Paper Citation Record · LEDGER

Regret-Free Reinforcement Learning for LTL Specifications

As of 21 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2411.12019.

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

pith.paper-citation-record.v1
2411.12019 v2

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:11:25.886360Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

25 of 25 outbound references displayed

verified exact3
verified fuzzy14
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4cad7e06-13a3-4ace-b18c-94f27668089c · outbound

This paper cites Taming the monster: A fast and simple algorithm for contextual bandits.

Regret-Free Reinforcement Learning for LTL Specifications Taming the monster: A fast and simple algorithm for contextual bandits

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:11:26.533555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation da52408f-d55b-4d75-9d60-afb0ef2589e6 · outbound

This paper cites A framework for transforming specifications in reinforcement learning.

Regret-Free Reinforcement Learning for LTL Specifications A framework for transforming specifications in reinforcement learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:11:26.517076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e899ab06-7803-4a8a-a303-72ab7be227ac · outbound

This paper cites and Ortner, R.

Regret-Free Reinforcement Learning for LTL Specifications and Ortner, R

Reference 3

Resolution: verified exact; doi, observed 2026-08-12T18:11:25.952024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fb5b4e90-dddf-4011-bd73-d4a6084438ae · outbound

This paper cites Near-optimal regret bounds for reinforcement learning.

Regret-Free Reinforcement Learning for LTL Specifications Near-optimal regret bounds for reinforcement learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T18:11:25.777975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63c5d959-556a-47f2-a1cd-5b55709f6a8a · outbound

This paper cites and Katoen, J.-P.

Regret-Free Reinforcement Learning for LTL Specifications and Katoen, J.-P

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:11:26.491168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4de79363-ee82-4854-87d8-b782ef3112fa · outbound

This paper cites K., Wang, Y., Zavlanos, M.

Regret-Free Reinforcement Learning for LTL Specifications K., Wang, Y., Zavlanos, M

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T18:11:25.789695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6139fdcf-c5c9-4ce8-a059-02feb2e107b1 · outbound

This paper cites Reinforcement Learning Based Temporal Logic Control with Maximum Probabilistic Satisfaction.

Regret-Free Reinforcement Learning for LTL Specifications Reinforcement Learning Based Temporal Logic Control with Maximum Probabilistic Satisfaction

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-12T18:11:26.205122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b05bbb49-67ca-4002-b59b-c6e1f334fc9b · outbound

This paper cites T., Klassen, T.

Regret-Free Reinforcement Learning for LTL Specifications T., Klassen, T

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:11:26.476306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6d944c74-5ed1-4e35-8d2e-3e5569344d25 · outbound

This paper cites Unifying pac and regret: uniform pac bounds for episodic reinforcement learning.

Regret-Free Reinforcement Learning for LTL Specifications Unifying pac and regret: uniform pac bounds for episodic reinforcement learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:11:26.460622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1155769f-eb4c-4f58-a495-5a424e48f6bd · outbound

This paper cites Near optimal exploration-exploitation in non-communicating markov decision processes.

Regret-Free Reinforcement Learning for LTL Specifications Near optimal exploration-exploitation in non-communicating markov decision processes

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:11:26.445302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6aa1f8f0-d39b-4713-939c-249189c38b2d · outbound

This paper cites and Topcu, U.

Regret-Free Reinforcement Learning for LTL Specifications and Topcu, U

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:11:26.429299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 42252b34-5c24-49e8-b1dd-3bfb3883f655 · outbound

This paper cites M., Perez, M., Schewe, S., Somenzi, F., Trivedi, A., and Wojtczak, D.

Regret-Free Reinforcement Learning for LTL Specifications M., Perez, M., Schewe, S., Somenzi, F., Trivedi, A., and Wojtczak, D

Reference 12

Resolution: verified exact; doi, observed 2026-08-12T18:11:26.413468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bebd1680-326a-4ff1-a77e-af272783b83f · outbound

This paper cites J., and Lee, I.

Regret-Free Reinforcement Learning for LTL Specifications J., and Lee, I

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T18:11:25.825480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efc71101-f444-4b4f-9003-c4f5cf53e9ef · outbound

This paper cites T., Klassen, T.

Regret-Free Reinforcement Learning for LTL Specifications T., Klassen, T

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:11:26.397995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cadc9e27-df50-484e-ad2e-41353dbe1603 · outbound

This paper cites Translating omega-regular specifications to average objectives for model-free reinforcement learning.

Regret-Free Reinforcement Learning for LTL Specifications Translating omega-regular specifications to average objectives for model-free reinforcement learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:11:26.382115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6670a22c-6667-4438-a6b8-2457e1178519 · outbound

This paper cites and Singh, S.

Regret-Free Reinforcement Learning for LTL Specifications and Singh, S

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:11:26.364212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 57902834-09b8-49a1-8d3d-e1cee0d0eb12 · outbound

This paper cites and Ramaswami, V.

Regret-Free Reinforcement Learning for LTL Specifications and Ramaswami, V

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T18:11:25.846083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95d9b663-84ad-4d35-a472-163b96dfbd24 · outbound

This paper cites Reinforcement learning of control policy for linear temporal logic specifications using limit-deterministic generalized büchi automata.

Regret-Free Reinforcement Learning for LTL Specifications Reinforcement learning of control policy for linear temporal logic specifications using limit-deterministic generalized büchi automata

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T18:11:25.851437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 733ff85b-7484-427e-b19e-699b21e122d5 · outbound

This paper cites A PAC Learning Algorithm for LTL and Omega-regular Objectives in MDPs.

Regret-Free Reinforcement Learning for LTL Specifications A PAC Learning Algorithm for LTL and Omega-regular Objectives in MDPs

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T18:11:25.856184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d10f045-77f6-4e8d-82cb-bb1f7f8555ba · outbound

This paper cites Near-optimal regret bounds for stochastic shortest path.

Regret-Free Reinforcement Learning for LTL Specifications Near-optimal regret bounds for stochastic shortest path

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:11:26.348014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2d4dec8b-8d72-4ec2-8884-5879b854497f · outbound

This paper cites Limit-deterministic b \"u chi automata for linear temporal logic.

Regret-Free Reinforcement Learning for LTL Specifications Limit-deterministic b \"u chi automata for linear temporal logic

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:11:26.330019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4f887869-2688-4c89-ad55-3d5cef78800c · outbound

This paper cites M., and Seeger, M.

Regret-Free Reinforcement Learning for LTL Specifications M., and Seeger, M

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T18:11:25.871468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7522be17-1a13-4a62-8c95-2b26d4956095 · outbound

This paper cites No-regret exploration in goal-oriented reinforcement learning.

Regret-Free Reinforcement Learning for LTL Specifications No-regret exploration in goal-oriented reinforcement learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:11:26.311916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f883008f-9eef-47d2-9a33-b409923e5db3 · outbound

This paper cites M., Chaudhuri, S., and Yue, Y.

Regret-Free Reinforcement Learning for LTL Specifications M., Chaudhuri, S., and Yue, Y

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:11:26.295449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6bdf0035-79a6-41a9-bf40-e429d7676c33 · outbound

This paper cites On the (In)Tractability of Reinforcement Learning for LTL Objectives.

Regret-Free Reinforcement Learning for LTL Specifications On the (In)Tractability of Reinforcement Learning for LTL Objectives

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T18:11:25.886360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.