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Paper Citation Record · LEDGER

PARSE: Provenance-Aware Retrieval Sanitization for Professional Domain LLM Agents

As of 8 August 2026, this Paper Citation Record lists 8 of 8 outbound references and 0 inbound Pith citation observations for arXiv:2606.17467.

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

pith.paper-citation-record.v1
2606.17467 v2

Coverage vector

measured 8 of 8 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T11:07:08.044674Z

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

8 of 8 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved8
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 592f3ea7-fc44-422d-8172-2daa9d727585 · outbound

This paper cites Baseline Defenses for Adversarial Attacks Against Aligned Language Models.

PARSE: Provenance-Aware Retrieval Sanitization for Professional Domain LLM Agents Baseline Defenses for Adversarial Attacks Against Aligned Language Models

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-02T11:07:07.657788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T11:07:07.657788Z digest=sha256:4bfbcf328f3233aaabc15d3f29812f66bb1bc1c616614ff070a098cee970ef06

Observation bb272f13-dfd3-4b2c-8333-cbf71bb84b4c · outbound

This paper cites Preprint, arXiv:2310.12815.

PARSE: Provenance-Aware Retrieval Sanitization for Professional Domain LLM Agents Preprint, arXiv:2310.12815

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-02T11:07:07.760381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T11:07:07.760381Z digest=sha256:0c02a5a3c6b49f3adb048d01db2ccbdd973953ff1bdd539b492d32a010002838

Observation 31994e44-11ee-4eed-8214-a03018936177 · outbound

This paper cites Blind Spots in the Guard: How Domain-Camouflaged Injection Attacks Evade Detection in Multi-Agent LLM Systems.

PARSE: Provenance-Aware Retrieval Sanitization for Professional Domain LLM Agents Blind Spots in the Guard: How Domain-Camouflaged Injection Attacks Evade Detection in Multi-Agent LLM Systems

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-02T11:07:07.881761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T11:07:07.881761Z digest=sha256:067259c3a439cb06488d98ceb6c923efcf0a6f598af6523f20fe19c701cc3dbf

Observation 0dd43cb4-5c27-4514-89d9-bedc3a1febbf · outbound

This paper cites The Prompt Report: A Systematic Survey of Prompt Engineering Techniques.

PARSE: Provenance-Aware Retrieval Sanitization for Professional Domain LLM Agents The Prompt Report: A Systematic Survey of Prompt Engineering Techniques

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-02T11:07:08.044674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T11:07:08.044674Z digest=sha256:5ad4d38f0c7bb3ed70326fea79201bc826230e56341d2ff4d2afffa13e201069

Observation 538a620d-b81b-4d4c-b1c6-dd2469c8395a · outbound

This paper cites Ignore Previous Prompt: Attack Techniques For Language Models.

PARSE: Provenance-Aware Retrieval Sanitization for Professional Domain LLM Agents Ignore Previous Prompt: Attack Techniques For Language Models

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-02T11:07:07.956789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T11:07:07.956789Z digest=sha256:e70a136583c0d7877a6395f422bb25dded6813834848fadb092eee136ba31c2e

Observation e531fa75-a6fb-415f-8cf9-ae14824f03fb · outbound

This paper cites Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations.

PARSE: Provenance-Aware Retrieval Sanitization for Professional Domain LLM Agents Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-02T11:07:07.582414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T11:07:07.582414Z digest=sha256:a73534b8c7aca2f85eb584b6cd5a5be7615729d794e83f0827cb9ff22844a801

Observation 317cc991-7106-429f-8973-67bdeb1617af · outbound

This paper cites Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models.

PARSE: Provenance-Aware Retrieval Sanitization for Professional Domain LLM Agents Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-02T11:07:07.442032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T11:07:07.442032Z digest=sha256:22427f2ed92041853bba8ccf0b9afa721340db6ea61d5ccfadc37a7e733e58ff

Observation e587a4f2-6137-49b9-add2-3647679d16e0 · outbound

This paper cites Preprint, arXiv:2510.08829.

PARSE: Provenance-Aware Retrieval Sanitization for Professional Domain LLM Agents Preprint, arXiv:2510.08829

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-02T11:07:07.283971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T11:07:07.283971Z digest=sha256:2f8d2bbe930c62c4c947cddb8b74d7895763c9b693ffc68bef7c7354f955f4f5

Pith citing papers

No inbound Pith citation observations are available.