Paper Citation Record · LEDGER

Learning of Robot Safety Policies via Adversarial Synthetic Scenarios

As of 10 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2606.05952.

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

pith.paper-citation-record.v1
2606.05952 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T01:12:25.956015Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

13 of 13 outbound references displayed

verified exact6
verified fuzzy0
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 55b3317c-8723-4a88-8408-8a82c5b6ddaa · outbound

This paper cites InCoRo: In-Context Learning for Robotics Control with Feedback Loops.

Learning of Robot Safety Policies via Adversarial Synthetic Scenarios InCoRo: In-Context Learning for Robotics Control with Feedback Loops

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T13:36:58.981880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0b8c0635-a346-4e03-9e2e-a4a5c04bfc65 · outbound

This paper cites Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics.

Learning of Robot Safety Policies via Adversarial Synthetic Scenarios Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-02T13:36:58.968726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8c499ce6-285a-4600-a69c-c5f292701e37 · outbound

This paper cites RoboMorph: In-Context Meta-Learning for Robot Dynamics Modeling.

Learning of Robot Safety Policies via Adversarial Synthetic Scenarios RoboMorph: In-Context Meta-Learning for Robot Dynamics Modeling

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-02T13:36:58.979309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a0456aac-093d-4326-8fda-2e9383c67b87 · outbound

This paper cites Inclet: Large language model in-context learning can improve embodied instruction-following,.

Learning of Robot Safety Policies via Adversarial Synthetic Scenarios Inclet: Large language model in-context learning can improve embodied instruction-following,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-28T01:12:25.956015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46752ed2-8080-476f-9419-73bd2bdba9ab · outbound

This paper cites MimicDroid: In-Context Learning for Humanoid Robot Manipulation from Human Play Videos.

Learning of Robot Safety Policies via Adversarial Synthetic Scenarios MimicDroid: In-Context Learning for Humanoid Robot Manipulation from Human Play Videos

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-02T13:36:58.971265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 400f5365-4a56-4880-8452-0ec55c08f22a · outbound

This paper cites Plug in the safety chip: Enforcing constraints for llm-driven robot agents,.

Learning of Robot Safety Policies via Adversarial Synthetic Scenarios Plug in the safety chip: Enforcing constraints for llm-driven robot agents,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-28T01:12:25.956015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa419390-cc15-4196-bb46-98e4f995dbca · outbound

This paper cites SafeEmbodAI: a Safety Framework for Mobile Robots in Embodied AI Systems.

Learning of Robot Safety Policies via Adversarial Synthetic Scenarios SafeEmbodAI: a Safety Framework for Mobile Robots in Embodied AI Systems

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-02T13:36:58.977127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8906e8a8-5288-42c9-9c3c-b557970d6e0d · outbound

This paper cites Longsafety: Evaluating long-context safety of large language models,.

Learning of Robot Safety Policies via Adversarial Synthetic Scenarios Longsafety: Evaluating long-context safety of large language models,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-28T01:12:25.956015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33c00085-e84e-4a2e-8982-88cfe9bd668c · outbound

This paper cites Selp: Generating safe and efficient task plans for robot agents with large language models,.

Learning of Robot Safety Policies via Adversarial Synthetic Scenarios Selp: Generating safe and efficient task plans for robot agents with large language models,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-28T01:12:25.956015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6740a321-509c-402d-856a-07d4d7a43a88 · outbound

This paper cites Safe In-Context Reinforcement Learning.

Learning of Robot Safety Policies via Adversarial Synthetic Scenarios Safe In-Context Reinforcement Learning

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-07-02T13:36:58.965988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ea051e49-9272-4a3e-adb6-ce4d0feb7bf7 · outbound

This paper cites Safe learning for contact-rich robot tasks: A survey from classical learning-based methods to safe foundation models.

Learning of Robot Safety Policies via Adversarial Synthetic Scenarios Safe learning for contact-rich robot tasks: A survey from classical learning-based methods to safe foundation models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-02T13:36:58.976774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 42255de0-9350-484d-8e0e-5c5f68413e39 · outbound

This paper cites Cyberbotics ltd. webots™: professional mobile robot simu- lation,.

Learning of Robot Safety Policies via Adversarial Synthetic Scenarios Cyberbotics ltd. webots™: professional mobile robot simu- lation,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-28T01:12:25.956015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9e33b43-e842-4af3-a2f7-b2ed2128ebff · outbound

This paper cites How to pick a mobile robot simulator: A quantitative comparison of coppeliasim, gazebo, morse and webots with a focus on accuracy of motion,.

Learning of Robot Safety Policies via Adversarial Synthetic Scenarios How to pick a mobile robot simulator: A quantitative comparison of coppeliasim, gazebo, morse and webots with a focus on accuracy of motion,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-28T01:12:25.956015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.