Paper Citation Record · LEDGER

LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection

As of 11 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 1 inbound Pith citation observation for arXiv:2507.11071.

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

pith.paper-citation-record.v1
2507.11071 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:20:43.456980Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T02:49:21.124253Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T02:53:29.828376Z

Reference resolution

18 of 18 outbound references displayed

verified exact0
verified fuzzy8
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7e4a4774-3186-4d14-be1d-ccb35320424e · outbound

This paper cites https://moldstud.com/articles/p-the-impact-of-big-data-on-software-development (2024).

LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection https://moldstud.com/articles/p-the-impact-of-big-data-on-software-development (2024)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:20:45.179841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f48f2aad-d1a1-40ab-8dca-4a932256b9c3 · outbound

This paper cites ICLR1(2), 3 (2022).

LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection ICLR1(2), 3 (2022)

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T17:20:42.001992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aefcab09-62bd-4a22-b9ff-961d54298ea9 · outbound

This paper cites In: International Conference on Machine Learning, pp.

LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection In: International Conference on Machine Learning, pp

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:20:45.099322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6fdb71f2-c4d4-49c4-baf9-3bfa29695b41 · outbound

This paper cites TinyLlama: An Open-Source Small Language Model.

LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection TinyLlama: An Open-Source Small Language Model

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T17:20:42.202188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c77f119-3df5-4ca2-88b5-d56227742f61 · outbound

This paper cites LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning.

LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T17:20:42.301515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e6367af-b238-4c38-b212-4c942f07db9e · outbound

This paper cites an unresolved cited work.

LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:20:44.909840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5d6599a8-a181-4343-824a-69a864638d46 · outbound

This paper cites IEEE international conference on web services (ICWS) (2017).

LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection IEEE international conference on web services (ICWS) (2017)

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:20:44.725586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d70b4de3-8c58-40a9-a171-588bafe04f18 · outbound

This paper cites Advances in Neural Information Pro- cessing Systems 30 (2017).

LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection Advances in Neural Information Pro- cessing Systems 30 (2017)

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:20:44.573471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8efddc4e-e8f5-49a7-a628-b0069e6f699d · outbound

This paper cites In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp.

LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:20:44.426260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ed366201-e1c7-4af2-a148-01612fc10b7a · outbound

This paper cites Advances in Neural Information Processing Systems (2017).

LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection Advances in Neural Information Processing Systems (2017)

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T17:20:42.700282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5499fc1-d7ae-4c45-86c2-37cb209e7c6e · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T17:20:42.850968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b022f57-4425-4903-ac28-dbb279415dff · outbound

This paper cites Recurrent Neural Networks (RNNs): A gentle Introduction and Overview.

LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection Recurrent Neural Networks (RNNs): A gentle Introduction and Overview

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T17:20:42.937876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f37655ed-feee-42ee-96e3-5bb49c24fb5e · outbound

This paper cites In: 2021 International Joint Conference on Neural Networks (IJCNN), pp.

LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection In: 2021 International Joint Conference on Neural Networks (IJCNN), pp

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:20:44.276727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 72100165-ec66-4655-9a32-998cc6577324 · outbound

This paper cites an unresolved cited work.

LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:20:44.090326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b65eaa95-92a8-43bc-bd5f-cebfb4864d91 · outbound

This paper cites In: 2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE), pp.

LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection In: 2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE), pp

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:20:43.902566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 52008183-0cea-44f2-9eeb-a08057643376 · outbound

This paper cites In: 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN’07), pp.

LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection In: 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN’07), pp

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:20:43.675147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d35a484d-3fd9-438c-958d-c5102796ed0b · outbound

This paper cites Textbooks Are All You Need II: phi-1.5 technical report.

LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection Textbooks Are All You Need II: phi-1.5 technical report

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T17:20:43.394359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14f71b86-a6b6-4fed-9d6c-8790f4d86d54 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection OPT: Open Pre-trained Transformer Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T17:20:43.456980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 57070d0d-378e-4cb9-9a28-095abbc9dc1e · inbound

DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs cites this paper.

DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-10T02:53:29.829602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.