Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.19065.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:47:14.331330Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T21:07:12.566544Z
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 bf669141-44b7-4e7e-ab00-e559ef889780 · inbound
USTBench: Benchmarking and Dissecting Spatiotemporal Reasoning of LLMs as Urban Agents cites this paper.
USTBench: Benchmarking and Dissecting Spatiotemporal Reasoning of LLMs as Urban Agents STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:47:14.331330Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d06ca945-cfb5-4e9a-840f-bcd0d4f1e2b8 · inbound
Can LLMs Learn to Map the World from Local Descriptions? cites this paper.
Can LLMs Learn to Map the World from Local Descriptions? STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:52:57.697883Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eeca5cda-ba16-4d8f-a648-34cd90fe1baf · inbound
Domain Specific Benchmarks for Evaluating Multimodal Large Language Models cites this paper.
Domain Specific Benchmarks for Evaluating Multimodal Large Language Models STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:39:41.742261Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 95649d76-a71e-4434-b0fc-10ffd2a0598b · inbound
A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs cites this paper.
A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:01:34.724997Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 63808a64-f969-4940-9735-7687f952633b · inbound
Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications cites this paper.
Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis
Reference 88
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T21:07:12.570539Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.