Paper Citation Record · LEDGER

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale

As of 5 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2604.21889.

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

pith.paper-citation-record.v1
2604.21889 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-09T21:46:02.002132Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

23 of 23 outbound references displayed

verified exact16
verified fuzzy2
unresolved0
parse uncertain0
malformed identifier1
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 03f5c014-97ff-4d8b-8f0f-060ecc612e79 · outbound

This paper cites 2025 , url =.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale 2025 , url =

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:35:41.857124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 713274b3-16bd-434c-9bd1-73e20e1a38d8 · outbound

This paper cites 2025 , url =.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale 2025 , url =

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:35:41.860688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a1b09305-b670-4a23-83a5-fe4ef2122ab5 · outbound

This paper cites Density-Based Clustering over an Evolving Data Stream with Noise , booktitle =.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale Density-Based Clustering over an Evolving Data Stream with Noise , booktitle =

Reference 3

Resolution: verified exact; doi, observed 2026-05-09T21:48:36.579856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 49db730f-0e44-4cfe-9783-279d442f223b · outbound

This paper cites Aggarwal, Philip S.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale Aggarwal, Philip S

Reference 4

Resolution: verified exact; doi, observed 2026-05-09T21:48:36.563585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3ae6fc7f-fc82-4df1-8b4c-6270f1800c61 · outbound

This paper cites Available: https://doi.org/10.1145/3292500.3330680.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale Available: https://doi.org/10.1145/3292500.3330680

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T21:48:36.587589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 17c08b84-5f89-45b5-bbb6-fc798129d0e3 · outbound

This paper cites Embed2Detect: temporally clustered embedded words for event detection in social media , journal =.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale Embed2Detect: temporally clustered embedded words for event detection in social media , journal =

Reference 6

Resolution: verified exact; doi, observed 2026-05-09T21:48:36.570995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e0d0dfeb-3043-4134-939b-c7952f88abd5 · outbound

This paper cites McKeown , editor =.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale McKeown , editor =

Reference 7

Resolution: verified exact; doi, observed 2026-05-09T21:48:36.551026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation eb8a72c7-3b15-4f2c-8651-53b6a5d58501 · outbound

This paper cites Lost in the Middle: How Language Models Use Long Contexts.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale Lost in the Middle: How Language Models Use Long Contexts

Reference 8

Resolution: malformed identifier; doi, observed 2026-05-09T21:48:36.568490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e506d055-b85d-44c1-905c-46e426892679 · outbound

This paper cites Matos-Carvalho and Nuno Fachada , keywords =.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale Matos-Carvalho and Nuno Fachada , keywords =

Reference 9

Resolution: verified exact; doi, observed 2026-05-09T21:48:36.577240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2f5af798-7605-4051-a65a-a616ce1a211a · outbound

This paper cites Proceedings of the 2025 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region , pages =.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale Proceedings of the 2025 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region , pages =

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-09T21:48:36.574773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f311f3a3-cccd-4b2e-80d7-a3c507387065 · outbound

This paper cites Context-Aware Clustering using Large Language Models.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale Context-Aware Clustering using Large Language Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-09T21:48:36.561116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8b23d43b-bcf4-4574-ab7c-d130987a66ab · outbound

This paper cites CoRR , volume =.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale CoRR , volume =

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-09T21:48:36.583444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a64a68fb-5794-43f0-a859-d353b151b6b2 · outbound

This paper cites Ghorbani.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale Ghorbani

Reference 13

Resolution: verified exact; doi, observed 2026-05-09T21:48:36.590039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 96d0d6e4-7376-4bb7-9543-ab0de658b01e · outbound

This paper cites Rokne , editor =.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale Rokne , editor =

Reference 14

Resolution: verified exact; doi, observed 2026-05-09T21:48:36.548959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d79ba294-4a8e-4772-80e2-84dd4a8edbdd · outbound

This paper cites Available: https://doi.org/10.1145/3292500.3330680.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale Available: https://doi.org/10.1145/3292500.3330680

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T21:48:36.557286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7f299596-6631-44ce-871e-6c8a015c4365 · outbound

This paper cites title =.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale title =

Reference 16

Resolution: verified exact; doi, observed 2026-05-09T21:48:36.565784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1ab32ca6-0fce-4769-a6cd-4e593e810bbf · outbound

This paper cites Dense Passage Retrieval for Open-Domain Question Answering.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale Dense Passage Retrieval for Open-Domain Question Answering

Reference 17

Resolution: verified exact; doi, observed 2026-05-09T21:48:36.553361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0de85334-c982-46f8-8b7d-41d887b28ce6 · outbound

This paper cites Proceedings of the 23rd.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale Proceedings of the 23rd

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-09T21:48:36.599969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a2e2c3c8-3819-4620-bb27-bea4987c2633 · outbound

This paper cites Hybrid AI for Responsive Multi-Turn Online Conversations with Novel Dynamic Routing and Feedback Adaptation.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale Hybrid AI for Responsive Multi-Turn Online Conversations with Novel Dynamic Routing and Feedback Adaptation

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-09T21:48:36.596222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 91acdf87-e253-4dc0-b998-a8048bb63799 · outbound

This paper cites Qwen3 Technical Report.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale Qwen3 Technical Report

Reference 20

Resolution: metadata mismatch; local_arxiv, observed 2026-05-09T21:48:36.606739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1df9062f-e9e4-43f0-b92e-bfd7dcf0adf1 · outbound

This paper cites M 3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale M 3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation

Reference 21

Resolution: verified exact; doi, observed 2026-05-09T21:48:36.602858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 00956387-e982-48c3-abc8-7eb6b4e761da · outbound

This paper cites Kimi K2: Open Agentic Intelligence.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale Kimi K2: Open Agentic Intelligence

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T17:49:28.234076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 809d65cb-ae5f-47a3-8b4b-3eb6ac343acf · outbound

This paper cites D 2 LLM : Decomposed and Distilled Large Language Models for Semantic Search.

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale D 2 LLM : Decomposed and Distilled Large Language Models for Semantic Search

Reference 23

Resolution: verified exact; doi, observed 2026-05-09T21:48:36.592551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

No inbound Pith citation observations are available.