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Paper Citation Record · LEDGER

Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm

As of 18 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2412.12006.

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

pith.paper-citation-record.v1
2412.12006 v2

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:25:38.563676Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

14 of 14 outbound references displayed

  • verified exact0
  • verified fuzzy3
  • unresolved11
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e8216082-2c00-410a-9da9-e208479c4379 · outbound

This paper cites E., Walker, S., Jones, S., Hancock-Beaulieu , M., & Gatford, M.

Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm E., Walker, S., Jones, S., Hancock-Beaulieu , M., & Gatford, M

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:25:38.677035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-11T14:25:38.526864Z digest=sha256:4cb011858f0bd7ad4baa44afc9ba02a4819e4a9149da9faae569e3ccaedcf1e3

Observation c0133878-584a-4e63-b45c-85831b522d1a · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-11T14:25:38.530559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:25:38.530559Z digest=sha256:04ee2826171c65df4528f58dae11641762aef885a2db3c97f72483951337988c

Observation 0364d475-cd95-4c12-b5e3-7280912ce46c · outbound

This paper cites MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers.

Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-11T14:25:38.533403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:25:38.533403Z digest=sha256:b94dab66a9b54841b4f7a88059021af207b97d41e9b1043782e372f2af6272b8

Observation 9c1cc2a7-b4bd-409b-a842-dcc594a44fe1 · outbound

This paper cites Billion-scale similarity search with GPUs.

Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm Billion-scale similarity search with GPUs

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-11T14:25:38.536358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:25:38.536358Z digest=sha256:203192196709aa31d55c03da1c1102e68e7c7e4ff7552354b43d0c549d715fb9

Observation 09a60544-2ec5-42b1-939a-793f35517052 · outbound

This paper cites an unresolved cited work.

Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm Unresolved cited work

Reference 5

Resolution
unresolved
raw_fallback, observed 2026-08-11T14:25:38.668800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-11T14:25:38.539182Z digest=sha256:76b497eadf2ffc14967dba50363f49121e9fa0f8a30bcc64856243c943e33dec

Observation 94c7b8d4-8dd7-48e1-bb50-79ed070cec15 · outbound

This paper cites B., Mann, B., Ryder, N., et al.

Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm B., Mann, B., Ryder, N., et al

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:25:38.661319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-11T14:25:38.542050Z digest=sha256:ba6b1b88cbe1708c8bd7abef025b9fc2783e09a6ee77a1ed7da373ecc1b7520e

Observation aa58b931-dabf-4001-a54e-7b389db084c2 · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-11T14:25:38.544838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:25:38.544838Z digest=sha256:d99c5df0a30baa2d84c80a282e0588a1eab635ae47c0dd4b6fd88b5f5452f96e

Observation 3d44665a-88a5-43b7-9986-4aa2b9580b3d · outbound

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

Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm Dense Passage Retrieval for Open-Domain Question Answering

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T14:25:38.547845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:25:38.547845Z digest=sha256:80b40b8a92f335ced6745339b7dcdfd9ca45dc804c6a5298332686c23d88684d

Observation f972222a-1935-4b8e-a5a9-6977307aee96 · outbound

This paper cites an unresolved cited work.

Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm Unresolved cited work

Reference 9

Resolution
unresolved
raw_fallback, observed 2026-08-11T14:25:38.653543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-11T14:25:38.550704Z digest=sha256:805f4cbbc941d1933d31fc2f5fc710ca96b01c1b7a9834e7e66654815f50a428

Observation c277862f-a8f1-4094-920d-ff05b2ca1116 · outbound

This paper cites Self-Taught Evaluators.

Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm Self-Taught Evaluators

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-11T14:25:38.553228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:25:38.553228Z digest=sha256:a4e54a436824254fdef368b0deda1a665f3520f45bca5829552bcfbd071720e7

Observation 0dab2e9f-2cb7-4fa1-b890-a7dc93e44d18 · outbound

This paper cites Optimizing Query Generation for Enhanced Document Retrieval in RAG.

Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm Optimizing Query Generation for Enhanced Document Retrieval in RAG

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-11T14:25:38.556024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:25:38.556024Z digest=sha256:4eb5d29dcd57ce4743ab8c7bfef829274677ea541829914555a649955d975b15

Observation a4230a81-33ee-492e-b264-38a441497128 · outbound

This paper cites A BERT Baseline for the Natural Questions.

Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm A BERT Baseline for the Natural Questions

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-11T14:25:38.558656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:25:38.558656Z digest=sha256:c3e90fe48c2b8591eb88b40c6719b434c8bfe3d460546dbbd693553bfecadf55

Observation c8cb4071-99bc-4be7-9554-41fb0edff46e · outbound

This paper cites N., Jones, L., Chang, M., Dai, A., Uszkoreit, J., Le, Q., & Petrov, S.

Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm N., Jones, L., Chang, M., Dai, A., Uszkoreit, J., Le, Q., & Petrov, S

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:25:38.645605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-11T14:25:38.561340Z digest=sha256:0c1f6dbf22989913c63eb96bbfb2de83183b576b51ec5e235bac5ff63eb7fc8d

Observation e6b081df-6b45-4413-bdab-7f5d6f2bc7c8 · outbound

This paper cites an unresolved cited work.

Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm Unresolved cited work

Reference 14

Resolution
unresolved
raw_fallback, observed 2026-08-11T14:25:38.636685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-11T14:25:38.563676Z digest=sha256:8e0faa98ff357b8881334fb93ee444a9afc5ed9449c01f3db42cf96dbff1f1da

Pith citing papers

No inbound Pith citation observations are available.