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Paper Citation Record · LEDGER

Attacking Large Language Models with Projected Gradient Descent

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 33 inbound Pith citation observations for arXiv:2402.09154.

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

pith.paper-citation-record.v1
2402.09154 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 33 of 33 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:28:48.859908Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

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External citation measurements

5
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation cd7ed140-9c2e-4f02-bcf7-da743d17a18b · inbound

SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks cites this paper.

SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks Attacking Large Language Models with Projected Gradient Descent

Reference 23

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arxiv_id, observed 2026-05-14T17:11:00.698921Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-14T17:11:00.639293Z digest=sha256:efa69461c62b2cef6ad2112dd491b0787404e50af8ff3e5fd41fba756b32a945

Observation dcd6d2d5-77a7-4685-a090-86134a0fa128 · inbound

JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models cites this paper.

JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models Attacking Large Language Models with Projected Gradient Descent

Reference 13

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arxiv_id, observed 2026-05-15T06:08:05.581194Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-05-15T06:08:05.386345Z digest=sha256:ed1f33d2eada45e693d47909fcab24d67faa0a782af1706058505df0c7ddbc2e

Observation cc169369-d863-4cf4-b57c-c8a5fa95f46d · inbound

Jailbreak Attacks and Defenses Against Large Language Models: A Survey cites this paper.

Jailbreak Attacks and Defenses Against Large Language Models: A Survey Attacking Large Language Models with Projected Gradient Descent

Reference 29

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arxiv_id, observed 2026-05-15T02:20:44.639144Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-15T02:20:44.368219Z digest=sha256:6b5bc478a8de734c924cbd437c91af60de4a5580945501637dfae9797a7e1ed2

Observation 6448b82d-8a67-444c-98d4-f9be429bdc3f · inbound

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation cites this paper.

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation Attacking Large Language Models with Projected Gradient Descent

Reference 35

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source=pdf_text observed=2026-08-12T15:57:06.132379Z digest=sha256:8194a534ff8b60a388fafd9c1d86da030219233cd6fcc54d80df03bab235242d

Observation c2f0d227-a640-4fe8-ab2f-a9062f9a3346 · inbound

LIAR: Leveraging Inference Time Alignment (Best-of-N) to Jailbreak LLMs in Seconds cites this paper.

LIAR: Leveraging Inference Time Alignment (Best-of-N) to Jailbreak LLMs in Seconds Attacking Large Language Models with Projected Gradient Descent

Reference 19

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source=pdf_text observed=2026-08-11T20:53:36.949947Z digest=sha256:6628fd77f8a1514a7a54f022372e817f16bb92a0c9bbeeb8d19983680391ed40

Observation bfd9486a-9c6d-471f-9738-f76846e34efa · inbound

Improving the Robustness of the Projected Gradient Descent Method for Nonlinear Constrained Optimization Problems in Topology Optimization cites this paper.

Improving the Robustness of the Projected Gradient Descent Method for Nonlinear Constrained Optimization Problems in Topology Optimization Attacking Large Language Models with Projected Gradient Descent

Reference 44

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source=pdf_text observed=2026-08-11T18:42:33.351242Z digest=sha256:c0d17275b95a9b521600b59fbd4a6f4ad372e510891fec8f97f00628e851ea19

Observation 66422da4-9284-4434-adf0-a283237a2e44 · inbound

DiffusionAttacker: Diffusion-Driven Prompt Manipulation for LLM Jailbreak cites this paper.

DiffusionAttacker: Diffusion-Driven Prompt Manipulation for LLM Jailbreak Attacking Large Language Models with Projected Gradient Descent

Reference 7

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source=arxiv_source observed=2026-08-11T05:31:31.576750Z digest=sha256:ce8ed143b5714f5c7ae4a39fb5c3649d101fd59b51056e3911e93c73354cf498

Observation d32d0ce5-0404-4151-80cb-80fb5e2ceb31 · inbound

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs cites this paper.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Attacking Large Language Models with Projected Gradient Descent

Reference 47

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source=arxiv_source observed=2026-08-10T23:40:18.803840Z digest=sha256:224da7a13ac640e0a0d3186053306bd9ffa50287aee8c4a718756799b40bb2df

Observation a48861e3-38af-4701-99f2-bc6ad706a53b · inbound

Trojan Detection Through Pattern Recognition for Large Language Models cites this paper.

Trojan Detection Through Pattern Recognition for Large Language Models Attacking Large Language Models with Projected Gradient Descent

Reference 5

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source=pdf_text observed=2026-08-10T18:07:46.811659Z digest=sha256:c61881e97fbcb2b644e03aacc9a246fc14fd1bb018ad3e960c9abcdcab49731f

Observation e6060557-e7b2-4978-86ba-8095c8d1971a · inbound

MPLinker: Multi-template Prompt-tuning with Adversarial Training for Issue-commit Link Recovery cites this paper.

MPLinker: Multi-template Prompt-tuning with Adversarial Training for Issue-commit Link Recovery Attacking Large Language Models with Projected Gradient Descent

Reference 44

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source=pdf_text observed=2026-08-09T21:39:57.801536Z digest=sha256:d41a81986b4ad09d32f675644fb092be7269299b63c6737f16b58fbbc64bbcd4

Observation 9efbfee1-fe77-4e5f-b64e-5940d1f523d5 · inbound

Safety Reasoning with Guidelines cites this paper.

Safety Reasoning with Guidelines Attacking Large Language Models with Projected Gradient Descent

Reference 19

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no resolver link, observed 2026-08-08T23:50:35.711274Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T23:50:35.711274Z digest=sha256:73f8fa26fc6353d3189a54a38cf5e9f93b35bfba45c6efc71b60af96a38f04b0

Observation 227aab02-0700-4ca3-9c78-dc0bc8748524 · inbound

LLM-Safety Evaluations Lack Robustness cites this paper.

LLM-Safety Evaluations Lack Robustness Attacking Large Language Models with Projected Gradient Descent

Reference 23

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arxiv_id, observed 2026-05-23T01:27:21.305556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-23T01:26:45.402983Z digest=sha256:976b23cf223a1cea6ac9bdb17cc69d44e192b667e2abbf01d1594ce6b13d2b1d

Observation 5661be85-fa06-42da-8390-2496b559f216 · inbound

Edge-Based Learning for Improved Classification Under Adversarial Noise cites this paper.

Edge-Based Learning for Improved Classification Under Adversarial Noise Attacking Large Language Models with Projected Gradient Descent

Reference 9

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source=pdf_text observed=2026-08-16T10:28:48.859908Z digest=sha256:bcfff1b658a7d23d5994b168a15e77a92fede799eb658be14b6ee6d521509839

Observation 4d6006f2-0e8f-4cfe-9107-a73599998ffc · inbound

OET: Optimization-based prompt injection Evaluation Toolkit cites this paper.

OET: Optimization-based prompt injection Evaluation Toolkit Attacking Large Language Models with Projected Gradient Descent

Reference 9

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source=pdf_text observed=2026-08-16T04:38:39.361751Z digest=sha256:01b4b1310da4b7cdeb04cf5f36ed641852de5b4cb5bff4827d3489bed5c760f8

Observation 9298d76f-bc7f-4d4a-aa27-382ceecacf83 · inbound

Lifelong Safety Alignment for Language Models cites this paper.

Lifelong Safety Alignment for Language Models Attacking Large Language Models with Projected Gradient Descent

Reference 16

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source=pdf_text observed=2026-08-07T14:00:04.958799Z digest=sha256:4bc82411a99959de43b0c56f5548cd32c5a21558cc5dab361f9696b650365d5f

Observation d8e12a24-16cb-4e2d-abc5-24064a025fd8 · inbound

Benign-to-Toxic Jailbreaking: Inducing Harmful Responses from Harmless Prompts cites this paper.

Benign-to-Toxic Jailbreaking: Inducing Harmful Responses from Harmless Prompts Attacking Large Language Models with Projected Gradient Descent

Reference 17

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no resolver link, observed 2026-08-07T14:01:06.516291Z

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source=pdf_text observed=2026-08-07T14:01:06.516291Z digest=sha256:0c7cfd5a4175a0ee196ef8a5f4df38f74b0762c92286e441ae60a73e99d0cfeb

Observation fdebc9ff-d61f-4f92-a347-780d0fa2a544 · inbound

Enhancing the Safety of Medical Vision-Language Models by Synthetic Demonstrations cites this paper.

Enhancing the Safety of Medical Vision-Language Models by Synthetic Demonstrations Attacking Large Language Models with Projected Gradient Descent

Reference 7

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arxiv_id, observed 2026-05-19T10:57:16.512989Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation b604ea45-9d0f-4d8b-872c-dc0f7479c980 · inbound

SecurityLingua: Efficient Defense of LLM Jailbreak Attacks via Security-Aware Prompt Compression cites this paper.

SecurityLingua: Efficient Defense of LLM Jailbreak Attacks via Security-Aware Prompt Compression Attacking Large Language Models with Projected Gradient Descent

Reference 16

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source=arxiv_source observed=2026-08-07T00:51:13.446894Z digest=sha256:8a5e6e7d61cc503162ed2e1a1d7d53a4beb28f18b100bc634fcb47408de37e98

Observation 6bb50952-d1d9-413d-be5e-82a3066116a6 · inbound

FORTRESS: Frontier Risk Evaluation for National Security and Public Safety cites this paper.

FORTRESS: Frontier Risk Evaluation for National Security and Public Safety Attacking Large Language Models with Projected Gradient Descent

Reference 17

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source=pdf_text observed=2026-08-07T00:15:00.545572Z digest=sha256:5dc6a050e940d6b004dfebf12cb01d94385a20f20bb91dcc7d18dcb1a4f8dee2

Observation 457de37f-944c-47b4-9a23-eb3cfd5ba36c · inbound

NSFW-Classifier Guided Prompt Sanitization for Safe Text-to-Image Generation cites this paper.

NSFW-Classifier Guided Prompt Sanitization for Safe Text-to-Image Generation Attacking Large Language Models with Projected Gradient Descent

Reference 31

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source=pdf_text observed=2026-08-15T18:56:13.268391Z digest=sha256:60d3c41ce1fc765f04760fa65f0dad3a09cea26b0912565b8e6aec9f27d704fc

Observation 09cebd2c-7789-45e8-89f2-5eb8bf3a0b10 · inbound

SoK: A Comprehensive Security Analysis of Jailbreak Resilience in GPT and DeepSeek Models cites this paper.

SoK: A Comprehensive Security Analysis of Jailbreak Resilience in GPT and DeepSeek Models Attacking Large Language Models with Projected Gradient Descent

Reference 38

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no resolver link, observed 2026-08-06T23:20:56.204745Z

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source=pdf_text observed=2026-08-06T23:20:56.204745Z digest=sha256:d2468242eea70e55b93be16c15ca634c848862f73568a6f3ee0d3bdd240cd3f9

Observation abd52154-d50a-4659-b063-cc2bbd32eb46 · inbound

Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation cites this paper.

Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation Attacking Large Language Models with Projected Gradient Descent

Reference 26

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source=pdf_text observed=2026-08-05T20:31:33.071055Z digest=sha256:4a56c4e85119352d618af7100ede07328b5386fa251899c67518fa4a014914bd

Observation 6b47654b-2df4-4b6f-b4e5-9da9ec7c2479 · inbound

On Surjectivity of Neural Networks: Can you elicit any behavior from your model? cites this paper.

On Surjectivity of Neural Networks: Can you elicit any behavior from your model? Attacking Large Language Models with Projected Gradient Descent

Reference 20

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:00:48.402754Z digest=sha256:18aea295d508e92c2c6958d7b95d28dd2b0ec440a89d3e261ed79e03e9f982d2

Observation dbb98816-ca6a-40f5-96a0-89c93ed6d197 · inbound

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses cites this paper.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Attacking Large Language Models with Projected Gradient Descent

Reference 57

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source=pdf_text observed=2026-08-04T09:25:40.661962Z digest=sha256:9a39622dee2b1c0360aa0932c6fe09d06f523295bb0780681aed1a12c0c1a621

Observation 3431ab42-a099-4b3d-bbd3-9069a13ea693 · inbound

ASTRA: An Automated Framework for Strategy Discovery, Retrieval, and Evolution for Jailbreaking LLMs cites this paper.

ASTRA: An Automated Framework for Strategy Discovery, Retrieval, and Evolution for Jailbreaking LLMs Attacking Large Language Models with Projected Gradient Descent

Reference 13

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arxiv_id, observed 2026-05-18T01:55:38.083052Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-18T01:54:22.995178Z digest=sha256:8ab47a4e8ccbf3588bda2d10b1aadcd8f2aabb64a17cb8b002881fef3a938b92

Observation a1b178c8-724c-4bfe-b48e-4bcf2c02f008 · inbound

GRM: Utility-Aware Jailbreak Attacks on Audio LLMs via Gradient-Ratio Masking cites this paper.

GRM: Utility-Aware Jailbreak Attacks on Audio LLMs via Gradient-Ratio Masking Attacking Large Language Models with Projected Gradient Descent

Reference 9

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arxiv_id, observed 2026-05-11T08:21:01.267141Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-10T16:40:59.993298Z digest=sha256:13e81d2aa77a6c2b7b5c4b66904c0624e871e562582dac4d69144e9814955701

Observation f87d6bfa-6cb8-4a93-a63a-07c2da470cf5 · inbound

On the Hardness of Junking LLMs cites this paper.

On the Hardness of Junking LLMs Attacking Large Language Models with Projected Gradient Descent

Reference 14

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arxiv_id, observed 2026-05-11T17:21:10.187348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-08T17:38:32.028947Z digest=sha256:8e0b9ab942b590a18d54e5e8cd231c8d3658308dd139f3ab127a9e09a819270f

Observation 6c7792d8-2f4a-4dc1-8be3-a90d998c2bed · inbound

LLM-Agnostic Semantic Representation Attack cites this paper.

LLM-Agnostic Semantic Representation Attack Attacking Large Language Models with Projected Gradient Descent

Reference 44

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arxiv_id, observed 2026-05-12T08:21:24.234026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-12T01:14:08.629862Z digest=sha256:e0633e24b371a046ca525471b2d853b116f312e88c651b6525de3e5ab67a4217

Observation 209e1f92-6b78-4cd4-9587-1b0b917b5ad8 · inbound

REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations cites this paper.

REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations Attacking Large Language Models with Projected Gradient Descent

Reference 158

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arxiv_id, observed 2026-05-14T20:17:56.850770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-05-14T20:13:10.814899Z digest=sha256:ddb7c13d9ba9737e66183fb9576fc22a05b85c3401627574e9b5a76fbec639eb

Observation cbeeb290-95be-49a3-a155-f2d60800eb74 · inbound

Black-box, Adaptive, Efficient, Transferable, Harmful, Applicable... Attacks Are All You Need to Break LLMs cites this paper.

Black-box, Adaptive, Efficient, Transferable, Harmful, Applicable... Attacks Are All You Need to Break LLMs Attacking Large Language Models with Projected Gradient Descent

Reference 18

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arxiv_id, observed 2026-07-02T04:16:34.691368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-06-28T09:21:57.373862Z digest=sha256:7337a54f3f498e6a78441cd48c5a7cb1870c1f5cf8d495a7cb2503196c73ff12

Observation b9c5ddeb-a11c-435d-bbd1-7ad52573b1be · inbound

Beyond the Prompt: Jailbreaking Function-Calling LLMs via Simulated Moderation Traces cites this paper.

Beyond the Prompt: Jailbreaking Function-Calling LLMs via Simulated Moderation Traces Attacking Large Language Models with Projected Gradient Descent

Reference 34

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arxiv_id, observed 2026-07-02T11:46:54.860562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-07-02T11:37:02.538968Z digest=sha256:07c694650930a655d7a78e04a6ba00158b25643f4e702669ec48af24b5f38656

Observation c5232ba9-5422-459d-ba88-940486fce466 · inbound

Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Turn Attacks cites this paper.

Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Turn Attacks Attacking Large Language Models with Projected Gradient Descent

Reference 17

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source=pdf_text observed=2026-08-04T01:16:10.330416Z digest=sha256:23bb0850216b511c901ad5e02cce1d83ec254b2c1a9d5bf0b55aa03d3655b5f1

Observation 2f6fcc8f-0817-4044-8971-ddbf7e95922a · inbound

Position: It's Time to Optimize LLMs for Self-Consistency cites this paper.

Position: It's Time to Optimize LLMs for Self-Consistency Attacking Large Language Models with Projected Gradient Descent

Reference 51

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no resolver link, observed 2026-08-07T01:00:03.202299Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T01:00:03.202299Z digest=sha256:419ebaed01240d7a8390a11c2b163c0806091ee63dc42c82d40e3d0d351d3fd5