Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
An Integrated Framework Integrating Monte Carlo Tree Search and Supervised Learning for Train Timetabling Problem
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2311.00971.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T14:08:35.518757Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T12:12:35.911311Z
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 db24e8c5-6749-44ff-b25f-503f5d9b7c69 · inbound
MC-NEST: Enhancing Mathematical Reasoning in Large Language Models leveraging a Monte Carlo Self-Refine Tree cites this paper.
MC-NEST: Enhancing Mathematical Reasoning in Large Language Models leveraging a Monte Carlo Self-Refine Tree An Integrated Framework Integrating Monte Carlo Tree Search and Supervised Learning for Train Timetabling Problem
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T14:08:35.518757Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1db25a7c-6ab2-41a6-97c8-a16b66e46da6 · inbound
Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search cites this paper.
Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search An Integrated Framework Integrating Monte Carlo Tree Search and Supervised Learning for Train Timetabling Problem
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T04:53:11.859993Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a671a3a2-4ac4-4417-8b0c-a96e2d689aec · inbound
DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search cites this paper.
DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search An Integrated Framework Integrating Monte Carlo Tree Search and Supervised Learning for Train Timetabling Problem
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T12:12:35.913902Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.