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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 18 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 2026-08-17T06:30:58.91139+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=sha256:9ff69e646cf0afc24d703d86e6ab3c61031dca7c569bfdd08b98b1fe33cde9bd

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=sha256:7fad1317bc6eb231490ca13278d8f35a34f4734e1320ce41b2e3e3850114bd71

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=sha256:525625f6aea7d44e41949175a6be5ab5b1d80be033844e39fe57817867189b69

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=sha256:2d64374126c2d4a909d363358fd7949dc5d46edccb321dab3bd2c29b91311d9b

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:fe2a1b238277722416dea7da6ae4bdde10d3974f497d52e39427bcb5e28eebd0

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:cb6e1e65a853ff32f0b323ffe1182806c7d27cc3c3f114aa923e2b8323d18a14

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:a1d117e0626ff8ae082e05cbb79ac34060ceaac18bde4f40677b5b71fdac799d

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T05:31:55.840869Z digest=sha256:8f2c229a3a38ffd2b56f9355432cef39bdbf830f239b97b7fdfac82674e4ccf2

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T05:31:55.846716Z digest=sha256:355b6fae6f846c450b3cac5550920fedf825353e3c46f57e72ae028efb001e21

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=sha256:3cfd1af0990e04fee5813a45203f2e47ece3cdb08bedbf4fd652ee0d471bdbd1

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:de125269c77481b94c7301d9b609f2cf4b176cdf6e572c0250168cc4f1c80dac

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=sha256:32a85e2754a2e2138544f0de32b386b2a135133180cb8aafef144cb2dab3e549

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=sha256:26d1b17357e7b7f734a9d05664b579edb237551fdccc37362b7d7dfd8da86e90

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:b59bb6d3ef126170206087ef99ebe2cd1d9930cb1a0ea8b1c041ffbc8806d76e

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=sha256:3ead9e891a41287185d6dcefd3283ac59559b04fb673419707c00311b90f520e

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T05:31:55.880456Z digest=sha256:3d483700a5f5156112cd6ee17f6394265bd279535057554796ac0f673395ebfc

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:ba084c1c81a9605d5f7c8265ca3bae0799af0f2fb289a165c65620959f750ed1

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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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T05:31:55.889270Z digest=sha256:a09c5fb2671ddedab2ee0f361d947c9e6cdbf1bb4e5848bb56d5fa0d0f2fceb7

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:a7b0a97ca5754361984c3a2d5277241d0a81733c9b4e1399975908c1e16d7bb6

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T05:31:55.898161Z digest=sha256:182efe691c880314d1d3641c937c681da8794f6fc9d4cfefec2e7320af78483b

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:b17097f315a13e907a41bdcf16a09f2f1491a3d31a35b92c445e963bc2792eb9

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:a05237a127225f66514c2ef77bf8268039b1e5bde513be314a4fab94355b8043

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:a79634d39ba0e4ef796e3fc63ed5c0b8660aa89d954c5773d3ddd8c9614af3b7

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T05:31:55.915897Z digest=sha256:55ef8b1649cb4a9f1a5d05af95690e623f192d37a39a5e5e6714e7c482c22c38

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=sha256:71aa4ca133643409ecf47d67e22a946692821f23802a61848aea8a5cc0802ddf

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:ba25c89d02d8acc887bed9dc9697d78666957db3883c97d88f4f52fb8415ff7b

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T05:31:55.929417Z digest=sha256:d6804b7e746564703d9d4e88557f3bd888e3aebe8ee854a49811d1cee3d5f498

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=sha256:4eb7a170d8e11537de6fe38973e373d7beb31c2c699d0964a7bad579cc1ea49e

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=sha256:935a3abb8eefc952b068ef98d8d9ac2942a525b3c6bc0126e7e7283f13503371

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=sha256:2953c60456d2a624713332db3b130fda96053857b9460efea4fb6d258a8dfe9a

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=sha256:7adf44be6acf157c14c9ea50b62939ad5bfa844427427a42aa8f7b9c3b4f179a

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=sha256:882b2dceb7d91ff4f0b7699fcca310949ff3441a5a9c78ad2c9e6ad40d832762

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=sha256:7716dbf841aa3fcf1cd60697bddc16ac9b36fa73049cf2dbe71d523a0fa63445

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

Resolution
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=sha256:4d7c49723bf9fcd74714c2d5929c7372dfeaf6802ca935eca22956689393b290

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:31:55.965513Z digest=sha256:3615f0911d3a30359f0e09f8fed49aafb6137c3eb62940218e02df86ec7020cd

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:31:55.969598Z digest=sha256:43bd5d838fec794923fc481e368111f62116d48a72537889225650f999eeea99

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

Resolution
unresolved
no resolver link, observed 2026-08-16T05:31:55.974072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:31:55.974072Z digest=sha256:679091661e87899820cad8d01984ce55a9346770cad318523e5386ddbe720a01

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

Resolution
unresolved
no resolver link, observed 2026-08-16T05:31:55.978621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:31:55.978621Z digest=sha256:227efff767b211052c5db00a9db4079066258cdcbf29a749131e7421932de074

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T05:31:55.983705Z digest=sha256:628cb17e5086c9ac002b1855b6329029cfb1c4efd008e7602eaa505cb2b4877c

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

Resolution
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=sha256:66c91a37141d39ade1b9d4e3593a8675361f5099e7102b7f87cbdd0627ff7c88

Pith citing papers

No inbound Pith citation observations are available.