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Paper Citation Record · LEDGER

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B

As of 9 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2607.22545.

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

pith.paper-citation-record.v1
2607.22545 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T14:52:12.628404Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

29 of 29 outbound references displayed

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  • verified fuzzy0
  • unresolved29
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 83d69bbd-be44-4869-9f7c-db4af95b53a5 · outbound

This paper cites DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing

Reference 1

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source=pdf_text observed=2026-08-02T14:52:12.512497Z digest=sha256:5a4dd55e8ad2e104094eae8709e48d46c4d823a22f190b45a7303b8b52052d7b

Observation 0a51ac02-aab5-4a6c-83a5-51bfa31645ae · outbound

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

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

Reference 2

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source=pdf_text observed=2026-08-02T14:52:12.517621Z digest=sha256:9d3509b673828f71c574090affc4b8d37c0954c2653a61907ea4ac4bf2c473f2

Observation bb59b46b-e2a1-46ba-9169-af54eb0c82bc · outbound

This paper cites Llama Guard 3 8B Model Card,.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B Llama Guard 3 8B Model Card,

Reference 3

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source=pdf_text observed=2026-08-02T14:52:12.522550Z digest=sha256:e65232d09e82999df42b770646524e6401e989decb0bbdd38710eb40284806c1

Reference 4

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source=pdf_text observed=2026-08-02T14:52:12.527253Z digest=sha256:154d11f8f6a271514008fa0495115ce27ebf63e1adb19500c86805204f2d38e0

Observation 5421efe7-1cea-4249-bf1c-197444273e0c · outbound

This paper cites WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs

Reference 5

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source=pdf_text observed=2026-08-02T14:52:12.532422Z digest=sha256:c1f452edbddd4b1fadb261edd2fb0410424cfa33147fd628fd3bb7adb4017ed6

Observation e1b881cd-e159-4812-9fe2-5a518ad0890d · outbound

This paper cites PromptGuard 2: Robust Prompt Injection and Jailbreak Detection,.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B PromptGuard 2: Robust Prompt Injection and Jailbreak Detection,

Reference 6

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source=pdf_text observed=2026-08-02T14:52:12.536512Z digest=sha256:cd474e9bdd7a123120867fad49ef7b345fd83b596027d026e71b120529dbaf09

Observation bcefd773-1b77-425e-8200-66404ab29744 · outbound

This paper cites AprielGuard: A Lightweight Safety Classifier for Enterprise AI,.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B AprielGuard: A Lightweight Safety Classifier for Enterprise AI,

Reference 7

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source=pdf_text observed=2026-08-02T14:52:12.540363Z digest=sha256:191403d0a4dd82c92aba2f0f0a6fae5a385a7693da42d029acd4fd5b386398ee

Observation 8de2d996-0ef0-4a84-929d-789e0909af53 · outbound

This paper cites OWASP Top 10 for Large Language Model Applications,.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B OWASP Top 10 for Large Language Model Applications,

Reference 8

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source=pdf_text observed=2026-08-02T14:52:12.545108Z digest=sha256:6137dc3f2772073940260aa9449e5ecb51db2853591b1f04afe28b398761d8c3

Observation 1ef3c163-70bd-4ad0-b7ac-f9b0bae8b292 · outbound

This paper cites Domain Shift Amplifies False Positive Rates in LLM Safety Classifiers: Evidence from Financial-Services Deployments,.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B Domain Shift Amplifies False Positive Rates in LLM Safety Classifiers: Evidence from Financial-Services Deployments,

Reference 9

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source=pdf_text observed=2026-08-02T14:52:12.549268Z digest=sha256:d801718c4356dd530521af8ae91876033e3b0017501084a5b139bac4ad8020c5

Observation 8ed5b653-f27b-4773-9f23-f0da7c00fae5 · outbound

This paper cites PINT: Prompt Injection Test,.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B PINT: Prompt Injection Test,

Reference 10

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source=pdf_text observed=2026-08-02T14:52:12.553455Z digest=sha256:478a1c5b9cc7836e5a8cb04941654a78d6f9619425fa3c9e32b4d7a38c222c34

Observation b96e1c78-9060-44b1-8d8a-4f50130564ce · outbound

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

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B Ignore Previous Prompt: Attack Techniques For Language Models

Reference 11

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source=pdf_text observed=2026-08-02T14:52:12.557207Z digest=sha256:0a6a2b66740fca1e40235f715d6cbe90327e41db1a9cc313d267794645700a27

Observation e6dc450b-2587-40ad-8e94-f381050cfa06 · outbound

This paper cites Gandalf: Prompt Extraction Challenge,.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B Gandalf: Prompt Extraction Challenge,

Reference 12

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source=pdf_text observed=2026-08-02T14:52:12.562062Z digest=sha256:b530819944a0f910fbbf0d2e8c5f530312d5da57bd8a5293a8f13312054313f4

Observation b699f868-d796-46df-add4-9f195bfc5f19 · outbound

This paper cites Mosscap: Escalating-Defense Prompt-Injection Benchmark,.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B Mosscap: Escalating-Defense Prompt-Injection Benchmark,

Reference 13

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source=pdf_text observed=2026-08-02T14:52:12.566222Z digest=sha256:b8ed02df6010449f8494b9d7f3bd0e63a1ad9d907cc5fe49a8e8dd01e4b596f9

Observation 0c1ab8a3-1312-4bf6-a06d-24d07b79121e · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 14

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source=pdf_text observed=2026-08-02T14:52:12.570231Z digest=sha256:73840c334be59927459fc7df82faefe1b67fcd83d6cc38402fe3d7dfe031b66c

Observation d84c92da-2222-42a2-8752-5f263b3c9b38 · outbound

This paper cites AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications

Reference 15

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source=pdf_text observed=2026-08-02T14:52:12.574580Z digest=sha256:c32481b3d9502542ccd38dd21234800fbe8fca116012757ca8cfc241705b0bd9

Observation f2fbe15c-2b55-4ad7-b185-e424e927d304 · outbound

This paper cites LLM-Mediated Domain-Specific Voice Agents: The Case of TextileBot.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B LLM-Mediated Domain-Specific Voice Agents: The Case of TextileBot

Reference 16

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source=pdf_text observed=2026-08-02T14:52:12.578738Z digest=sha256:b43488d070ffe22006d2bcad363b9bff44fdc26c5e9e582fc82eb5193ebe6d8e

Observation d3c15edd-cf71-4f14-ac06-637c4675ff21 · outbound

This paper cites HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal

Reference 17

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source=pdf_text observed=2026-08-02T14:52:12.582991Z digest=sha256:557a0bffee55c25fdf8ab7fb5364c5fdc800981679ba803838a9cfe9721ca8af

Observation 3f1a0b64-2096-4c6d-8cce-6397fee83a69 · outbound

This paper cites Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!

Reference 18

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source=pdf_text observed=2026-08-02T14:52:12.587112Z digest=sha256:739f6fb5be7bffa9a2e778def68df99c0bff0d58f9ef36e424a84f8395cce73f

Observation 048e4edc-5e2d-46cd-aa71-38c0bc50b6ab · outbound

This paper cites ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation

Reference 19

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source=pdf_text observed=2026-08-02T14:52:12.591220Z digest=sha256:b081539fea95eb6b8235b0b76c23ac6bb30e781b021d2b94a5e7f0feb3615c36

Observation 7f123c3d-e33c-4331-9ff0-0f7a25b04ebe · outbound

This paper cites BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset,.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset,

Reference 20

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source=pdf_text observed=2026-08-02T14:52:12.594827Z digest=sha256:90eac21a3e156d65ee7c63ffd6caf22614bede7c2a7baa770050b553a157322f

Observation 1bd7fe88-6b44-44b1-9e7c-c53df2ebe41c · outbound

This paper cites SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models

Reference 21

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source=pdf_text observed=2026-08-02T14:52:12.598561Z digest=sha256:34008c43dc7a5908934fa2a9f4cad970694ea45fc09ad5ff9645301df1e810ca

Observation 9f208a3a-3e98-4988-bd5a-a9a9a43da872 · outbound

This paper cites SimpleSafetyTests: a Test Suite for Identifying Critical Safety Risks in Large Language Models.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B SimpleSafetyTests: a Test Suite for Identifying Critical Safety Risks in Large Language Models

Reference 22

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source=pdf_text observed=2026-08-02T14:52:12.602287Z digest=sha256:791534a2270edd901f26d6bd1abe89943999f21fd41a9638ec98a7a12ecb17ad

Observation af7225e6-0900-4190-b5cf-68831d5081bd · outbound

This paper cites AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents

Reference 23

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source=pdf_text observed=2026-08-02T14:52:12.605850Z digest=sha256:e9c4121f3add3c2991b3575f55e640b62cce18b4c7c64bb3c25825978410aefe

Observation f1520880-211c-44f5-b112-f2f0bff72d37 · outbound

This paper cites Probable Inference, the Law of Succession, and Statistical Inference,.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B Probable Inference, the Law of Succession, and Statistical Inference,

Reference 24

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source=pdf_text observed=2026-08-02T14:52:12.609376Z digest=sha256:89d00e6734338067a55914fe477dc58520ee9739e496ea0335d8e60901bd5659

Observation 5ae4f87f-55bd-4dba-8461-76c2976be57e · outbound

This paper cites The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning

Reference 25

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source=pdf_text observed=2026-08-02T14:52:12.612861Z digest=sha256:651256c778ec3b869072778a6e31afb7f98e3e59e359d72e105ffde9944fed9e

Observation 5266b85b-aef4-49b9-8186-17bb32c29e55 · outbound

This paper cites Investment Adviser Code of Ethics,.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B Investment Adviser Code of Ethics,

Reference 26

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source=pdf_text observed=2026-08-02T14:52:12.616537Z digest=sha256:032723c5b86a7db6819694e3cc153ded0e2dcffd94b83939463fb05accbfadea

Observation 158dd0a9-26ca-4285-8e8d-ac31c00b65bc · outbound

This paper cites Conduct of Business Sourcebook (COBS),.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B Conduct of Business Sourcebook (COBS),

Reference 27

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source=pdf_text observed=2026-08-02T14:52:12.620450Z digest=sha256:f9a9fba2c12c1ac7e0fa1ee7274f2286048de31847be85aa43295d27f8bace89

Observation 87f50105-aa15-4d34-9fe0-bcf6efe72e2b · outbound

This paper cites Markets in Financial Instruments Directive II (MiFID II),.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B Markets in Financial Instruments Directive II (MiFID II),

Reference 28

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source=pdf_text observed=2026-08-02T14:52:12.623954Z digest=sha256:fdedd6631ff47e217d0999ce309b7b7fac2838132c55587a4812d887add8d5e5

Observation f5c94133-41b8-4610-bb03-19e00d8f2493 · outbound

This paper cites Artificial Intelligence Act,.

Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B Artificial Intelligence Act,

Reference 29

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source=pdf_text observed=2026-08-02T14:52:12.628404Z digest=sha256:1ed7a678bcf0ae42d474244a4c7b1d51cd0bcf91d1193da509df120abb6ebadc

Pith citing papers

No inbound Pith citation observations are available.