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Paper Citation Record · LEDGER

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating

As of 20 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2608.01112.

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

pith.paper-citation-record.v1
2608.01112 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:16:34.949172Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

24 of 24 outbound references displayed

  • verified exact0
  • verified fuzzy16
  • unresolved7
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1ea054fc-d5a0-46d3-9c7f-1bde009fcf14 · outbound

This paper cites Chen, L.; Bian, Y.; Deng, Y.; Cai, D.; Li, S.; Zhao, P.; and Wong, K.-F.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating Chen, L.; Bian, Y.; Deng, Y.; Cai, D.; Li, S.; Zhao, P.; and Wong, K.-F

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:16:35.261575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 78b77604-3b24-46d3-bacd-38aa271ef215 · outbound

This paper cites H.; Postma, D.; Hickerson, D.; McGaughran,J.;andKhuat,H.Q.2024.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating H.; Postma, D.; Hickerson, D.; McGaughran,J.;andKhuat,H.Q.2024

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:16:35.157562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T15:16:34.898390Z digest=sha256:56bf3ca5e0183debadc826ed8a2b3d372fc8230d1a6dd6109033812e102b3861

Observation d714eda8-b8d3-467d-8400-6dfed7560e26 · outbound

This paper cites Under the conditionally independent Bernoulli model, if the true null probabilities satisfy qfp i,j≤qfp i,j, calibrating the upper-tail threshold underqfp j is conservative.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating Under the conditionally independent Bernoulli model, if the true null probabilities satisfy qfp i,j≤qfp i,j, calibrating the upper-tail threshold underqfp j is conservative

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:16:35.092145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T15:16:34.931305Z digest=sha256:f8c35d38a8bcf19409141d69f2d6d5e20649d717504b06e09a5d4a7c83f82c8d

Observation 080750ee-020a-4eec-805f-fbe337f85dc8 · outbound

This paper cites Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-15T15:16:34.876410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:16:34.876410Z digest=sha256:ce5e1c03c961beb64011b38a5379d1a47ad5b31012235e49e86d6cbe8f5b3055

Observation 33991960-a7bb-4395-b8b3-34e32dc1ef48 · outbound

This paper cites Surrogate parameters remain frozen, and gradients update onlyδi.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating Surrogate parameters remain frozen, and gradients update onlyδi

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:16:35.052232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T15:16:34.945045Z digest=sha256:244c1dbbe68cf465fd0d4eef72c3a691dc3fb4d0099c5ef3a6a98c2d11153805

Observation 52960f39-7b78-4090-abb2-b7c9bc0db35e · outbound

This paper cites an unresolved cited work.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating Unresolved cited work

Reference 10

Resolution
unresolved
raw_fallback, observed 2026-08-15T15:16:35.180248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T15:16:34.885196Z digest=sha256:fdc5a5c46e6d8c4b8e1a3eb49871c8affbfabdfd68a8dda5fdaa89cc015c0e99

Observation f5ce7caa-72eb-4c68-9c39-4adb7483c3a0 · outbound

This paper cites SmolVLM: Redefining small and efficient multimodal models.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating SmolVLM: Redefining small and efficient multimodal models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-15T15:16:34.889489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:16:34.889489Z digest=sha256:9e1a0d19138496c7adf19932202adf438396e56f6e5fcaba2d74791688f2097c

Observation ebc6d5a7-f5d1-4f7e-aba9-bc3081def176 · outbound

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

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating InThe Twelfth In- ternational Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:16:35.169063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T15:16:34.894183Z digest=sha256:8c8818388446566569426dccc87fe140b92493a77b84c5822a4181cb5ed6ffc5

Observation b524d347-8f71-4dec-b3ec-ea0e0a8326d3 · outbound

This paper cites In Calandrino, J.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating In Calandrino, J

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:16:35.133092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T15:16:34.906369Z digest=sha256:ea68fcde20c7fe74e74fa1080a02f6d6aac57f06fe86c7d573d0e4c73728f170

Observation 15703848-e166-4b2b-91d3-19a3beaceae3 · outbound

This paper cites InIEEESym- posium on Security and Privacy, SP 2024, San Francisco, CA, USA, May 19-23, 2024, 807–825.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating InIEEESym- posium on Security and Privacy, SP 2024, San Francisco, CA, USA, May 19-23, 2024, 807–825

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:16:35.120974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T15:16:34.909912Z digest=sha256:63f9f18831896fa850d615b666fc854ee012a1184cbe0a8923e63134f128e97f

Observation f70bd5c7-908f-4aa8-8a2f-fb6d1b7be57f · outbound

This paper cites Gemma 4 Technical Report.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating Gemma 4 Technical Report

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-15T15:16:34.918285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:16:34.918285Z digest=sha256:0bb4d88f656439f00c52a377833e694e1413d5045d00a7de1d57e65d8d7b5970

Observation 73f1e7c8-ebdf-4ed1-9b31-78b1bf53b138 · outbound

This paper cites InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-15T15:16:34.925242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:16:34.925242Z digest=sha256:bbcdcec62fa6beaae8881f3ad5469927da4f12b72a155bb5c2ea9c3cb3efe939

Observation e1132a61-7307-43cf-85ea-d328deedf9e3 · outbound

This paper cites (15) Writingρi =|E i|−1, a conservative threshold is tα = min{t:P ρ (Ttgt≥t)≤α}.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating (15) Writingρi =|E i|−1, a conservative threshold is tα = min{t:P ρ (Ttgt≥t)≤α}

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:16:35.078749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T15:16:34.935507Z digest=sha256:2f59c73346de931f096822893de35cceba858ded95a10dc214d5e82c2f86895f

Observation f89a0a43-c790-4446-b684-338ff5527d29 · outbound

This paper cites The reported detector instead usesK= 30returned-answer calls for provider-independent calibration, so these probability-valued estimates do not enter the reported operating points.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating The reported detector instead usesK= 30returned-answer calls for provider-independent calibration, so these probability-valued estimates do not enter the reported operating points

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:16:35.065337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T15:16:34.940585Z digest=sha256:3aa13f07af4a2c5bcace0e7e44daecf6c34669b72e9b62fd0fd2135e430baf1a

Observation 91b1353b-5741-4396-9d94-7c196454f278 · outbound

This paper cites Adherence is the fraction of responses that contain a valid capital letter corresponding to an available option.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating Adherence is the fraction of responses that contain a valid capital letter corresponding to an available option

Reference 24

Resolution
malformed identifier
raw_fallback, observed 2026-08-15T15:16:35.039613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T15:16:34.949172Z digest=sha256:f549b622d74f65f77962dee3247be2401b2e9a683a4a492475a4e328373fcf42

Observation 72585eed-112a-4b68-9cc7-100e5ce63d76 · outbound

This paper cites In Bengio, Y.; and LeCun, Y., eds.,2nd International Conference on Learning Represen- tations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating In Bengio, Y.; and LeCun, Y., eds.,2nd International Conference on Learning Represen- tations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings

Reference 2014

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:16:35.107374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T15:16:34.913743Z digest=sha256:02fdaf08c58acbf427a76f670096347d84b92872d152fbf732e2e14375e25626

Observation 60c37316-aa9b-41b7-8f15-5a388bb841ec · outbound

This paper cites InInternational Conference on Learning Representations.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating InInternational Conference on Learning Representations

Reference 2017

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:16:35.190800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T15:16:34.881144Z digest=sha256:97bc3763e05f547c247623e52849dc17634dcfe34d5ec1e91260b7412964930d

Observation 4b603af3-ce0f-4522-a49e-926da81792e7 · outbound

This paper cites In2018 IEEE Conference on Computer Vision and Pattern Recognition,CVPR2018,SaltLakeCity,UT,USA,June18- 22, 2018, 9185–9193.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating In2018 IEEE Conference on Computer Vision and Pattern Recognition,CVPR2018,SaltLakeCity,UT,USA,June18- 22, 2018, 9185–9193

Reference 2018

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:16:35.249056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T15:16:34.854127Z digest=sha256:748cde20decee74478b227b5134f98980b0ebfc2c1cc8197746d83aec8239e3c

Observation 7c849d80-810c-4c3c-b3b6-3c27a822b92b · outbound

This paper cites In Costa-jussà, M.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating In Costa-jussà, M

Reference 2019

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:16:35.237736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T15:16:34.859048Z digest=sha256:6b3a0c8465341ab1c26236e78da2ad0b0ffdb72232991fff76d18dec4e364bae

Observation dabc2c75-7a5f-4c4f-8eb0-8364896ca8dd · outbound

This paper cites InProceedings of the AAAI Conference on Artificial Intelligence, volume 36, 10758–10766.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating InProceedings of the AAAI Conference on Artificial Intelligence, volume 36, 10758–10766

Reference 2022

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:16:35.214495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T15:16:34.868109Z digest=sha256:aec73aeb973b4412689073e57f7e8fba2fa5ee617fe5f9687db637ec5cf2cf54

Observation 538a685d-9c7b-45a2-a9ad-4eb5f439cf47 · outbound

This paper cites an unresolved cited work.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating Unresolved cited work

Reference 2023

Resolution
unresolved
raw_fallback, observed 2026-08-15T15:16:35.145900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T15:16:34.902204Z digest=sha256:97208c6a28b8a83e8a63e8d90c60b7595a23dbf33e2061537bb4c60b4c00bad2

Observation d96a0700-9e7b-4948-85d4-6e1260f2f51e · outbound

This paper cites In Kim, B.; Yue, Y.; Chaudhuri, S.; Fragkiadaki, K.; Khan, M.;andSun,Y.,eds.,InternationalConferenceonLearning Representations, volume 2024, 38745–38768.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating In Kim, B.; Yue, Y.; Chaudhuri, S.; Fragkiadaki, K.; Khan, M.;andSun,Y.,eds.,InternationalConferenceonLearning Representations, volume 2024, 38745–38768

Reference 2024

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:16:35.225794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T15:16:34.863880Z digest=sha256:f6159c09b5b5ac8601cdbb844d1e28c1e2831813e77f45ea7e44f09c706bf271

Observation 312c612f-1bd8-4d39-8a77-16cc2d442311 · outbound

This paper cites Qwen3-VL Technical Report.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating Qwen3-VL Technical Report

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-15T15:16:34.845672Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:16:34.845672Z digest=sha256:f4bfbd7f79d9380dccc56cce2cb0546db75727871ef8527d951c374e670866f1

Observation a0c56cda-fd64-4129-9b3a-5e9954c0a155 · outbound

This paper cites InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 42341–42351.

Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 42341–42351

Reference 2026

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:16:35.202851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T15:16:34.872402Z digest=sha256:4b540acb1f80b3479a3c5f792eea0c39bfe0bf58aab4e2eb8e8e6cb29e0f317b

Pith citing papers

No inbound Pith citation observations are available.