Paper Citation Record · LEDGER

STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2406.19065.

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

pith.paper-citation-record.v1
2406.19065 v1

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 2026-08-20T06:33:59.587034+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T05:03:11.925122Z

measured 0 of 1 external citation measurements

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 2026-08-20T06:33:59.587034+00:00.

Observation d3a9abd7-488d-4076-a13c-b9e7f241dc99 · inbound

GeoBenchLLM: A Comprehensive Benchmark for Evaluating LLMs on Geo-Related Tasks cites this paper.

GeoBenchLLM: A Comprehensive Benchmark for Evaluating LLMs on Geo-Related Tasks STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T05:03:11.925122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.