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Paper Citation Record · LEDGER

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations

As of 19 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2504.20520.

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

pith.paper-citation-record.v1
2504.20520 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:31:55.988273Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

40 of 40 outbound references displayed

  • verified exact0
  • verified fuzzy8
  • unresolved32
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 724a6766-eec4-44d0-b6e7-b4c5ca824590 · outbound

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

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations OpenVLA: An Open-Source Vision-Language-Action Model

Reference 1

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source=pdf_text observed=2026-08-16T05:31:55.803958Z digest=sha256:90d20630b7dc0a897dbd131f7046fe963451758749ca6bfc055128bfa9b25fa1

Observation 697593da-946d-4691-8155-b15bde20acee · outbound

This paper cites Octo: An open-source generalist robot policy,.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Octo: An open-source generalist robot policy,

Reference 2

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source=pdf_text observed=2026-08-16T05:31:55.809516Z digest=sha256:3c855d8c1302692f89a0fc04358fa3a779bcc53c67944cc79d4306f77e3fbfd3

Observation 6519e67c-44dc-4544-a387-7a58f5154cae · outbound

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

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models

Reference 3

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source=pdf_text observed=2026-08-16T05:31:55.814353Z digest=sha256:ff64a8983fda46e7f7ff12cdbae7289a4e91371d664fcc4f27b9dbc19d764555

Observation 5823bcb9-675d-42b9-aed9-4b80bd71566a · outbound

This paper cites RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation

Reference 4

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source=pdf_text observed=2026-08-16T05:31:55.819513Z digest=sha256:b4ae673143edfca4c182ee6ab579161edcefd0243ba1cfe708cc32723d81f9fd

Observation b6d51c55-ef6a-49c8-b37e-b8b31e30ecc1 · outbound

This paper cites Diffusion policy: Visuomotor policy learning via ac- tion diffusion,.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Diffusion policy: Visuomotor policy learning via ac- tion diffusion,

Reference 5

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source=pdf_text observed=2026-08-16T05:31:55.824585Z digest=sha256:e7246be6b0276a4dd39bd71c73db502c7e1984636c649366824f08a57c9f7cfc

Observation e991310f-17b0-4821-9c74-6361d7759037 · outbound

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

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation

Reference 6

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source=pdf_text observed=2026-08-16T05:31:55.829605Z digest=sha256:1687f63f73028d34ab6210a9e30b3f4b98d1bc0da3f7547cd860a39f64ccc27e

Observation 63a5ba13-35d6-45dc-a177-b3b0b7b6f171 · outbound

This paper cites Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation

Reference 7

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source=pdf_text observed=2026-08-16T05:31:55.835673Z digest=sha256:9c0f5f70b873866232fc024a55008a62e38c4c78c9c81c666bfd40506ddef77d

Observation f73a30d8-8445-4eec-be15-0acb31934993 · outbound

This paper cites Real–sim–real transfer for real-world robot control policy learning with deep reinforcement learning,.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Real–sim–real transfer for real-world robot control policy learning with deep reinforcement learning,

Reference 8

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T05:31:55.840869Z digest=sha256:7c676caf5d3bc024c513218149e197095723e1e6485182829bc75f6b597d3138

Observation c0cdb703-48e4-4e67-ad7f-b7c63077fef4 · outbound

This paper cites A real2sim2real method for robust object grasping with neural surface reconstruction,.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations A real2sim2real method for robust object grasping with neural surface reconstruction,

Reference 9

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T05:31:55.846716Z digest=sha256:b9a6abe25466d5bd11f0210aec02534a770a3f9e8293fb24d15257f3a74abcdf

Observation d57f1de7-bda3-4e08-a4b7-cec64ff19267 · outbound

This paper cites 3d gaussian splatting for real-time radiance field rendering.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations 3d gaussian splatting for real-time radiance field rendering

Reference 10

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source=pdf_text observed=2026-08-16T05:31:55.851658Z digest=sha256:eb177cb2509075fdb6bbaa01b7a7f39fd0ad0efe88393873b03b21e145e3832d

Observation ef7d9bc3-7555-47da-a568-5f058ba49b53 · outbound

This paper cites SplatSim: Zero-Shot Sim2Real Transfer of RGB Manipulation Policies Using Gaussian Splatting.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations SplatSim: Zero-Shot Sim2Real Transfer of RGB Manipulation Policies Using Gaussian Splatting

Reference 11

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source=pdf_text observed=2026-08-16T05:31:55.856343Z digest=sha256:85cb494f4e868a51410e0f0889c868bacaa970599d659482e5693a5586862f0d

Observation aa830a25-c5a0-40e9-8baa-804ede1f487b · outbound

This paper cites RoboGSim: A Real2Sim2Real Robotic Gaussian Splatting Simulator.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations RoboGSim: A Real2Sim2Real Robotic Gaussian Splatting Simulator

Reference 12

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source=pdf_text observed=2026-08-16T05:31:55.861443Z digest=sha256:7a30cc8878f584c70b513f4eecff7dc07fbf2964c16b09cbe76fae184e719341

Observation 8166f4ac-dc0c-423f-b5ad-cfb92fcf2d92 · outbound

This paper cites URDFormer: A Pipeline for Constructing Articulated Simulation Environments from Real-World Images.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations URDFormer: A Pipeline for Constructing Articulated Simulation Environments from Real-World Images

Reference 13

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source=pdf_text observed=2026-08-16T05:31:55.866917Z digest=sha256:d5076abc88e0838b7d7a6a85dcb45c88f650b82320c56bf2a84958f9969983de

Observation 3b20c66b-5382-4ac4-96c8-5a4278987d23 · outbound

This paper cites A system for general in-hand object re-orientation,.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations A system for general in-hand object re-orientation,

Reference 14

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source=pdf_text observed=2026-08-16T05:31:55.871473Z digest=sha256:0f3e51f094946bce238e0754141916646d009f3f34ce9a1201b5f65bb79278d3

Observation 439ff0e7-26e5-482c-ac27-38bc5bcb0087 · outbound

This paper cites Visual dexterity: In-hand reorientation of novel and complex object shapes,.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Visual dexterity: In-hand reorientation of novel and complex object shapes,

Reference 15

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source=pdf_text observed=2026-08-16T05:31:55.875921Z digest=sha256:b131deac0b8a62979c77d3e4ac7f7013ae011519507a1914a3e5ca882b2be956

Observation a6987da9-45a3-493a-a5bd-d1b86c2ed5f4 · outbound

This paper cites Learning dexterous in-hand manipulation,.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Learning dexterous in-hand manipulation,

Reference 16

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T05:31:55.880456Z digest=sha256:9f960751b405fc871942558c14cd378082e5c494f44441376b80e4ca694b9f17

Observation 9e3fe534-7360-4e64-a34c-0bd21a0af5a8 · outbound

This paper cites Dextreme: Transfer of agile in-hand manipulation from simu- lation to reality,.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Dextreme: Transfer of agile in-hand manipulation from simu- lation to reality,

Reference 17

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source=pdf_text observed=2026-08-16T05:31:55.884944Z digest=sha256:c2dc5469e4a0f572a4cb5f92577fdb90b111783b26ed406e78c94659d96ad574

Observation ed964feb-2fb0-492a-9b1a-2a99e38fcd89 · outbound

This paper cites Real2sim2real: Self-supervised learning of physical single-step dynamic actions for planar robot casting,.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Real2sim2real: Self-supervised learning of physical single-step dynamic actions for planar robot casting,

Reference 18

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source=pdf_text observed=2026-08-16T05:31:55.889270Z digest=sha256:b48543e873124a2650cf92aa3ee6aa3547f98140e978a898c45308c696612530

Observation 280b2c5b-80a5-4ef4-8464-cce8fb185730 · outbound

This paper cites RL-GSBridge: 3D Gaussian Splatting Based Real2Sim2Real Method for Robotic Manipulation Learning.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations RL-GSBridge: 3D Gaussian Splatting Based Real2Sim2Real Method for Robotic Manipulation Learning

Reference 19

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source=pdf_text observed=2026-08-16T05:31:55.893608Z digest=sha256:ae9a91cc30f5fd3829d2a1741cce808149b36b88ce7647f5952df9828779c852

Observation ce377d2b-6d67-4b7c-a81a-5a499a76320a · outbound

This paper cites Retinagan: An object-aware approach to sim-to-real transfer,.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Retinagan: An object-aware approach to sim-to-real transfer,

Reference 20

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T05:31:55.898161Z digest=sha256:3ab2edec5869e10a5a27d6fc6d3a0de46da483c480a42f41c9e969bda0a278c1

Observation 59baa586-1292-42ec-80d4-d940ebf3ac16 · outbound

This paper cites GenAug: Retargeting behaviors to unseen situations via Generative Augmentation.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations GenAug: Retargeting behaviors to unseen situations via Generative Augmentation

Reference 21

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source=pdf_text observed=2026-08-16T05:31:55.902671Z digest=sha256:0890a747d305484946fc4dabf4670dfb54290e9a3ad6ee3df96ba3945d224700

Observation eb452bfc-af49-4c9b-bca6-87c6e85f7c34 · outbound

This paper cites CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learning.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learning

Reference 22

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source=pdf_text observed=2026-08-16T05:31:55.907248Z digest=sha256:c2450e0edf05c1802094b5903fb9ed2c937835daf2b34da1d0cd4bb4c3358d6c

Observation 0b208c99-aa67-4029-a99d-c19ef7265262 · outbound

This paper cites Language to Rewards for Robotic Skill Synthesis.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Language to Rewards for Robotic Skill Synthesis

Reference 23

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source=pdf_text observed=2026-08-16T05:31:55.911543Z digest=sha256:84f934767e799b991779460a5dd3ce82d62adeee5a5aa390d920f07f780e3356

Observation 7926283e-35ac-4d36-9682-cbe4141e65d9 · outbound

This paper cites Text2reward: Automated dense reward function generation for reinforcement learning,.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Text2reward: Automated dense reward function generation for reinforcement learning,

Reference 24

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T05:31:55.915897Z digest=sha256:d2692263786af30a9ed5794ef21460bb54ea611b2f451085a04595412200ea8d

Observation 463dbd0d-0492-4371-88ab-fa44c3261a63 · outbound

This paper cites Eureka: Human-Level Reward Design via Coding Large Language Models.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Eureka: Human-Level Reward Design via Coding Large Language Models

Reference 25

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source=pdf_text observed=2026-08-16T05:31:55.920234Z digest=sha256:c20cf7bd3673a329f69e1af918fa3bedced72952a5bf74a2dbbda0259c95566f

Observation 5e49f440-9299-4c4c-aa45-57d9ed67b91b · outbound

This paper cites Motif: Intrinsic Motivation from Artificial Intelligence Feedback.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Motif: Intrinsic Motivation from Artificial Intelligence Feedback

Reference 26

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source=pdf_text observed=2026-08-16T05:31:55.924776Z digest=sha256:64a7a55a2ab535eaa034903a23b43e175480895edaad2a5db36ad5e26c847a84

Observation 88b82ea4-3e62-48f3-982d-19a9a9984333 · outbound

This paper cites Liv: Language-image representations and rewards for robotic control,.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Liv: Language-image representations and rewards for robotic control,

Reference 27

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T05:31:55.929417Z digest=sha256:bd787a9363f6520615a816e1e50e0b613926a019eef6a54de6a6777e7c72b6e9

Observation b7d5836e-0166-409c-bbd8-bc3809cb2584 · outbound

This paper cites DrEureka: Language Model Guided Sim-To-Real Transfer.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations DrEureka: Language Model Guided Sim-To-Real Transfer

Reference 28

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source=pdf_text observed=2026-08-16T05:31:55.933829Z digest=sha256:23026a34a0a34edee5ba762c4b9c4e64da5e85a566416029e89a9b13a913f84e

Observation 3018beab-e469-4ac9-8e3b-626594bc91d9 · outbound

This paper cites Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning

Reference 29

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source=pdf_text observed=2026-08-16T05:31:55.938438Z digest=sha256:aa460b6e9065a96163306d0f6998573dc24ea7813da1a02a93eca6c0363bcb8e

Observation 14fd4e46-3faa-4086-98ac-57d7907e25ef · outbound

This paper cites LiFT: Unsupervised Reinforcement Learning with Foundation Models as Teachers.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations LiFT: Unsupervised Reinforcement Learning with Foundation Models as Teachers

Reference 30

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source=pdf_text observed=2026-08-16T05:31:55.943133Z digest=sha256:1da2d64886bae2d187b97a2fe0d2001471b143843c4b1f69e1ebd4839fd8db57

Observation 488cd1d1-f385-44ca-a97b-1de576fd0111 · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Constitutional AI: Harmlessness from AI Feedback

Reference 31

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source=pdf_text observed=2026-08-16T05:31:55.947625Z digest=sha256:567f36ce57892cea78aea94a5185cbbbcea204486cc620ade7f60d9304b35abe

Observation 9d02f681-62ea-4e8c-bdca-ca75113c4e45 · outbound

This paper cites PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training

Reference 32

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source=pdf_text observed=2026-08-16T05:31:55.952038Z digest=sha256:c93a21487afe8b37d38d098359c1ba835c56fff970456bb49e71fa682b23d044

Observation 1e78bded-9810-45f3-9f60-d1d02b92c022 · outbound

This paper cites RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback

Reference 33

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source=pdf_text observed=2026-08-16T05:31:55.956455Z digest=sha256:faa1e85f92390cca800175c1f3b40e9e25a0492c32f6d87cbdcec6aafa4fd993

Observation ee73f03b-dd0d-4e3a-b58b-7a5941387f41 · outbound

This paper cites Deep reinforcement learning from human preferences,.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Deep reinforcement learning from human preferences,

Reference 34

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unresolved
no resolver link, observed 2026-08-16T05:31:55.961203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:31:55.961203Z digest=sha256:12d0d6f7ae47cd1fb9efe947ec31806447598b0449237e3c3e848e44403b53c7

Observation b06b6397-f35b-46e6-aa15-bdc08b69178e · outbound

This paper cites Segment anything,.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Segment anything,

Reference 35

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unresolved
no resolver link, observed 2026-08-16T05:31:55.965513Z

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source=pdf_text observed=2026-08-16T05:31:55.965513Z digest=sha256:60cb3c4228d778c67a0ced18a4c928615e42efbc600b0361f77a3443da4cb673

Observation 6df01f5c-9790-417f-94f2-8740e2dfeba6 · outbound

This paper cites FoundationPose: Unified 6d pose estimation and tracking of novel objects,.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations FoundationPose: Unified 6d pose estimation and tracking of novel objects,

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-16T05:31:55.969598Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-16T05:31:55.969598Z digest=sha256:39c600d0eef90f65ccce855119957b0e1fc9feeba5080a5421d52a43f7b3a400

Observation e0f94b1a-1cfa-418e-8622-f92832a89bf9 · outbound

This paper cites SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation

Reference 37

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unresolved
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source=pdf_text observed=2026-08-16T05:31:55.974072Z digest=sha256:7c171f19861a23a52519a088b4e4d0e34965de8a341e3ba8d00e2d6184e1bf00

Observation 57eadecb-3a43-4401-83cd-af402209bc83 · outbound

This paper cites Qwen Technical Report.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Qwen Technical Report

Reference 38

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unresolved
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source=pdf_text observed=2026-08-16T05:31:55.978621Z digest=sha256:55b9ef875ea37900230214553564404c63ff1e80ad6b47862e92939f63fefadb

Observation 88b8ab7c-5a51-42b2-9757-5766d12ca51c · outbound

This paper cites Openai. gpt-4v(ision) system card.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Openai. gpt-4v(ision) system card

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T05:31:56.365606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T05:31:55.983705Z digest=sha256:e404e3c35560c299d6b24e4f5c02cf1c7fff66c13014645b4857c57abd8209de

Observation 787816da-4bca-4658-846e-1b5558db01e5 · outbound

This paper cites Emerging properties in self-supervised vision trans- formers,.

PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations Emerging properties in self-supervised vision trans- formers,

Reference 40

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unresolved
no resolver link, observed 2026-08-16T05:31:55.988273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:31:55.988273Z digest=sha256:0f6af7d31b013603687c9c5265eab2b9a775134139227ea1c23ea1451958bbda

Pith citing papers

No inbound Pith citation observations are available.