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Paper Citation Record · LEDGER

Black-Box Forensics for Conversational LLM Agents

As of 11 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2606.22698.

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

pith.paper-citation-record.v1
2606.22698 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T09:50:07.322483Z

measured 28 of 28 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 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

28 of 28 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved21
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch6

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 83ef343e-9ed5-4cad-b13c-e7a0e49ef547 · outbound

This paper cites gpt-oss-120b & gpt-oss-20b Model Card.

Black-Box Forensics for Conversational LLM Agents gpt-oss-120b & gpt-oss-20b Model Card

Reference 1

Resolution
metadata mismatch
local_arxiv, observed 2026-07-04T09:39:45.908963Z

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.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:27b83b1c1da838ade028639ebcae7015e80a080b6d724cd0cc5b7e751b7ff757

Observation f0ccd271-37f0-4995-b803-adede4ea8fba · outbound

This paper cites InProceedings of the 2024 Joint International Conference on Compu- tational Linguistics, Language Resources and Evalu- ation (LREC-COLING 2024), pages 7531–7543.

Black-Box Forensics for Conversational LLM Agents InProceedings of the 2024 Joint International Conference on Compu- tational Linguistics, Language Resources and Evalu- ation (LREC-COLING 2024), pages 7531–7543

Reference 2

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no resolver link, observed 2026-06-26T09:50:07.322483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:8be33ee626727eaf7c5e56ace39892fddb4a7e63393630196c88ad3cac3b4bc4

Observation 7254337b-3fe1-4ea6-9d1b-5c459b05b163 · outbound

This paper cites Longformer: The Long-Document Transformer.

Black-Box Forensics for Conversational LLM Agents Longformer: The Long-Document Transformer

Reference 3

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metadata mismatch
local_arxiv, observed 2026-07-04T09:39:45.920570Z

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.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:0186a1e92feb18fffca54c2e4de71daa320ed406b815ad37eb9f730d94283f5c

Observation 383f04f3-b9df-4a15-b625-26e7ad4b965a · outbound

This paper cites Zero-Shot Attribution for Large Language Models: A Distribution Testing Approach.

Black-Box Forensics for Conversational LLM Agents Zero-Shot Attribution for Large Language Models: A Distribution Testing Approach

Reference 4

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verified exact
arxiv_id, observed 2026-07-04T09:39:45.923729Z

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.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:9b39637af76fee64cb3921418c105082830c52c601d3dfd213ce01d45743bf53

Observation b800b6f1-cff3-46ba-822f-15fca7b6e3df · outbound

This paper cites The Llama 3 Herd of Models.

Black-Box Forensics for Conversational LLM Agents The Llama 3 Herd of Models

Reference 5

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metadata mismatch
local_arxiv, observed 2026-07-04T09:39:45.917902Z

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.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:b169c4c0259b7a5c3ff09790bea85705ffef9f9ff2d8e9581a6fd6b2cc24f0ef

Observation d20b28a9-3b44-4e53-b0a3-fd3ed37c0f85 · outbound

This paper cites AI Generated Text Detection Using Instruction Fine-tuned Large Language and Transformer-Based Models.

Black-Box Forensics for Conversational LLM Agents AI Generated Text Detection Using Instruction Fine-tuned Large Language and Transformer-Based Models

Reference 6

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metadata mismatch
arxiv_id, observed 2026-07-04T09:39:45.912070Z

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.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:4a51c5042f0a4f3e5628659bc863121d560bd5d97458412c6a594272bac54fa0

Observation 62db3b1e-373c-464d-8cb4-f53f8ba43316 · outbound

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

Black-Box Forensics for Conversational LLM Agents BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 7

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metadata mismatch
local_arxiv, observed 2026-07-04T09:39:45.906234Z

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.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:4b53c6887cb55f79bdb874a5abc9db81d08c5653bfaf7ad7108acb06a3ad2ef5

Observation d4ec31aa-8a28-4ae4-8ac0-f748407eb51d · outbound

This paper cites InProceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pages 8384–8395.

Black-Box Forensics for Conversational LLM Agents InProceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pages 8384–8395

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:9c635ad322302e3455dfac10f99e7cc31a95baf4aab86b13df6c246085f00307

Observation e6caa0bb-2bfa-40b1-96cd-c9791e30d324 · outbound

This paper cites Qwen3 Technical Report.

Black-Box Forensics for Conversational LLM Agents Qwen3 Technical Report

Reference 9

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metadata mismatch
local_arxiv, observed 2026-07-04T09:39:45.915029Z

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.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:4766d44a71f2a90d94c47bf52cd524c7ad7661b40340f5e1aea16dfcea9c1eb3

Observation d3053100-48e2-48e1-aed7-3d65619df413 · outbound

This paper cites Be clear, direct, and helpful at all times.

Black-Box Forensics for Conversational LLM Agents Be clear, direct, and helpful at all times

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:cc977c075750f443f59cf39bdfb7dfe2bebffc7a7f41f686010aa1870f47ddff

Observation 435689a5-c398-4b26-90f6-94ac91a88e69 · outbound

This paper cites Speak with kindness, patience, and encouragement.

Black-Box Forensics for Conversational LLM Agents Speak with kindness, patience, and encouragement

Reference 11

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:979eabf68522f994b48a4505c5c3cd5073ea6b217c2de7a9f065ff1f88216acf

Observation 00958292-191b-4e43-8e94-72a4804de488 · outbound

This paper cites Speak naturally, like a thoughtful and approachable person.

Black-Box Forensics for Conversational LLM Agents Speak naturally, like a thoughtful and approachable person

Reference 12

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unresolved
no resolver link, observed 2026-06-26T09:50:07.322483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:4d2a10be02ffb06961e67660eb17befd413bc5cc07088e7528762bbd1cf497f0

Observation 351c4de3-ee6d-4129-b3ba-6e1ac5333f3a · outbound

This paper cites Provide clear reasoning, step-by-step explanations, and enough detail for the user to understand the answer deeply.

Black-Box Forensics for Conversational LLM Agents Provide clear reasoning, step-by-step explanations, and enough detail for the user to understand the answer deeply

Reference 13

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no resolver link, observed 2026-06-26T09:50:07.322483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:6804db09e9ba33d997e80eec999b73b43a4d966d69c73a39bc55ef8f4a13ec12

Observation 699fec75-0efe-4ef4-85fc-ebf9b3fd6d21 · outbound

This paper cites Rather than always giving the answer immediately, help the user think through problems by asking thoughtful questions.

Black-Box Forensics for Conversational LLM Agents Rather than always giving the answer immediately, help the user think through problems by asking thoughtful questions

Reference 14

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no resolver link, observed 2026-06-26T09:50:07.322483Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:ba73402c34d42c6559cc9637776252ca3a982882ebeb7f285e537ff10128e3e8

Observation 60ec9403-161f-4713-9d91-be27daa27614 · outbound

This paper cites Break down complex ideas into manageable pieces.

Black-Box Forensics for Conversational LLM Agents Break down complex ideas into manageable pieces

Reference 15

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:5377812c2f60d7e7b62610e57fba8067ab0cdcfbc8ad5c99c5002ec70ce94ae4

Observation bf9c6728-42ec-476a-9eba-43bb46e53a27 · outbound

This paper cites Approach requests with originality, flexible thinking, and vivid language when appropriate.

Black-Box Forensics for Conversational LLM Agents Approach requests with originality, flexible thinking, and vivid language when appropriate

Reference 16

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:2771d1530889882f6db2bafd8206fcc71e835cf933aabc86ce3e7d8b690be0ca

Observation 2febc086-a6fb-49e2-9001-3a73057afe38 · outbound

This paper cites Break problems into components, examine assumptions, and reason carefully.

Black-Box Forensics for Conversational LLM Agents Break problems into components, examine assumptions, and reason carefully

Reference 17

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no resolver link, observed 2026-06-26T09:50:07.322483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:ed10125f3d5ca314ffe6f4caf1b963f2ef203856d5a231380092f6e370e9fe03

Observation e5c68695-a0e9-4888-ba1f-98ea6c11f8ac · outbound

This paper cites Respond in a way that shows careful listening and emotional awareness.

Black-Box Forensics for Conversational LLM Agents Respond in a way that shows careful listening and emotional awareness

Reference 18

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:84375a14175a03c49945f68fdd62c97923687aefe3e9a9ff18a08096116df39f

Observation 9fe764df-9a7f-4c65-b7cc-07667415bc48 · outbound

This paper cites Bring positive energy into the conversation while remaining useful and grounded.

Black-Box Forensics for Conversational LLM Agents Bring positive energy into the conversation while remaining useful and grounded

Reference 19

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source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:7ee6f83b638dc0bc70d7f16da1be5a753af76b1aa681415ebca5a95e502d02ec

Observation c0309707-0376-4984-9c60-0041cc91fa72 · outbound

This paper cites Use refined, profes- sional language and a composed tone.

Black-Box Forensics for Conversational LLM Agents Use refined, profes- sional language and a composed tone

Reference 20

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:07c3941e95c12bb55242ce7a06f354003bb525c22ba174b87ea7a32e7ec01b74

Observation 94060efc-6909-4e25-ae03-07f7ac32d9bc · outbound

This paper cites Prioritize actionable advice, concrete next steps, and realistic solutions.

Black-Box Forensics for Conversational LLM Agents Prioritize actionable advice, concrete next steps, and realistic solutions

Reference 21

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:261cfdc0184a87e4a98e0705f1175bd45f21678e3a34e3d63fbc86429e4c0cb1

Observation 9b680013-f4f7-42f8-a014-7aafb75861d5 · outbound

This paper cites Frame the interaction as joint problem-solving.

Black-Box Forensics for Conversational LLM Agents Frame the interaction as joint problem-solving

Reference 22

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:a6928daab9775c71e81fc132e3e46b02326714e574d8a58a7a0481a07b40cb00

Observation 1bf027bd-e4b1-4cba-8d65-4a6adc3be1cc · outbound

This paper cites Be polite, patient, and solutions- oriented.

Black-Box Forensics for Conversational LLM Agents Be polite, patient, and solutions- oriented

Reference 23

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source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:fe219570f1e38bb773d516af92dd088038df0f1e93c2d7939bc7b268ad27ecaf

Observation 5a785cbc-ac50-493f-b3dd-c7b79520cacb · outbound

This paper cites Handle sensitive topics carefully and respectfully.

Black-Box Forensics for Conversational LLM Agents Handle sensitive topics carefully and respectfully

Reference 24

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:9f3f0d04bfd4a17166bfc040c3132be84efacf3d45ac63f55bbbf4bffafed315

Observation b4d073a0-5061-485f-b795-a921f062fa3f · outbound

This paper cites Encourage the user to make progress and build confidence.

Black-Box Forensics for Conversational LLM Agents Encourage the user to make progress and build confidence

Reference 25

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:7bb785f2cfc413a6219c3b40c65006b4471940de4730cd7f556f43df5ee5a398

Observation 09f6c1d6-773c-4952-abe5-1e4e995390fa · outbound

This paper cites Respond with care, nuance, and depth.

Black-Box Forensics for Conversational LLM Agents Respond with care, nuance, and depth

Reference 26

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source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:63521f1e61217c18b601bfaffc693283de0f08c038111718b64228696e5c21f6

Observation 954a86fc-e845-4735-8d6d-f9df04ffd07c · outbound

This paper cites Use light humor and a bit of personality when appropriate, while still giving solid, useful answers.

Black-Box Forensics for Conversational LLM Agents Use light humor and a bit of personality when appropriate, while still giving solid, useful answers

Reference 27

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no resolver link, observed 2026-06-26T09:50:07.322483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:32b1c66f9e66df8a6edd44caff678882a22a0678b75b1ec0f4343b68cc331b75

Observation 8d7ca185-525c-4ec7-93bb-4a79ae125cf6 · outbound

This paper cites You are a friendly, conversational assistant. Make the interaction feel easy and comfortable.

Black-Box Forensics for Conversational LLM Agents You are a friendly, conversational assistant. Make the interaction feel easy and comfortable

Reference 28

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no resolver link, observed 2026-06-26T09:50:07.322483Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-26T09:50:07.322483Z digest=sha256:0866947410cd3d5cf1c217074121d9de35c04b2d0b071f047139170d9f3d7fd2

Pith citing papers

No inbound Pith citation observations are available.