gigaworld-policy: An efficient action-centered world–action model.arXiv preprint arXiv:2603.17240, 2026

Reference 9

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Observation 81aa507c-0274-4d81-9a60-e9dfa6ac5d15 · outbound

This paper cites Causal world modeling for robot control.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Causal world modeling for robot control

Reference 10

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Observation c9289ebf-357e-41d5-9587-4552a03d6bc2 · outbound

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

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory World Action Models are Zero-shot Policies

Reference 11

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source=pdf_text observed=2026-08-01T14:13:39.223718Z digest=sha256:08114529ca55a290b5b8bcf4f259c8577eb28b70074b13b0e2fdb44832043f3e

Observation 372fdb1e-5908-4251-b944-80bf2036fa57 · outbound

This paper cites MemoryWAM: Efficient World Action Modeling with Persistent Memory.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory MemoryWAM: Efficient World Action Modeling with Persistent Memory

Reference 12

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source=pdf_text observed=2026-08-01T14:13:39.333086Z digest=sha256:6241784006b46bd9f2e4a0ff581b338d0aa462d0f75014b673d905e6cfa1348c

Observation 882b5e51-5cee-421d-b5aa-b41862f5f032 · outbound

This paper cites DINO-WM: World models on pre-trained visual features enable zero-shot planning.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory DINO-WM: World models on pre-trained visual features enable zero-shot planning

Reference 13

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Observation 40a1b1d2-bb1e-4501-9475-d31fbd9ca26d · outbound

This paper cites Enerverse: Envisioning em- bodied future space for robotics manipulation.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Enerverse: Envisioning em- bodied future space for robotics manipulation

Reference 14

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source=pdf_text observed=2026-08-01T14:13:39.529335Z digest=sha256:554edfb9641390fcbec2e02e0a0e187ec6482b4f43eca5faa883f9b796b29c4a

Observation 45583fcb-3af1-4342-bc6c-0ca983522649 · outbound

This paper cites Video prediction policy: A generalist robot policy with predictive visual representations.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Video prediction policy: A generalist robot policy with predictive visual representations

Reference 15

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Observation eb2e74b2-c1ae-4552-a602-5f812dfe7024 · outbound

This paper cites Dreamgen: Unlocking generalization in robot learning through video world models.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Dreamgen: Unlocking generalization in robot learning through video world models

Reference 16

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Observation f6de41c8-9195-40fe-aa81-7694b388cbdc · outbound

This paper cites Learning universal policies via text-guided video generation.Advances in neural information processing systems, 36:9156–9172, 2023.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Learning universal policies via text-guided video generation.Advances in neural information processing systems, 36:9156–9172, 2023

Reference 17

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Observation 359b81e4-7740-4388-b2e2-7f9871ed3641 · outbound

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

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Vidar: Embodied Video Diffusion Model for Generalist Manipulation

Reference 18

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Observation 535ee2bf-4f90-43ae-aefa-7836873278c8 · outbound

This paper cites Gen2Act: Human video generation in novel scenarios enables generalizable robot manipulation.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Gen2Act: Human video generation in novel scenarios enables generalizable robot manipulation

Reference 19

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Observation 2b8480e5-c48d-47c1-988c-7b857d5c5217 · outbound

This paper cites TSI: Temporal Saliency Integration for Video Action Recognition.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory TSI: Temporal Saliency Integration for Video Action Recognition

Reference 20

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Observation 8c3cc0c4-29f9-4e54-ab06-53f8bb564121 · outbound

This paper cites Discovering A Variety of Objects in Spatio-Temporal Human-Object Interactions.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Discovering A Variety of Objects in Spatio-Temporal Human-Object Interactions

Reference 21

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Observation f42bcf16-4f21-4df4-bce7-b6dbb7e8888c · outbound

This paper cites Collaborative Distillation in the Parameter and Spectrum Domains for Video Action Recognition.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Collaborative Distillation in the Parameter and Spectrum Domains for Video Action Recognition

Reference 22

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Observation c6783921-208f-49c3-b6c5-98cb378ed426 · outbound

This paper cites RoboDreamer: Learning compositional world models for robot imagination.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory RoboDreamer: Learning compositional world models for robot imagination

Reference 23

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Observation 6cf56b68-376d-4bfe-8efa-1521bc204a49 · outbound

This paper cites Drivemamba: Task-centric scalable state space model for efficient end-to-end autonomous driving.arXiv preprint arXiv:2602.13301, 2026.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Drivemamba: Task-centric scalable state space model for efficient end-to-end autonomous driving.arXiv preprint arXiv:2602.13301, 2026

Reference 24

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Observation be27caad-b6e2-432d-92d9-215e65331d7d · outbound

This paper cites Egofsd: Ego-centricfullysparseparadigmwithuncertainty denoising and iterative refinement for efficient end-to-end self-driving.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Egofsd: Ego-centricfullysparseparadigmwithuncertainty denoising and iterative refinement for efficient end-to-end self-driving

Reference 25

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Observation 3c2fddd7-d1a5-4842-98f5-36b61433c126 · outbound

This paper cites V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Reference 26

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Observation 6faa0534-60bd-4eef-b7ad-6fe12f24bc9b · outbound

This paper cites Gigaworld-0: Worldmodelsasdataenginetoempowerembodiedai.arXivpreprint arXiv:2511.19861, 2025.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Gigaworld-0: Worldmodelsasdataenginetoempowerembodiedai.arXivpreprint arXiv:2511.19861, 2025

Reference 27

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Observation 1b243485-4fd0-4d4a-a2c6-8a0db1408edc · outbound

This paper cites Cosmos policy: Fine-tuning video models for visuomotor control and planning.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Cosmos policy: Fine-tuning video models for visuomotor control and planning

Reference 28

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Observation ca47800b-33be-405b-9de4-152d62fb172a · outbound

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

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation

Reference 29

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source=pdf_text observed=2026-08-01T14:13:41.459402Z digest=sha256:61ce7e97b3f400022d48d1ad89bcbc0c58687afc4dc3cdc4632eddad9c960f0f

Observation b4b797fc-aab4-4c4f-9514-cd71785e8656 · outbound

This paper cites Long-VLA: Unleashing long-horizon capability of vision language action model for robot manipulation.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Long-VLA: Unleashing long-horizon capability of vision language action model for robot manipulation

Reference 30

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Observation 161d3985-54a1-4910-b68e-121f43527f27 · outbound

This paper cites VLA-OS: Structuring and dissecting planning representations and paradigms in vision-language-action models.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory VLA-OS: Structuring and dissecting planning representations and paradigms in vision-language-action models

Reference 31

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Observation 4db6b3af-ce6e-4652-b905-38cf93c605c7 · outbound

This paper cites MEM: Multi-scale embodied memory for vision language action models, 2026.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory MEM: Multi-scale embodied memory for vision language action models, 2026

Reference 32

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Observation 6fb34578-3438-472a-bf9c-f1444a842a2e · outbound

This paper cites MemoryVLA: Perceptual-cognitive memory in vision-language-action models for robotic manipulation.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory MemoryVLA: Perceptual-cognitive memory in vision-language-action models for robotic manipulation

Reference 33

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Observation 6c34152d-26e1-42c7-9543-bafaa629035f · outbound

This paper cites Long-Horizon Manipulation via Trace-Conditioned VLA Planning.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Long-Horizon Manipulation via Trace-Conditioned VLA Planning

Reference 34

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source=pdf_text observed=2026-08-01T14:13:42.241197Z digest=sha256:62beef75babe773138260e681f0e2398aa3624966d08e32056bc1c8ba3d7660e

Observation 91897aee-f866-46ad-ae20-469dbee0bbf0 · outbound

This paper cites Goal2Skill: Long-horizon manipulation with adaptive planning and reflection,.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Goal2Skill: Long-horizon manipulation with adaptive planning and reflection,

Reference 35

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source=pdf_text observed=2026-08-01T14:13:42.368717Z digest=sha256:51137ff041734d84ea8b73cf88063335635d21381094222df545f1fe1965b5e8

Observation a4e641f9-35d4-4368-b4d5-c0d28f917430 · outbound

This paper cites DSWAM: A dual-system world action foundation model for fine-grained robot manipulation,.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory DSWAM: A dual-system world action foundation model for fine-grained robot manipulation,

Reference 36

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source=pdf_text observed=2026-08-01T14:13:42.639609Z digest=sha256:113a3431f69dad5d162e6b2a6a7b1bcfa7b85d0fc63c06c202bf5ca757547af7

Observation cf19d58f-082a-4f28-832b-d405f84e2ad9 · outbound

This paper cites Barry, Kris Kitani, and George Konidaris.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Barry, Kris Kitani, and George Konidaris

Reference 37

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source=pdf_text observed=2026-08-01T14:13:42.839785Z digest=sha256:6cfdad87940428f0bdc10157bfebd15c96de1a40525b00a94d248a68ff36e082

Observation 80b73ad3-46cd-470b-9527-cdf32075db52 · outbound

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

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments

Reference 38

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source=pdf_text observed=2026-08-01T14:13:43.019889Z digest=sha256:59a5f2dad1b64ce0ae5bfe4588fa88156d22948eacc90c1be861e78504dabf46

Observation cee9344a-f529-4a00-9dce-d34e812cb017 · outbound

This paper cites DSWAM: A Dual-System World Action Foundation Model for Fine-Grained Robot Manipulation.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory DSWAM: A Dual-System World Action Foundation Model for Fine-Grained Robot Manipulation

Reference 39

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source=pdf_text observed=2026-08-01T14:13:42.700278Z digest=sha256:ecfdcb89e46a13577fdabaf3161612134404ee1c6c1911e4af1ea038ce396d71

Observation 243146ff-8915-4577-aa9b-ca46774dbc80 · outbound

This paper cites PixelVLA: Advancing pixel-level understanding in vision-language-action model.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory PixelVLA: Advancing pixel-level understanding in vision-language-action model

Reference 40

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source=pdf_text observed=2026-08-01T14:13:43.254333Z digest=sha256:d0417c1c1658d89b14dd87bc12818acf04e106f140b833e99a2a9789cd9d6ffb

Observation 8e2d2a05-8091-4924-b880-a62460a1624e · outbound

This paper cites SpatialVLA: Exploring spatial representations for vision-language-action models.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory SpatialVLA: Exploring spatial representations for vision-language-action models

Reference 41

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source=pdf_text observed=2026-08-01T14:13:43.372258Z digest=sha256:19b9e57198eb1ec432c126832d9adb23905addfa6aca8ad4723574f0107971bf

Observation 7c868e7c-0a96-43be-8bcc-ce4d9ef74c5f · outbound

This paper cites FineVLA: Fine-Grained Instruction Alignment for Steerable Vision-Language-Action Policies.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory FineVLA: Fine-Grained Instruction Alignment for Steerable Vision-Language-Action Policies

Reference 42

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source=pdf_text observed=2026-08-01T14:13:43.160700Z digest=sha256:6a2ee82e5f5d590af37d071d47810675f5b65e3b480c016ac81375f391f7813b

Observation 7d16b042-62f8-4733-8e1e-16a6d4b3dd69 · outbound

This paper cites SG- VLA: Learning spatially-grounded vision-language-action models for mobile manipulation, 2026.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory SG- VLA: Learning spatially-grounded vision-language-action models for mobile manipulation, 2026

Reference 43

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source=pdf_text observed=2026-08-01T14:13:43.628048Z digest=sha256:5d458abe45f6bf52bff54c18c6d5062a2e2bcd7bac76e2439649ab9f24a65d89

Observation 7534caab-3e2d-4f77-8dc3-3103f8df9616 · outbound

This paper cites TraceVLA: Visual trace prompting enhances spatial-temporal awareness for generalist robotic policies.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory TraceVLA: Visual trace prompting enhances spatial-temporal awareness for generalist robotic policies

Reference 44

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source=pdf_text observed=2026-08-01T14:13:43.735147Z digest=sha256:fc1b6877f8cb76a94db69cbd65f8eec0584498146f41d397af44f9863815bec4

Observation e6242b36-3a9c-460f-98f0-f70902d52a99 · outbound

This paper cites ReconVLA: Reconstructive vision-language-action model as effective robot perceiver.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory ReconVLA: Reconstructive vision-language-action model as effective robot perceiver

Reference 45

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-01T14:13:43.514761Z digest=sha256:613d9cbef02b2a8cace0b0e7fca7d0b3242e8acecd85c35257b2a0019da64226

Observation ee80f8f8-8e2d-4269-b9eb-1e688a82a04f · outbound

This paper cites Learning Generalizable Robot Policy with Human Demonstration Video as a Prompt.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Learning Generalizable Robot Policy with Human Demonstration Video as a Prompt

Reference 46

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source=pdf_text observed=2026-08-01T14:13:44.007600Z digest=sha256:7bce2ea4ea58a4a5b72b93246c7ff3262b418889f98d318adb414c901fd46767

Observation d362c639-9f6c-49e2-bac6-408c500d9a85 · outbound

This paper cites AgiBot World Colosseo: A large-scale manipulation platform for scalable and intelligent embodied systems.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory AgiBot World Colosseo: A large-scale manipulation platform for scalable and intelligent embodied systems

Reference 47

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source=pdf_text observed=2026-08-01T14:13:44.180705Z digest=sha256:27f8632b940307cbc32dd906402eecc6f3ca7004f81ab96739a8a31dddbd0b57

Observation 04cad03d-e3f8-4346-aad7-6ddb95b5a7fc · outbound

This paper cites InProceedingsoftheIEEE/CVFConferenceon Computer Vision and Pattern Recognition (CVPR), pages 1702–1713, June 2025.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory InProceedingsoftheIEEE/CVFConferenceon Computer Vision and Pattern Recognition (CVPR), pages 1702–1713, June 2025

Reference 48

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source=pdf_text observed=2026-08-01T14:13:43.818357Z digest=sha256:8cefd92df99c5bbbbce9ec3e70f7ad697a471dbc461d3ab33220ab39587b07fb

Observation 59199955-f162-42ee-b347-342f429ef0fc · outbound

This paper cites RoboCOIN: An open-sourced bimanual robotic data collection for integrated manipulation,.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory RoboCOIN: An open-sourced bimanual robotic data collection for integrated manipulation,

Reference 49

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source=pdf_text observed=2026-08-01T14:13:44.480590Z digest=sha256:94cd6aeac100763d8d0b0262f48d901744ff5b2b211bc445fb5bb5b296333339

Observation 13e6ec83-99d4-43b3-87f8-2c643d7689c9 · outbound

This paper cites DROID: A large-scale in-the-wild robot manipulation dataset.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory DROID: A large-scale in-the-wild robot manipulation dataset

Reference 50

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source=pdf_text observed=2026-08-01T14:13:44.624339Z digest=sha256:5dac6e26e7a9f2c800b5a4fec5ca9b2ac98eb344428cb79f3c5ff4af7ad0d73f

Observation e40e7c69-cabe-4426-a8dd-35c7c0e66b3a · outbound

This paper cites Robomind: Benchmark on multi-embodiment intelligence normative data for robot manipulation.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Robomind: Benchmark on multi-embodiment intelligence normative data for robot manipulation

Reference 51

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source=pdf_text observed=2026-08-01T14:13:44.285015Z digest=sha256:f933f4b3acc3417e120bb1849cadc6a5248c62e3b05ac5a40b6b5744894288e4

Observation 1c552217-e707-4c9f-80cb-2d1ca8af1d1a · outbound

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

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory LIBERO: Benchmarking knowledge transfer for lifelong robot learning

Reference 52

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source=pdf_text observed=2026-08-01T14:13:44.891602Z digest=sha256:f5f2c6798143fc993d32c69cb2e137f5be41443db4afdd7a49123f2ed2bdea54

Observation 7043e7a5-867c-454d-b080-a7293b078fc9 · outbound

This paper cites RoboCOIN: An Open-Sourced Bimanual Robotic Data Collection for Integrated Manipulation.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory RoboCOIN: An Open-Sourced Bimanual Robotic Data Collection for Integrated Manipulation

Reference 53

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source=pdf_text observed=2026-08-01T14:13:44.554216Z digest=sha256:86f77991534862e73387befd700b2212aef51941c9a8dbdc0aa4f53c9270024c

Observation 85770ba9-e171-433d-a7b1-fe1d43e705e7 · outbound

This paper cites Qwen3-VL Technical Report.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Qwen3-VL Technical Report

Reference 54

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source=pdf_text observed=2026-08-01T14:13:45.012525Z digest=sha256:a8e07d78025203116430f106bbe04e6e0fc41de9bcd2d2fb11ec4f245f3f7b82

Observation 03652136-8814-4fb7-b122-45b2f0eb6e00 · outbound

This paper cites Wan: Open and Advanced Large-Scale Video Generative Models.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Wan: Open and Advanced Large-Scale Video Generative Models

Reference 55

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source=pdf_text observed=2026-08-01T14:13:45.083917Z digest=sha256:214a3e6e1569384d2f26e10b53b4f266b595c9a6f69f2ca5244dae22e10766e6

Observation e14ea408-0cfd-4d97-9a24-31c838e49c6e · outbound

This paper cites RoboTwin 2.0: A scalable data generator and benchmark with strong domain randomization for robust bimanual robotic manipulation.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory RoboTwin 2.0: A scalable data generator and benchmark with strong domain randomization for robust bimanual robotic manipulation

Reference 56

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source=pdf_text observed=2026-08-01T14:13:44.737368Z digest=sha256:bf19ea40f755c193ccc6903570cccf056f158fe54ed1197362acbc1977f6d5ac

Observation 331af288-456c-469b-848b-144a8bbd6b83 · outbound

This paper cites an unresolved cited work.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Unresolved cited work

Reference 57

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source=pdf_text observed=2026-08-01T14:13:44.815116Z digest=sha256:2ded2c5e805a7988fe06a6b09c9488fa58133635e795047beff50d4891c7030d

Observation d5e05900-8822-47b9-b61f-233bc2530e6a · outbound

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

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory ABot-M0.5: Unified Mobility-and-Manipulation World Action Model

Reference 58

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source=pdf_text observed=2026-08-01T14:13:45.285868Z digest=sha256:169d9c10df311142dac0b71d7767f51d9c5f299f0d82c91967fad984067c9cc4

Observation 4c22a60a-d27a-4e73-af9a-6a8a8833cd0c · outbound

This paper cites Yoon, Mouli Sivapurapu, and Jian Zhang.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Yoon, Mouli Sivapurapu, and Jian Zhang

Reference 59

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source=pdf_text observed=2026-08-01T14:13:44.941570Z digest=sha256:1ca028b201ab1ecce13df013b64c0b07eba624e1e199a5e223958c86618ab40f

Observation a3082c4c-787e-4232-bce6-7070d9f27aaa · outbound

This paper cites Holobrain-0 technical report.arXiv preprint arXiv:2602.12062, 2026.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Holobrain-0 technical report.arXiv preprint arXiv:2602.12062, 2026

Reference 60

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source=pdf_text observed=2026-08-01T14:13:45.426224Z digest=sha256:2ee22d97f9cc87c74d1d4c9189551030c89bc756ec9f0b6a90560e0e7b8b2761

Observation 8db9f672-2b78-4965-bdc9-ed2e07dc608c · outbound

This paper cites Native Video-Action Pretraining for Generalizable Robot Control.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Native Video-Action Pretraining for Generalizable Robot Control

Reference 61

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source=pdf_text observed=2026-08-01T14:13:45.477522Z digest=sha256:3be626ef418e00d501f7434bc78d1ddb5b476845643c077efb6138a12c44ec49

Observation 723389a4-788d-42a6-a7b1-e9762bbe2a97 · outbound

This paper cites InProceedings of Robotics: Science and Systems, Los Angeles, CA, USA, June 2025.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory InProceedings of Robotics: Science and Systems, Los Angeles, CA, USA, June 2025

Reference 62

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source=pdf_text observed=2026-08-01T14:13:45.149253Z digest=sha256:aa00a900be81080f9e00d2036e10f8f8f195cefdbee8c506070ee75ac3dc656d

Observation 2a85c7a2-ec78-470f-941c-a68c14353b56 · outbound

This paper cites X-VLA: Soft-prompted transformerasscalablecross-embodimentvision-language-actionmodel.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory X-VLA: Soft-prompted transformerasscalablecross-embodimentvision-language-actionmodel

Reference 63

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source=pdf_text observed=2026-08-01T14:13:45.221774Z digest=sha256:6f71d981308245acd1c4e023756f1b1f8ab2a7a022f22172696d79f502afda3d

Observation e8680daa-fdba-4146-b56c-8a7064801848 · outbound

This paper cites A Pragmatic VLA Foundation Model.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory A Pragmatic VLA Foundation Model

Reference 65

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source=pdf_text observed=2026-08-01T14:13:45.374473Z digest=sha256:414e957ea27839ce2f7596efb6b80b20a7026d7646962c2820d653ff6191792c

Observation 8707f45d-d9ef-4eaa-81d5-8116b9e85798 · outbound

This paper cites Worldscape policy: Generalizable robotic learning via a foundation world model, 2026.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Worldscape policy: Generalizable robotic learning via a foundation world model, 2026

Reference 68

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source=pdf_text observed=2026-08-01T14:13:45.530188Z digest=sha256:67d7d246538564fe1dd16604b391d8c42dd7e38e5ed425ca914ed76d92f8f6a9

Observation 271471b9-0bc7-49ad-869b-f52ece1bd2e3 · outbound

This paper cites an unresolved cited work.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Unresolved cited work

Reference 2024

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source=pdf_text observed=2026-08-01T14:13:44.689350Z digest=sha256:997206e35319432cc2cb35f4e2b2ac7b2f28e9f96f82ace29599e3052c3ccab4

Observation 08a83553-19a3-4dbe-a115-de9b69fad730 · outbound

This paper cites an unresolved cited work.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Unresolved cited work

Reference 2025

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no resolver link, observed 2026-08-01T14:13:38.256042Z

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source=pdf_text observed=2026-08-01T14:13:38.256042Z digest=sha256:3c9f65ca2c90527a9afae953e30b34ca0bda21ea675264999f6aefc32e2fe8c4

Observation 66d974a0-49de-47ee-b496-144c8efccc38 · outbound

This paper cites Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection.

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection

Reference 2026

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-01T14:13:42.515238Z digest=sha256:ae0efd57b8d307f32a320e5dd84f680284ad057c9c690fbda9620198951859c6

Pith citing papers

No inbound Pith citation observations are available.