Paper Citation Record · LEDGER

Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks

As of 9 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 5 inbound Pith citation observations for arXiv:2502.00633.

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

pith.paper-citation-record.v1
2502.00633 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T18:23:05.741808Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T23:22:50.927678Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T22:13:46.752750Z

Reference resolution

16 of 16 outbound references displayed

verified exact4
verified fuzzy4
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8da2b906-4ba1-4fe9-8f77-9ff51183c971 · outbound

This paper cites A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity.

Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T18:23:05.702292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fb2c679-de10-41c4-9c7a-6e10ea80d260 · outbound

This paper cites Collaborative AI Teaming in Unknown Environments via Active Goal Deduction.

Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks Collaborative AI Teaming in Unknown Environments via Active Goal Deduction

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-09T18:23:05.928845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f429ba82-3a66-4a4d-b69f-ed27e211e732 · outbound

This paper cites Br- defedrl: Byzantine-robust decentralized federated reinf orcement learning with fast convergence and com- munication efﬁciency.

Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks Br- defedrl: Byzantine-robust decentralized federated reinf orcement learning with fast convergence and com- munication efﬁciency

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:23:06.063330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 195f9ae5-2f35-477a-8eb4-3384a90ac35d · outbound

This paper cites Cooperative Backdoor Attack in Decentralized Reinforcement Learning with Theoretical Guarantee.

Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks Cooperative Backdoor Attack in Decentralized Reinforcement Learning with Theoretical Guarantee

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T18:23:05.726981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e9ea51f-33ba-4d93-ba39-021685dbfabf · outbound

This paper cites Mastering nim and impartial games with weak neur al networks: An alphazero-inspired multi-frame approach.

Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks Mastering nim and impartial games with weak neur al networks: An alphazero-inspired multi-frame approach

Reference 14

Resolution: verified exact; raw_fallback, observed 2026-08-09T18:23:05.890881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a40d4423-447b-4f96-aa5e-7e9087b825ad · outbound

This paper cites Network Diffuser for Placing-Scheduling Service Function Chains with Inverse Demonstration.

Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks Network Diffuser for Placing-Scheduling Service Function Chains with Inverse Demonstration

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-09T18:23:05.787046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3df758e2-7871-4b2b-8ce7-e819f7d50f10 · outbound

This paper cites Lipschitz Lifelong Reinforcement Learning.

Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks Lipschitz Lifelong Reinforcement Learning

Reference 2003

Resolution: verified exact; local_arxiv, observed 2026-08-09T18:23:05.949780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 53b0ebd4-a1fe-43d1-96ae-663e30905e37 · outbound

This paper cites Speculat ive monte-carlo tree search.

Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks Speculat ive monte-carlo tree search

Reference 2006

Resolution: unresolved; no resolver link, observed 2026-08-09T18:23:05.697513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43e0fa1b-1f72-46c4-8d95-067571745a97 · outbound

This paper cites Empirical analysis of puct algorithm w ith evaluation functions of different quality.

Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks Empirical analysis of puct algorithm w ith evaluation functions of different quality

Reference 2013

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:23:06.105639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e6bc3594-ea50-41aa-b4b8-b1c5686cd307 · outbound

This paper cites Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm.

Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-09T18:23:05.665783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c47ef75b-c920-4acd-a533-f60d55c811e3 · outbound

This paper cites Efficient Multi-agent Reinforcement Learning by Planning.

Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks Efficient Multi-agent Reinforcement Learning by Planning

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-09T18:23:05.671582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98dcfb02-6133-4b33-a350-1cc45fba3fba · outbound

This paper cites Lifelong learni ng of structure in the space of policies.

Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks Lifelong learni ng of structure in the space of policies

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:23:06.078571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5b4d6e31-635b-4ef4-ac79-a40e374d28dc · outbound

This paper cites Reset-Free Lifelong Learning with Skill-Space Planning.

Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks Reset-Free Lifelong Learning with Skill-Space Planning

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-09T18:23:05.682169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01777381-bb5f-4ab7-9377-63d87e7f7ef7 · outbound

This paper cites Continu ous upper conﬁdence trees with polynomial exploration–consistency.

Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks Continu ous upper conﬁdence trees with polynomial exploration–consistency

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:23:06.122204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b260221f-5d19-4bd3-b952-616ea814b5e6 · outbound

This paper cites Deep Reinforcement Learning amidst Lifelong Non-Stationarity.

Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks Deep Reinforcement Learning amidst Lifelong Non-Stationarity

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T18:23:05.676890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2992528-6057-4ec0-b4d5-27ef024a450f · outbound

This paper cites an unresolved cited work.

Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks Unresolved cited work

Reference 2025

Resolution: unresolved; raw_fallback, observed 2026-08-09T18:23:06.047805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation 91a38b12-e650-4864-b704-c54bf43fb3be · inbound

3D DNA Origami-Enabled Molecularly Addressable Optical Nanocircuit cites this paper.

3D DNA Origami-Enabled Molecularly Addressable Optical Nanocircuit Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T23:22:50.927678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eda2cdf4-cfb6-4734-9f14-2db650e75ce8 · inbound

Tail-Risk-Safe Monte Carlo Tree Search under PAC-Level Guarantees cites this paper.

Tail-Risk-Safe Monte Carlo Tree Search under PAC-Level Guarantees Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T23:22:29.231783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f116d33-cd68-4989-85d5-71d89ab5d3a1 · inbound

MINT: Minimal Information Neuro-Symbolic Tree for Objective-Driven Knowledge-Gap Reasoning and Active Elicitation cites this paper.

MINT: Minimal Information Neuro-Symbolic Tree for Objective-Driven Knowledge-Gap Reasoning and Active Elicitation Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-16T07:17:30.666906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 57806f1d-b1e3-42ab-98bf-2c1f1b1ba908 · inbound

Operator-Guided Invariance Learning for Continuous Reinforcement Learning cites this paper.

Operator-Guided Invariance Learning for Continuous Reinforcement Learning Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:06:08.767806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cf33e756-3e32-44b9-aa43-1f5a4befbeda · inbound

Metric-Gradient Projection for Stable Multi-Agent Policy Learning cites this paper.

Metric-Gradient Projection for Stable Multi-Agent Policy Learning Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:13:46.755847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.