Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2410.02062.
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 2026-08-07T15:26:54.004758Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T22:03:36.207727Z
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 1b4e6695-c23a-44b9-9c3b-27339f2fa43a · inbound
Large Language models for Time Series Analysis: Techniques, Applications, and Challenges cites this paper.
Large Language models for Time Series Analysis: Techniques, Applications, and Challenges TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:26:54.004758Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2274a2f9-4da2-4e91-9488-37bf6ffbad7b · inbound
CRMAgent: A Multi-Agent LLM System for E-Commerce CRM Message Template Generation cites this paper.
CRMAgent: A Multi-Agent LLM System for E-Commerce CRM Message Template Generation TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:26:50.759871Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 230a24f7-58ae-45a0-91f2-c21501df2e00 · inbound
Temporal Tokenization Strategies for Event Sequence Modeling with Large Language Models cites this paper.
Temporal Tokenization Strategies for Event Sequence Modeling with Large Language Models TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T22:03:36.209768Z
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.
Observation 63ea7706-cde6-4320-ba2e-c52fea5340d3 · inbound
MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling cites this paper.
MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models
Reference 51
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-14T20:19:27.813695Z
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.