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Paper Citation Record · LEDGER

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills

As of 19 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2608.12416.

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

pith.paper-citation-record.v1
2608.12416 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:35:57.405912Z

measured 37 of 37 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

37 of 37 outbound references displayed

  • verified exact0
  • verified fuzzy13
  • unresolved24
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ecc557c1-3834-4a52-83a8-9a498f8e671f · outbound

This paper cites Dimarogonas, and Danica Kragic.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Dimarogonas, and Danica Kragic

Reference 1

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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.

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Observation 5f3723ab-9b00-4bbf-9ae8-bffd49ba1f00 · outbound

This paper cites A review of robot learning for manip- ulation: Challenges, representations, and algorithms.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills A review of robot learning for manip- ulation: Challenges, representations, and algorithms

Reference 2

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

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Observation 2e6d6c29-980a-427c-bba2-1b392762f412 · outbound

This paper cites Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn

Reference 3

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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.

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Observation bab60f2f-f039-47e8-9d37-2eef7f847e2a · outbound

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

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Diffusion policy: Visuomotor policy learning via action diffusion

Reference 4

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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=2026-08-16T00:35:57.249144Z digest=sha256:51ccde14301d272c2f6a9f8cb0d180a74a452b7e4a20edf13a0f06acc3268d47

Observation 2e78ff35-6382-4a8b-bc30-548fdc086cd7 · outbound

This paper cites A Survey on Vision-Language-Action Models for Embodied AI.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills A Survey on Vision-Language-Action Models for Embodied AI

Reference 5

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-16T00:35:57.254175Z digest=sha256:4207f098118664e65d85b4636a8db09a7b74731f0fa8839f46a4c54a6b629000

Observation db2c8500-a6da-4cb5-9c7b-020ef9bd69a5 · outbound

This paper cites Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets

Reference 6

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source=pdf_text observed=2026-08-16T00:35:57.259262Z digest=sha256:d10d7e62b1a4f24ff9f7802e4f9c67a71e8b1962e0e51f627e85055b5f3d64e7

Observation 8eaf39d6-0d2f-45b1-9307-b80455095c31 · outbound

This paper cites Motus: A Unified Latent Action World Model.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Motus: A Unified Latent Action World Model

Reference 7

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source=pdf_text observed=2026-08-16T00:35:57.264664Z digest=sha256:7421fe499c5bd36399593c71a1c1cdb298d3f9618401a54b0d7595ae5b57d28a

Observation 3647e897-ae43-444f-8c2d-3adb2e73be70 · outbound

This paper cites Causal World Modeling for Robot Control.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Causal World Modeling for Robot Control

Reference 8

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source=pdf_text observed=2026-08-16T00:35:57.269530Z digest=sha256:8ac9f2715fa2ca8c3bcbd762f409c65a0adf9477c666cc7be7409cbf1bd62fbd

Observation e7e29899-399d-494b-9bd0-9a00f59aed35 · outbound

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

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills World Action Models are Zero-shot Policies

Reference 9

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source=pdf_text observed=2026-08-16T00:35:57.274280Z digest=sha256:e4185566fcce230f9da9d0c8a39d529df99662396c5d52a503c54c839319c694

Observation 3bb46dec-82f5-4d15-88f6-7f58b3504786 · outbound

This paper cites A survey of embodied learning for object-centric robotic manipulation.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills A survey of embodied learning for object-centric robotic manipulation

Reference 10

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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=2026-08-16T00:35:57.279105Z digest=sha256:0025aa284a8138de63ea6bb3d87a97b20c18df342b16590238088c789b114031

Observation f4e49c4a-1573-4b14-aea0-353a83be860a · outbound

This paper cites Rlbench: The robot learning benchmark & learning environment.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Rlbench: The robot learning benchmark & learning environment

Reference 11

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source=pdf_text observed=2026-08-16T00:35:57.283788Z digest=sha256:51572ff92390aa40b40f8ab70233a64a8ea1924b0b7964021109643ca4ec056c

Observation b39745eb-1645-407f-9682-2a59569bdcc6 · outbound

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

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks

Reference 12

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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=2026-08-16T00:35:57.288410Z digest=sha256:ff41c41b25edd19a52c8e8ba3ec6bcdb29b11ca00523071d7f7f6f59eb7fd3ed

Observation e3958f9d-8132-4e8a-bccb-4990f0613728 · outbound

This paper cites LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning

Reference 13

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source=pdf_text observed=2026-08-16T00:35:57.292942Z digest=sha256:a2daebeeaa89c21f75a67e0530f7d7efbf046dfb8599faaf1643e2e239d18382

Observation 0e4ab3af-ff24-4e2f-9c13-610be177679b · outbound

This paper cites RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins (early version).

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins (early version)

Reference 14

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source=pdf_text observed=2026-08-16T00:35:57.298034Z digest=sha256:e6445f7b7f5989e77e3028bf25205a30c187f326387b4d3953c52bca9dfc4d00

Observation 0087b1a5-6473-4ed7-9af6-fa739aff6f70 · outbound

This paper cites Robochallenge: Large-scale real-robot evaluation of embodied policies.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Robochallenge: Large-scale real-robot evaluation of embodied policies

Reference 15

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source=pdf_text observed=2026-08-16T00:35:57.303097Z digest=sha256:b17ade9851ab66192f56c41170c15b502c2b8d1adc80fc88758b6afbda63a633

Observation 94d88f8f-0bbd-4cc3-8507-7b87811652de · outbound

This paper cites Robotarena $\infty$: Unlimited robot bench- marking via real-to-sim translation.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Robotarena $\infty$: Unlimited robot bench- marking via real-to-sim translation

Reference 16

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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=2026-08-16T00:35:57.307743Z digest=sha256:2461befbcc5bd9ae3c0458ca2b571b08a8a177d4e7262ba9842accf6d3fe2bae

Observation 64f2ef62-3496-4c0a-94fe-18faaeb7d3ea · outbound

This paper cites Roboarena: Distributed real-world evaluation of generalist robot policies.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Roboarena: Distributed real-world evaluation of generalist robot policies

Reference 17

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source=pdf_text observed=2026-08-16T00:35:57.312158Z digest=sha256:796c40d853d0b9fcf4361e03f07b7c4decf76005f1fcdc0c65815f7360d459b9

Observation 4f4d3319-876e-4cec-84b2-038dc0fbeba1 · outbound

This paper cites Manipulationnet: An infrastructure for benchmarking real-world robot manipulation with phys- ical skill challenges and embodied multimodal reasoning.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Manipulationnet: An infrastructure for benchmarking real-world robot manipulation with phys- ical skill challenges and embodied multimodal reasoning

Reference 18

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source=pdf_text observed=2026-08-16T00:35:57.316439Z digest=sha256:ef6f0b67ac2047b19e31770ff2ac938a00e3225f8bf707b5607384167b3c420c

Observation e4d6cbc7-4887-4a33-b545-9e86199230d4 · outbound

This paper cites Mimicgen: A data generation system for scalable robot learning using human demonstrations.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Mimicgen: A data generation system for scalable robot learning using human demonstrations

Reference 19

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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=2026-08-16T00:35:57.320836Z digest=sha256:66d9533163d17b1e6daf3eed79997867943950bcbd600ab6028ea9446bf6abbf

Observation e1ea313a-c00b-4896-930c-a53dc5cb6b2c · outbound

This paper cites GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Data.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Data

Reference 20

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source=pdf_text observed=2026-08-16T00:35:57.325165Z digest=sha256:4369946640db234d1be9fada6ff6734c070e11e89917be7b6338f143177f2d80

Observation 2030793a-5e41-4aeb-9daa-bab6ea101f1c · outbound

This paper cites Dexscale: Automating data scaling for sim2real generalizable robot control.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Dexscale: Automating data scaling for sim2real generalizable robot control

Reference 21

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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=2026-08-16T00:35:57.330024Z digest=sha256:610e9b723d5cdc649389430d39616b1d4867010a2e62e25505de40e3979f8423

Observation 56d97c3a-3450-4460-9809-1b6f51507540 · outbound

This paper cites Sim2real VLA: Zero-shot generalization of synthesized skills to realistic manipulation.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Sim2real VLA: Zero-shot generalization of synthesized skills to realistic manipulation

Reference 22

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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=2026-08-16T00:35:57.334551Z digest=sha256:7c6b193742e1235c04770222260e6c52ca1380d9aca958f9df60fed3b3c4c771

Observation 393d1504-e3a8-4c62-ab86-423b0553f958 · outbound

This paper cites Vision language action models in robotic manipulation: A systematic review.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Vision language action models in robotic manipulation: A systematic review

Reference 23

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source=pdf_text observed=2026-08-16T00:35:57.339005Z digest=sha256:18fc437c2d6cf4fd8dba942beabd4582b0a9539466bd75238cd1c43d74a52f5e

Observation a3ef9d86-69e1-46c0-bd0a-1ff3f4692c1f · outbound

This paper cites RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots

Reference 24

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source=pdf_text observed=2026-08-16T00:35:57.343606Z digest=sha256:a7eaf7b987c7d76abd356031ab8b2f72152b29e7800cef3c92caa765a3c371e1

Observation 68b83489-8cef-4652-88e3-44744ee44dfb · outbound

This paper cites Embodichain: An end-to-end, gpu-accelerated, and modular platform for building generalized embodied intelligence., November 2025.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Embodichain: An end-to-end, gpu-accelerated, and modular platform for building generalized embodied intelligence., November 2025

Reference 25

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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=2026-08-16T00:35:57.348579Z digest=sha256:c9127c57a37c51f03e773018ed225c9e5da8d61ba10a86568a2987f050769d2f

Observation 65d9a7a1-2d0e-440d-b8a3-ec3b36e34e7d · outbound

This paper cites World Simulation with Video Foundation Models for Physical AI.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills World Simulation with Video Foundation Models for Physical AI

Reference 26

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source=pdf_text observed=2026-08-16T00:35:57.353172Z digest=sha256:041fa0688de3d5823d531184e8bc530ed148011551c975492907fee32b209b9a

Observation 9b0aa8d7-68b5-477b-a805-b8dc70b15696 · outbound

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

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Wan: Open and Advanced Large-Scale Video Generative Models

Reference 27

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source=pdf_text observed=2026-08-16T00:35:57.358134Z digest=sha256:1d45e0bfd27bcd531101c26384db1f7c755fffabd241e838dc3af94f0a620e27

Observation aa66d629-a29b-44e5-8ffc-fda5112cf5a1 · outbound

This paper cites DemoGen: Synthetic Demonstration Generation for Data-Efficient Visuomotor Policy Learning.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills DemoGen: Synthetic Demonstration Generation for Data-Efficient Visuomotor Policy Learning

Reference 28

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source=pdf_text observed=2026-08-16T00:35:57.362695Z digest=sha256:b299768aa9ab7d9559babc16c88945da64383b07c330d1500bd523155525afdb

Observation abe9be4f-4575-4e45-afe6-c213e06d37f3 · outbound

This paper cites Benchmarking Generalizable Bimanual Manipulation: RoboTwin Dual-Arm Collaboration Challenge at CVPR 2025 MEIS Workshop.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Benchmarking Generalizable Bimanual Manipulation: RoboTwin Dual-Arm Collaboration Challenge at CVPR 2025 MEIS Workshop

Reference 29

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source=pdf_text observed=2026-08-16T00:35:57.367949Z digest=sha256:199ed7078fc3827f64658e6505fc8a1fe6821a0054aade2fc28947dae26c1350

Observation 541eb9bb-487a-49f8-b95a-66665d01e6e1 · outbound

This paper cites Cyclemanip: Enabling cyclic task manipulation via effective historical perception and understanding.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Cyclemanip: Enabling cyclic task manipulation via effective historical perception and understanding

Reference 30

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source=pdf_text observed=2026-08-16T00:35:57.373007Z digest=sha256:1936264b4b8ce00e14eb808438e78ff44e9bf9e34276be29ad92272b84eae5fa

Observation 9cdf0796-be7f-46bb-818d-79254e807558 · outbound

This paper cites Eva: Aligning video world models with executable robot actions via inverse dynamics rewards.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Eva: Aligning video world models with executable robot actions via inverse dynamics rewards

Reference 31

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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=2026-08-16T00:35:57.377525Z digest=sha256:9fde272f09a9ae205489cd3183ffdee374aba9891413dfbb3cd9b5609fe14b06

Observation 79ff6a69-bcc8-44c1-bb2c-6ba0e94a7d8f · outbound

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

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 32

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source=pdf_text observed=2026-08-16T00:35:57.382200Z digest=sha256:7bae271d4afc94538ff8ba32a6a46c85a72f939c597b7e851a7ace57063cc890

Observation 5caee10a-a8e0-4891-92b9-eef332ffb8a9 · outbound

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

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization

Reference 33

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source=pdf_text observed=2026-08-16T00:35:57.387136Z digest=sha256:083dd7e9b6879dbb153a3cd864bdf5232da9f84bf370b4de52b72d905c3d5974

Observation 4d034616-8e1a-4bc1-b874-6884aa1a2d21 · outbound

This paper cites RDT-1b: a diffusion foundation model for bimanual manipulation.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills RDT-1b: a diffusion foundation model for bimanual manipulation

Reference 34

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raw_fallback, observed 2026-08-16T00:35:58.043051Z

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=2026-08-16T00:35:57.392077Z digest=sha256:2f276533fbb33dceddf182c7f28263b901edd79f222aa129310eb193eeb8fc80

Observation 5287ce8e-f5cd-4ebe-bc9a-f53fe8a5a451 · outbound

This paper cites an unresolved cited work.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Unresolved cited work

Reference 35

Resolution
unresolved
raw_fallback, observed 2026-08-16T00:35:58.026028Z

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=2026-08-16T00:35:57.396488Z digest=sha256:a66160aee0a813def7acde55c4fa4bc1e4e0c3d9a441c82d861abcda162d8e64

Observation d6b602fb-c95c-4332-a186-2d8800bc6cdd · outbound

This paper cites an unresolved cited work.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Unresolved cited work

Reference 36

Resolution
unresolved
raw_fallback, observed 2026-08-16T00:35:58.010440Z

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=2026-08-16T00:35:57.400975Z digest=sha256:1faaf81e5f59abd5c0a8fd9268a9327b15a78361ef51c67f0922d47b4af73cfb

Observation 388feaac-1b43-4427-b017-ec12b7d0f274 · outbound

This paper cites an unresolved cited work.

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills Unresolved cited work

Reference 37

Resolution
unresolved
raw_fallback, observed 2026-08-16T00:35:57.994090Z

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=2026-08-16T00:35:57.405912Z digest=sha256:9f5702c96f9df1c6de1b1934924b8989cd70e8879dfe540c97ed800ba1fc00ec

Pith citing papers

No inbound Pith citation observations are available.