Pith. sign in

Paper Citation Record · LEDGER

Safe Reinforcement Learning via Probabilistic Shields

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1807.06096.

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

pith.paper-citation-record.v1
1807.06096 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T00:04:49.321403Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T21:57:25.898616Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d310cfac-573c-4a6c-a07a-38206a96ac6d · inbound

Model-Free RL Agents Demonstrate System 1-Like Intentionality cites this paper.

Model-Free RL Agents Demonstrate System 1-Like Intentionality Safe Reinforcement Learning via Probabilistic Shields

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-10T00:04:49.321403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T00:04:49.321403Z digest=sha256:a579ebb7151185744f2d580cc84c81519dccc885fc68105fafdb69d55de8c0d8

Observation 8d4f9a6f-2ed7-4e04-b29a-00e7d28abeee · inbound

Efficient Dynamic Shielding for Parametric Safety Specifications cites this paper.

Efficient Dynamic Shielding for Parametric Safety Specifications Safe Reinforcement Learning via Probabilistic Shields

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T13:23:50.468620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:23:50.468620Z digest=sha256:835c1414cb601c75c0495d34342da59223e7934e50a0425dafe70889a4f05751

Observation 7657fcd5-7f59-409f-852e-b92157779f95 · inbound

Conformal Safety Shielding for Imperfect-Perception Agents cites this paper.

Conformal Safety Shielding for Imperfect-Perception Agents Safe Reinforcement Learning via Probabilistic Shields

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T04:19:36.788398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:19:36.788398Z digest=sha256:573688ef8c3f1673960e6c11213c32a931cfc8a35ca1e2e20b7763b57e55bb7e

Observation 56dcd510-2b8c-48c6-a0f8-b82a6dfca552 · inbound

What if Pinocchio Were a Reinforcement Learning Agent: A Normative End-to-End Pipeline cites this paper.

What if Pinocchio Were a Reinforcement Learning Agent: A Normative End-to-End Pipeline Safe Reinforcement Learning via Probabilistic Shields

Reference 98

Resolution
verified exact
arxiv_id, observed 2026-05-15T10:05:26.323581Z

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.

source=pdf_text observed=2026-05-15T10:01:38.533566Z digest=sha256:179fe5c783e33a3224ccb3bca9cc204dc6e34f12d2c32d833c2b035d7c300191

Observation 4c4814fe-15a9-49ac-9cac-834ab135343e · inbound

Neuro-Symbolic Injection of LTLf Constraints in Autoregressive Reinforcement Learning Policies cites this paper.

Neuro-Symbolic Injection of LTLf Constraints in Autoregressive Reinforcement Learning Policies Safe Reinforcement Learning via Probabilistic Shields

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-07-02T21:57:25.900276Z

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.

source=pdf_text observed=2026-06-27T19:23:32.966503Z digest=sha256:f093fdeca7dd46dbacadebf732fab9dfa45ed31078e3a0a4ae5cd6a4a6996c72

Observation 8bde590d-0565-4250-bd3c-16d612285405 · inbound

Certified Speculative Execution for Untrusted AI Agents cites this paper.

Certified Speculative Execution for Untrusted AI Agents Safe Reinforcement Learning via Probabilistic Shields

Reference 15

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T10:05:40.483737Z

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.

source=arxiv_source observed=2026-07-01T05:56:45.544138Z digest=sha256:ce7f275c57cc9e7121e8b640545bf69a66838408aea079e3f09a4c930cf2b5fc