Paper Citation Record · LEDGER

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study

As of 9 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2608.04576.

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

pith.paper-citation-record.v1
2608.04576 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:42:28.806022Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

22 of 22 outbound references displayed

verified exact1
verified fuzzy8
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0d5d616c-35c4-4771-9b2f-b4761dcea40e · outbound

This paper cites The Llama 3 Herd of Models.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study The Llama 3 Herd of Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T21:42:27.313734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3336d1aa-2402-4cb7-ba38-c08efd04b350 · outbound

This paper cites GPT-4o System Card.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study GPT-4o System Card

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T21:42:27.485777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6474e68d-9974-462f-a564-73c877a8b673 · outbound

This paper cites Faithful-First Reasoning, Planning, and Acting for Multimodal LLMs.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study Faithful-First Reasoning, Planning, and Acting for Multimodal LLMs

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T21:42:29.408157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2a15e60e-ba00-4be9-9b1c-52c1a7c0591f · outbound

This paper cites Peiyang Liu, Ziqiang Cui, Di Liang, and Wei Ye.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study Peiyang Liu, Ziqiang Cui, Di Liang, and Wei Ye

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T21:42:27.729698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7764d2c0-bdd0-4d32-bd40-3b3025a5675e · outbound

This paper cites SynthVLM: Towards High-Quality and Efficient Synthesis of Image-Caption Datasets for Vision-Language Models.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study SynthVLM: Towards High-Quality and Efficient Synthesis of Image-Caption Datasets for Vision-Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T21:42:27.849692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82308d31-6f21-4882-a463-971cb1c6850f · outbound

This paper cites EmoMAS: Emotion-Aware Multi-Agent System for High-Stakes Edge-Deployable Negotiation with Bayesian Orchestration.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study EmoMAS: Emotion-Aware Multi-Agent System for High-Stakes Edge-Deployable Negotiation with Bayesian Orchestration

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T21:42:27.948363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e8d3e4d-b38f-411a-8f4e-c6e39d3a6bef · outbound

This paper cites Kexin Ma, Ruochun Jin, Wang Haotian, Wang Xi, Huan Chen, Yuhua Tang, and Qian Wang.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study Kexin Ma, Ruochun Jin, Wang Haotian, Wang Xi, Huan Chen, Yuhua Tang, and Qian Wang

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T21:42:28.019020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf5df034-8eca-467f-987e-13bf30a61638 · outbound

This paper cites InFindings of the Association for Computational Linguistics: EMNLP 2024, pages 4886–4901, Miami, Florida, USA.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study InFindings of the Association for Computational Linguistics: EMNLP 2024, pages 4886–4901, Miami, Florida, USA

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:42:30.884535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5f8b5803-f624-4259-a148-18d9f827a182 · outbound

This paper cites Preprint, arXiv:2601.09833.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study Preprint, arXiv:2601.09833

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T21:42:28.167841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 902378a5-136c-4600-ae14-5563a12ac04c · outbound

This paper cites InProceedings of the 2023 Conference on Empiri- cal Methods in Natural Language Processing, pages 9004–9017, Singapore.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study InProceedings of the 2023 Conference on Empiri- cal Methods in Natural Language Processing, pages 9004–9017, Singapore

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:42:30.617994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9efd8efa-e69e-421d-a0f5-d5be066dccd9 · outbound

This paper cites https://apidoc.reliefweb.int/.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study https://apidoc.reliefweb.int/

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:42:30.362599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ac5a2912-4d13-41cd-994f-a11daa8de4b9 · outbound

This paper cites Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, and Maosong Sun.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, and Maosong Sun

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T21:42:28.492480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edccf8c5-f0c0-413f-baf1-61bc9d2b4f8c · outbound

This paper cites InThe Twelfth International Conference on Learning Representa- tions, ICLR 2024, Vienna, Austria, May 7-11,.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study InThe Twelfth International Conference on Learning Representa- tions, ICLR 2024, Vienna, Austria, May 7-11,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:42:30.177012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5cba50ec-f09c-4680-868c-a91888607cf4 · outbound

This paper cites Qianchi Zhang, Hainan Zhang, Liang Pang, Yongxin Tong, Hongwei Zheng, and Zhiming Zheng.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study Qianchi Zhang, Hainan Zhang, Liang Pang, Yongxin Tong, Hongwei Zheng, and Zhiming Zheng

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:42:29.968903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b4120cec-9f57-4856-bc12-c1a1faed0297 · outbound

This paper cites ExpSeek: Self-Triggered Experience Seeking for Web Agents.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study ExpSeek: Self-Triggered Experience Seeking for Web Agents

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T21:42:28.806022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c56b503c-b9d0-4b59-b828-f35b33b29198 · outbound

This paper cites Enhanced Response Capacity Project 2014–2015.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study Enhanced Response Capacity Project 2014–2015

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:42:31.310446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e0b04a57-60c8-4dce-829d-b7ba84adc250 · outbound

This paper cites In8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30,.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study In8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30,

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T21:42:28.715685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f79e9b56-4a8e-4164-901d-4ad276f3620a · outbound

This paper cites InThe Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29,.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study InThe Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29,

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:42:31.105867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 95f84802-f357-4527-bd16-ff8fb17e73db · outbound

This paper cites Qwen Technical Report.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study Qwen Technical Report

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T21:42:26.901623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf576353-d78f-4770-98a8-ad07d305c7ca · outbound

This paper cites InThe Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11,.

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:42:31.502571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 12beddc8-80af-428e-a103-f1ff42a26685 · outbound

This paper cites DeepSeek-V3 Technical Report.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study DeepSeek-V3 Technical Report

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T21:42:27.141126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a8becfb-db21-41fa-a883-8773a47f476b · outbound

This paper cites EMSDialog: Synthetic Multi-person Emergency Medical Service Dialogue Generation from Electronic Patient Care Reports via Multi-LLM Agents.

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study EMSDialog: Synthetic Multi-person Emergency Medical Service Dialogue Generation from Electronic Patient Care Reports via Multi-LLM Agents

Reference 2026

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:42:29.649741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

No inbound Pith citation observations are available.