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Paper Citation Record · LEDGER

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models

As of 8 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 2 inbound Pith citation observations for arXiv:2506.20251.

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

pith.paper-citation-record.v1
2506.20251 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:00:19.643921Z

measured 51 of 51 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T08:45:52.855783Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-15T02:13:30.051815Z

Reference resolution

49 of 49 outbound references displayed

  • verified exact2
  • verified fuzzy1
  • unresolved45
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5e0785be-b6c2-490b-8182-a791103ad961 · outbound

This paper cites QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs

Reference 3

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source=pdf_text observed=2026-08-06T23:00:19.487900Z digest=sha256:b300f995a9bd09669545b9d149f1758425dcb3846052aca698ebd6b636cc8e4c

Observation 4539ba0d-6f7a-472c-a27c-c1b85ec456a7 · outbound

This paper cites Quantifying the Capabilities of LLMs across Scale and Precision.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Quantifying the Capabilities of LLMs across Scale and Precision

Reference 4

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source=pdf_text observed=2026-08-06T23:00:19.492020Z digest=sha256:710e435bbefc910a5fc4e8dae7525f10a4317d2ae271a4f764a6fc0ba6b1c567

Observation 00875733-8dc6-4479-b87f-39693db4771b · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 5

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source=pdf_text observed=2026-08-06T23:00:19.495735Z digest=sha256:893201758b5ae4956d952348e609bf00cbd0ae17aa7bbeea906d6d80ba1e283b

Observation d9fbfd4e-e824-401d-b391-772d2b489a83 · outbound

This paper cites INT2.1: Towards Fine-Tunable Quantized Large Language Models with Error Correction through Low-Rank Adaptation.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models INT2.1: Towards Fine-Tunable Quantized Large Language Models with Error Correction through Low-Rank Adaptation

Reference 7

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source=pdf_text observed=2026-08-06T23:00:19.503155Z digest=sha256:756ba5ac44617a9d9d1ac16f853e89495307503a8764a80ec194c6af3cd82a0e

Observation fbfdd1f4-5006-4668-ad4d-d7defadad16f · outbound

This paper cites Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP

Reference 8

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source=pdf_text observed=2026-08-06T23:00:19.506346Z digest=sha256:a9f5bec3ef2e4e86bade6cfd610081ff41c9d65c8d5abb6456adf3c844559dd0

Observation 689babd4-9ad4-4521-843a-937a500f0bd1 · outbound

This paper cites TEQ: Trainable Equivalent Transformation for Quantization of LLMs.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models TEQ: Trainable Equivalent Transformation for Quantization of LLMs

Reference 9

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source=pdf_text observed=2026-08-06T23:00:19.509727Z digest=sha256:846dcc9e5e5d9e9dc3830412f9eac8a69a3ff6cd195ef76e5c50ff65d96fae27

Observation d33b3879-494b-40f1-a65a-20f6c9abcd64 · outbound

This paper cites UltraFeedback: Boosting Language Models with Scaled AI Feedback.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models UltraFeedback: Boosting Language Models with Scaled AI Feedback

Reference 11

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source=pdf_text observed=2026-08-06T23:00:19.516351Z digest=sha256:41b1b522587b2c3fa3941b85c9173f2b6dd5d25bbeecab0aa75df10eb6bac77b

Observation 9eda0494-dd20-4f21-be98-f0f98ace6a74 · outbound

This paper cites SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 12

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source=pdf_text observed=2026-08-06T23:00:19.519528Z digest=sha256:3009ae17e0b9fe1143c6605084b824dda56ab8b1d7b5dfe57038f8c4973679ec

Observation 8ad88c67-5de8-455f-90cc-bd4460508410 · outbound

This paper cites Enhancing Chat Language Models by Scaling High-quality Instructional Conversations.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Enhancing Chat Language Models by Scaling High-quality Instructional Conversations

Reference 13

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source=pdf_text observed=2026-08-06T23:00:19.522619Z digest=sha256:5a6dc826fb40f88a91d915aa22ddd22bfaafdd80bc882126e52e023328f3977f

Observation 1ae8183d-82e0-46cc-9bf1-0bb9076891e9 · outbound

This paper cites BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation

Reference 14

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source=pdf_text observed=2026-08-06T23:00:19.525721Z digest=sha256:23fa2764c49c44691cab19c114e169d4d5164526c3b7d9049ae88e595879b4c5

Observation 64ba23fe-bd76-43e3-8320-52f2e528e5f8 · outbound

This paper cites Exploiting LLM Quantization.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Exploiting LLM Quantization

Reference 15

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source=pdf_text observed=2026-08-06T23:00:19.528854Z digest=sha256:bbb27903d0b6f5502b6de651c0fdab50803a5917173ba3e28900bb6a0be910eb

Observation 58eaad20-086d-4325-9ae9-7323145700c7 · outbound

This paper cites Extreme Compression of Large Language Models via Additive Quantization.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Extreme Compression of Large Language Models via Additive Quantization

Reference 16

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Observation 3340f34a-0738-4f92-a433-a33eb67a963e · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 18

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source=pdf_text observed=2026-08-06T23:00:19.539701Z digest=sha256:1cd04c13d29805259794b092f397ee73443d0ad9333f327a5f3cd81e7eccdcb8

Observation 60016d0c-8bf3-48c2-b1e7-caa4e00fe9b7 · outbound

This paper cites LQ-LoRA: Low-rank Plus Quantized Matrix Decomposition for Efficient Language Model Finetuning.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models LQ-LoRA: Low-rank Plus Quantized Matrix Decomposition for Efficient Language Model Finetuning

Reference 19

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source=pdf_text observed=2026-08-06T23:00:19.542797Z digest=sha256:716d7634fcb56bc92a582476884dccbafcb503c7bd60fcb18578b3b065787888

Observation 716ca101-91a8-4787-99ac-ca851702663f · outbound

This paper cites LoRA+: Efficient Low Rank Adaptation of Large Models.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models LoRA+: Efficient Low Rank Adaptation of Large Models

Reference 20

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source=pdf_text observed=2026-08-06T23:00:19.546271Z digest=sha256:727eac739ad4ac639a075f9db27115c175227fa2a060f63ad2c35d873e470d84

Observation 0ca9828c-5f3b-459c-9652-10c4bb43c315 · outbound

This paper cites From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems

Reference 21

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source=pdf_text observed=2026-08-06T23:00:19.549520Z digest=sha256:763b2f01a4b7a8c2fcd9cef797a2697effb618a9f020edcf941a3a25e9f89662

Observation 56b4a5db-6959-4436-9fa4-fb83cdcf6016 · outbound

This paper cites Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression

Reference 22

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source=pdf_text observed=2026-08-06T23:00:19.552626Z digest=sha256:83c750c7d824da6b3c52064cd99ab29c15ff446f99b8a74b454eb2c04bee63bb

Observation 4e63ccb5-8ef6-4bcf-a40c-d785d3254895 · outbound

This paper cites Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation

Reference 23

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Observation 53dfb406-e291-4a4b-badf-f4e1a106241a · outbound

This paper cites SqueezeLLM: Dense-and-Sparse Quantization.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models SqueezeLLM: Dense-and-Sparse Quantization

Reference 24

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source=pdf_text observed=2026-08-06T23:00:19.558832Z digest=sha256:835c91a96ac88a930213e3a941d462d88cf44bbf919f95a86e3a13b6797e43a3

Observation e85c48a5-6869-4fa6-a91f-b814b854d16e · outbound

This paper cites QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference

Reference 25

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source=pdf_text observed=2026-08-06T23:00:19.562688Z digest=sha256:4fe7e4f907014973024c03dfd541631a34ba484b1e9011de31d7b397c2ba05db

Observation d6ce4aef-7da2-4931-a3a7-8f5f2d8ea19b · outbound

This paper cites QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models

Reference 27

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Observation 7581cd33-ceb0-42cc-8e90-1deb6d8272e1 · outbound

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

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal

Reference 28

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source=pdf_text observed=2026-08-06T23:00:19.573170Z digest=sha256:c9012f89fe7deec783719fb2feffb67fca49d275b37360c5062c8d076171034d

Observation a274cb67-87a7-462f-9018-4af02697adeb · outbound

This paper cites Outliers and Calibration Sets have Diminishing Effect on Quantization of Modern LLMs.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Outliers and Calibration Sets have Diminishing Effect on Quantization of Modern LLMs

Reference 29

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Observation 9d15065a-dc73-4f80-8dce-f8478c36e740 · outbound

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

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!

Reference 31

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Observation 6d222a10-dc3c-43b4-a92f-a14a9784836f · outbound

This paper cites Position: Understanding LLMs Requires More Than Statistical Generalization.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Position: Understanding LLMs Requires More Than Statistical Generalization

Reference 32

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source=pdf_text observed=2026-08-06T23:00:19.586821Z digest=sha256:e90e9f59947f55f6f6cc63dce1c3c4b8525bfe1526c64b83ea88a2bd48cdc25c

Observation 0996d68a-fd79-417b-a84d-e650e408bd8b · outbound

This paper cites PB-LLM: Partially Binarized Large Language Models.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models PB-LLM: Partially Binarized Large Language Models

Reference 33

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Observation 39d67b9d-8413-474a-82c7-dcfcc9e8db8e · outbound

This paper cites OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Reference 34

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source=pdf_text observed=2026-08-06T23:00:19.593559Z digest=sha256:9abe7e9bb9fb8a1dd4ebc89b3647c73f6945bc0f86e19396befb8fa64cd289a3

Observation 9349d506-9723-4722-8c1d-6ac5b156a399 · outbound

This paper cites A StrongREJECT for Empty Jailbreaks.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models A StrongREJECT for Empty Jailbreaks

Reference 35

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source=pdf_text observed=2026-08-06T23:00:19.597335Z digest=sha256:3bfd0c6c37dc4ac0f52c2f150753d7133311d0e80c3ebe2c29ebd79ef43431d3

Observation 498f0760-32de-46d1-ad71-287ca6a6e97c · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Gemma 2: Improving Open Language Models at a Practical Size

Reference 36

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source=pdf_text observed=2026-08-06T23:00:19.601286Z digest=sha256:492fe954d305c8211477733c6573880cbb7e1c906ad0ad4817896ecbd277ae18

Observation bfd6f8d0-9bf1-40e0-93b6-c105454220da · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 37

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source=pdf_text observed=2026-08-06T23:00:19.604475Z digest=sha256:e4794525ebe530af21ec4d28f8c92bc9057e1711ae29288fbda1318723e61394

Observation 9b293a47-23d1-4807-8d7a-b34cb4df8079 · outbound

This paper cites Zephyr: Direct Distillation of LM Alignment.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Zephyr: Direct Distillation of LM Alignment

Reference 38

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Observation 32bf6349-d0e6-4e50-bce4-50b815969fc6 · outbound

This paper cites Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 39

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Observation d1466df5-fb21-4e86-879e-5d9ad4bd481f · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 40

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source=pdf_text observed=2026-08-06T23:00:19.614654Z digest=sha256:2716aa3f6af0a6faee4d2f5d75a2c98843b5b68ad36fff3675a38b39678a781b

Observation 56838325-0136-47d9-a8c0-582bab515fe5 · outbound

This paper cites QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models

Reference 41

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source=pdf_text observed=2026-08-06T23:00:19.617930Z digest=sha256:1378e23a1eabb3918fb25df97c8ec6651fe7fa03eca1f1f6446fd0c0aab630a5

Observation 401a0149-7bef-4eb7-95a5-cff96ea5e6e1 · outbound

This paper cites OneBit: Towards Extremely Low-bit Large Language Models.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models OneBit: Towards Extremely Low-bit Large Language Models

Reference 42

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source=pdf_text observed=2026-08-06T23:00:19.621017Z digest=sha256:f0480341af581cc113519e74973bd4c1a86c98b37d78fb4dbedd9241fdd1f850

Observation 3c95b49e-d931-464c-98ec-bb89f3495c16 · outbound

This paper cites RPTQ: Reorder-based Post-training Quantization for Large Language Models.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models RPTQ: Reorder-based Post-training Quantization for Large Language Models

Reference 43

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source=pdf_text observed=2026-08-06T23:00:19.624008Z digest=sha256:bc9b677c8b06f0b445a2344b7fcf573bbd1bffebc06d4538a4752a6ecfcb8abb

Observation 0f2d9e24-efd8-43c9-bf37-44c75b1e2e61 · outbound

This paper cites AIR-Bench 2024: A Safety Benchmark Based on Risk Categories from Regulations and Policies.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models AIR-Bench 2024: A Safety Benchmark Based on Risk Categories from Regulations and Policies

Reference 44

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source=pdf_text observed=2026-08-06T23:00:19.627162Z digest=sha256:de26864b8ade7c40a06cc097bcb8bd91bfc186cbfd2014d3d21be6945411c13e

Observation 80a66c5d-3841-47f2-9391-cd8c2d36cf8c · outbound

This paper cites Activation Approximations Can Incur Safety Vulnerabilities Even in Aligned LLMs: Comprehensive Analysis and Defense.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Activation Approximations Can Incur Safety Vulnerabilities Even in Aligned LLMs: Comprehensive Analysis and Defense

Reference 45

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no resolver link, observed 2026-08-06T23:00:19.630547Z

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source=pdf_text observed=2026-08-06T23:00:19.630547Z digest=sha256:096db5af56513976a4bf4edcde466236d57ceb2794021387ca6a858604311163

Observation 0365a828-9cb1-4377-b751-7c28b563deeb · outbound

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

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 46

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source=pdf_text observed=2026-08-06T23:00:19.633676Z digest=sha256:7ec1bc78a648d16bff0159f050c3544d414f7932ee1e3540cec07889fb409e09

Observation 45ed98f6-7de5-4da7-a5fd-2cee95970f5f · outbound

This paper cites an unresolved cited work.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Unresolved cited work

Reference 47

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raw_fallback, observed 2026-08-06T23:00:20.059874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:00:19.637507Z digest=sha256:a6925826bf8c60cfa8f599c97982653903c6cc8938c42b6c343f828882dcf3b5

Observation db635d82-38aa-4af0-8cb0-13afb93fa4db · outbound

This paper cites ASRVanilla: Tested with system prompt:”You are a helpful assistant.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models ASRVanilla: Tested with system prompt:”You are a helpful assistant

Reference 48

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malformed identifier
raw_fallback, observed 2026-08-06T23:00:20.050712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:00:19.640521Z digest=sha256:c386408651c70562c4f6e9a935a4b06c40f15ab8fefc28566a153e940ebca1be

Observation 6a5ff4c0-fa57-478d-b2a6-dde65acec5ab · outbound

This paper cites an unresolved cited work.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Unresolved cited work

Reference 49

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raw_fallback, observed 2026-08-06T23:00:20.040409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:00:19.643921Z digest=sha256:2d0e0feca3514bc3e9326652341b5a4bd927f9772b0fafed4e9a0c7d8e0de46a

Observation 53031d22-f6a6-49d8-938f-7cc3cf6f8c47 · outbound

This paper cites JailbreakRadar: Comprehensive Assessment of Jailbreak Attacks Against LLMs.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models JailbreakRadar: Comprehensive Assessment of Jailbreak Attacks Against LLMs

Reference 2017

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no resolver link, observed 2026-08-06T23:00:19.513275Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T23:00:19.513275Z digest=sha256:37d332e51e365fa48c36cffbe0a0af439495c5ced4306261f15c6d1ee412f44a

Observation 7a90c41f-4898-42d3-86d6-c6a44e90120a · outbound

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

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models

Reference 2019

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no resolver link, observed 2026-08-06T23:00:19.566396Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T23:00:19.566396Z digest=sha256:72b6729dacd5a3df08543fca195a4fc605e91df39e249acad8784c1da9789285

Observation c5b92e23-f8ed-45b8-bf7e-95e66bc372b8 · outbound

This paper cites Instruction Tuning with GPT-4.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Instruction Tuning with GPT-4

Reference 2021

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no resolver link, observed 2026-08-06T23:00:19.579669Z

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source=pdf_text observed=2026-08-06T23:00:19.579669Z digest=sha256:15ba640e5069d06723eb0b7615b82f1433c2c28d5153bf8543a70c4d4e5b87d3

Observation b4a0c716-2058-454d-83ed-a637157748f5 · outbound

This paper cites HarmLevelBench: Evaluating Harm-Level Compliance and the Impact of Quantization on Model Alignment.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models HarmLevelBench: Evaluating Harm-Level Compliance and the Impact of Quantization on Model Alignment

Reference 2022

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no resolver link, observed 2026-08-06T23:00:19.499755Z

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source=pdf_text observed=2026-08-06T23:00:19.499755Z digest=sha256:e5fed77807c13fb1809df3eb8a2ceca2be6c0f4d7e08fe905e485894c47b194c

Observation f7021a1b-5078-4ed8-8cf5-53e483c72be2 · outbound

This paper cites GPT-4 Technical Report.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models GPT-4 Technical Report

Reference 2023

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no resolver link, observed 2026-08-06T23:00:19.480415Z

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source=pdf_text observed=2026-08-06T23:00:19.480415Z digest=sha256:5d03c199042ac8a76caf3891ba0c1a29f9a730b1bb3ea383d58b2c08325c1efb

Observation 5341d5a9-ed0c-4624-8130-c5348f4c79a9 · outbound

This paper cites PaLM 2 Technical Report.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models PaLM 2 Technical Report

Reference 2024

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source=pdf_text observed=2026-08-06T23:00:19.484466Z digest=sha256:c54a8dae0e256bb6badb55bd67660d2f21e8c0ee8f83024f84092c8e2d2e0416

Observation 730be733-a0db-446d-babf-ddc3c399ef90 · outbound

This paper cites Accessed: 2025-01-24.

Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models Accessed: 2025-01-24

Reference 2025

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verified fuzzy
raw_fallback, observed 2026-08-06T23:00:20.071040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:00:19.535729Z digest=sha256:9d612c0bd893b0ce95e4d726a967c0003630086278437be00cbb909803ee6d75

Pith citing papers

Observation 1a36ddfb-7141-4243-95c9-f0d1c6cd1d7f · inbound

Knowledge Beyond Language: Bridging the Gap in Multilingual Machine Unlearning Evaluation cites this paper.

Knowledge Beyond Language: Bridging the Gap in Multilingual Machine Unlearning Evaluation Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models

Reference 38

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verified exact
arxiv_id, observed 2026-05-15T02:13:30.053304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-15T02:13:17.039114Z digest=sha256:79bc7a68641457bd08423a6980f6a1b7d5d4330a0d449f711a28505ce126d845

Observation 49685228-f86e-4eb3-901e-86c011bb2c24 · inbound

Reliability Scaling Laws for Quantized Large Language Models cites this paper.

Reliability Scaling Laws for Quantized Large Language Models Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models

Reference 148

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source=arxiv_source observed=2026-07-14T08:45:52.855783Z digest=sha256:0732fb601e752dd3d11899f26292fb75262b4ebbda027f5b75852ba991d73733