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Paper Citation Record · LEDGER

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

As of 20 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 2026-08-20T06:33:59.587034+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

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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:35:57.249144Z digest=sha256:692568913ec904ad6fff2e5b4cf25c4d91251dab19fcc084f2ceb9013f51bc1e

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=pdf_text observed=2026-08-16T00:35:57.254175Z digest=sha256:11ee69ca52bc7c234706661505515872285347f275c32ee81f8a55bcb7469110

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:cebd603a49339dc555c6a699400034e3ed2e4c73c50057add6cdee6eeac28dd1

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=sha256:9e0d834f7a27ad98ad8c9ff2750a82fde28615d951cb2af9282d37a44b0de842

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=sha256:155eb013e16a795e31899a0f0a59cd0c2f69711c2108ec898455095f5c296e14

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:f66ddadf5339d85ed892d283995fbe7146ac4576e6405e1ee38a3112759434d6

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:35:57.279105Z digest=sha256:311f05368916bc3d057768447825f05813f7e691c912846b7b4855437c8c60a0

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:e611ab20bbfccd401fdf137856acac654eaf592b88fc3ede11710b467a0523a9

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

source=pdf_text observed=2026-08-16T00:35:57.288410Z digest=sha256:63459296df604c68b85a87d3dc04ae387b630c05933a133e492d1ee2c88b00f1

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=sha256:4b50ec0b8bb35c51df815e62dab2574e0120a5eb5d231a1bbca73292efb76a43

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=sha256:273777646cc26b3d388b4646d5603b4944e784eed59984de43ea5377104f9505

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:b9b0a4e95c32c7470d41076c6a179110b6f51dcd88f609cfea70f3b9efbdd5b4

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:35:57.307743Z digest=sha256:23ad136f3d89857a2beefd71191ea0c365322e0953c218f8ea2fa47749f46223

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=sha256:996d20042057c534baf80e3c1af4d672569a2eb4acb3f566a04f56b6f5298ff3

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=sha256:4cdb1e32fc38692e55fc90cc97ff5e10a1753736e2a8d63af0f7300773ca97a4

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:35:57.320836Z digest=sha256:1b9f8a7a1e76b95b9b6d75a78690fe5b9761ff528e54b14f760568066999b330

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=sha256:9a64dc18628cae68e0e71506c257adf862fbeec58fd1f4985aca9e9cdbb3005b

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:35:57.330024Z digest=sha256:690a0ec98f3f5ff0d654ad1d876a600be7391d5a4474a0419343318be0d412cd

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

source=pdf_text observed=2026-08-16T00:35:57.334551Z digest=sha256:9ef184fcb91f0f1b75dad8b454114e3af8d0c13eee4a1d46eee3e6e9e33d9109

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=sha256:9071e8ba96ae3d62a2949909d976c44497cbc3f55ecd3de4790c569f0eae2a6b

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:e452b7c63f641eb7427567745ae29e769203c96edb1d52478ecffa7f2ced07cd

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:35:57.348579Z digest=sha256:9d7c0206bec431b1f0350094b5492f8a8cef77ac5c29d2ce03003cb26b7908ce

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=sha256:2c9cabce3c695613feaab56a2bac0cebdb2ffc740d5a026cf16d1addcb39867b

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=sha256:0b3799f463fac44c4c443c71d56285990037bf3077d9d702f5553354c57cd30f

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=sha256:9b25b06d28ca8aa94c0a6756826a7234fa7db12534f0fe2ff011848bbcc1bda3

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=sha256:8674628a8c28199cd731a7f1c9a0101ea9d32fe3d2ad7a89086b05d3caad7e97

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=sha256:75c4833ee7d836dff0377ee3e34267bda266d53f533a75155af5bb3c166bfe33

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:35:57.377525Z digest=sha256:5e0a8801b241042659b81fa9d4883550bafd8de9dbf62347499b179b1a3d8197

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:ada829e2c6ca8149958e27674fa7b4fcd85da003d1b97224f211fefe4396551d

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=sha256:57d48413a4227aa3be776b77c7013959686bd45bf05a1478b1292f3b696776bd

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:35:57.392077Z digest=sha256:2b8301b2ab1f04154a2e5c59434104a364dbad653dd2e9b0f9bbb0534081a2f5

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:35:57.396488Z digest=sha256:eb64ba7300d337ea6efe0527256759c1a916dd7dda968d4beabe67b74b3b94b3

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:35:57.400975Z digest=sha256:884f30892fa3f8e0ccbef2e2df1906db3124b3c1814d2fb131b6f656ec6df887

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:35:57.405912Z digest=sha256:c2ee07656e811fb5320870b9114f4eaee969c4f54ff9fee51bbe59249b7cc6f8

Pith citing papers

No inbound Pith citation observations are available.