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Paper Citation Record · LEDGER

Challenges and Applications of Large Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 55 inbound Pith citation observations for arXiv:2307.10169.

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

pith.paper-citation-record.v1
2307.10169 v1

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 55 of 55 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:36:44.065396Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T09:59:45.738392Z

Reference resolution

0 of 0 outbound references displayed

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Outbound references

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Pith citing papers

Observation 42660317-1616-4802-961e-720ec8df04d2 · inbound

Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations cites this paper.

Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations Challenges and Applications of Large Language Models

Reference 66

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arxiv_id, observed 2026-05-14T22:34:15.756417Z

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source=arxiv_source observed=2026-05-14T22:34:15.638114Z digest=sha256:726c20a2e648f5bfc26791542b93f3a9b6cc9672fd8df993e8d4c84831dd68e6

Observation a1b313e3-fd56-442b-8a43-618194cb7e18 · inbound

Large Language Models: A Survey cites this paper.

Large Language Models: A Survey Challenges and Applications of Large Language Models

Reference 209

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arxiv_id, observed 2026-05-11T15:22:55.952990Z

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source=pdf_text observed=2026-05-11T15:22:54.023279Z digest=sha256:1205f6aeff444046a1772fb8aab45b7d78713df69657c4fe99deb2af79daf6cb

Observation 35a91e3d-920a-4546-9ef8-ad20949a2dc1 · inbound

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code cites this paper.

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code Challenges and Applications of Large Language Models

Reference 195

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source=arxiv_source observed=2026-05-10T17:34:42.565806Z digest=sha256:48c7752624ac035f1fbee0c18554f6ca91878fdca62df547664ad437b9691ceb

Observation 6fb7fd57-cd1d-4bc8-9a1a-14e55b7f361d · inbound

Preserving Knowledge in Large Language Model with Model-Agnostic Self-Decompression cites this paper.

Preserving Knowledge in Large Language Model with Model-Agnostic Self-Decompression Challenges and Applications of Large Language Models

Reference 25

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arxiv_id, observed 2026-05-24T00:13:39.560059Z

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source=arxiv_source observed=2026-05-24T00:09:52.093810Z digest=sha256:071750f19364176ac930318d4e7eef5836efefdb7b7667ce9e43c813717b0699

Observation 241a7c39-5c31-4ae5-98f2-37fbe4ed47d1 · inbound

AI Governance through Markets cites this paper.

AI Governance through Markets Challenges and Applications of Large Language Models

Reference 81

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source=pdf_text observed=2026-08-10T04:36:44.065396Z digest=sha256:209cd71cac9b02839ee37dbf8ffb1f5fd4b49ef0a16cdb9cd29e0605dac0bf6d

Observation 68002f0a-4a22-429c-8857-d77a56330f24 · inbound

To Code or not to Code? Adaptive Tool Integration for Math Language Models via Expectation-Maximization cites this paper.

To Code or not to Code? Adaptive Tool Integration for Math Language Models via Expectation-Maximization Challenges and Applications of Large Language Models

Reference 19

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source=arxiv_source observed=2026-08-09T18:07:52.160355Z digest=sha256:fac0f8f1c75d7f9998f6bff424fcdc5ca00396e36ae753b5ce40e92117e320f0

Observation 21c9d41a-c017-4551-9245-53e35e6eb829 · inbound

Dynamic benchmarking framework for LLM-based conversational data capture cites this paper.

Dynamic benchmarking framework for LLM-based conversational data capture Challenges and Applications of Large Language Models

Reference 14

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source=pdf_text observed=2026-08-09T12:15:05.274234Z digest=sha256:fef228706d3dfec5103dabab6c726ae123b675562aec7001c998e9661819cd49

Observation 81f76193-46b4-4c7e-bfbe-15ea25755cd8 · inbound

Boosting Self-Efficacy and Performance of Large Language Models via Verbal Efficacy Stimulations cites this paper.

Boosting Self-Efficacy and Performance of Large Language Models via Verbal Efficacy Stimulations Challenges and Applications of Large Language Models

Reference 10

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source=pdf_text observed=2026-08-08T14:46:41.045500Z digest=sha256:9705c654e3ec6ad1c77a900c8daf5a1072a1a92d1fd46a8526383dee2b6e8017

Observation ed83df33-e554-4ed4-a75b-022cfe10914d · inbound

SelfElicit: Your Language Model Secretly Knows Where is the Relevant Evidence cites this paper.

SelfElicit: Your Language Model Secretly Knows Where is the Relevant Evidence Challenges and Applications of Large Language Models

Reference 25

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source=arxiv_source observed=2026-08-07T23:49:14.531294Z digest=sha256:639ed5517bba0aa52a2b7610d29b92057cc34a9ea78e0746aa3bc58c44be336f

Observation 80a27dc0-f98f-4787-ae07-e01ad4f95f90 · inbound

Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languages cites this paper.

Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languages Challenges and Applications of Large Language Models

Reference 68

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source=pdf_text observed=2026-08-07T21:10:57.272115Z digest=sha256:dd4001cff00f67169e598d015205378944c25ce3a8c61b3e4e484509b117bb52

Observation 60bd0767-dcfe-43c8-9814-479d7479a2e5 · inbound

SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning cites this paper.

SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning Challenges and Applications of Large Language Models

Reference 8

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source=pdf_text observed=2026-05-23T01:27:33.123243Z digest=sha256:9422364080ce66934e7864cd16bb773af3ec25582afe1e74010c1eda616985f1

Observation d2f25941-c9ad-497e-9cfb-ae67c92c6063 · inbound

LFTF: Locating First and Then Fine-Tuning for Mitigating Gender Bias in Large Language Models cites this paper.

LFTF: Locating First and Then Fine-Tuning for Mitigating Gender Bias in Large Language Models Challenges and Applications of Large Language Models

Reference 29

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source=arxiv_source observed=2026-08-07T15:22:14.837529Z digest=sha256:780a29b9243643d111dfe5720a27af6608afc9ad326610a2117e8088ababb9ff

Observation 98795094-409e-4c76-8392-426c594f8830 · inbound

InFact: Informativeness Alignment for Improved LLM Factuality cites this paper.

InFact: Informativeness Alignment for Improved LLM Factuality Challenges and Applications of Large Language Models

Reference 25

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source=arxiv_source observed=2026-08-07T13:59:01.412128Z digest=sha256:613ca6abb13d571e5b98683ef2c257431b0f691447a40e1118ce8414b3e685d1

Observation 8fb13176-a1aa-408e-8a83-e24201382f23 · inbound

CXXCrafter: An LLM-Based Agent for Automated C/C++ Open Source Software Building cites this paper.

CXXCrafter: An LLM-Based Agent for Automated C/C++ Open Source Software Building Challenges and Applications of Large Language Models

Reference 26

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source=pdf_text observed=2026-08-07T13:46:07.664498Z digest=sha256:47b19d0f6499c686801b880992f7604a4f5868ef147bd9b8ed733c0fee2ebc23

Observation 9715ff79-b118-45c2-a83e-1d30d446f897 · inbound

AnchorAttention: Difference-Aware Sparse Attention with Stripe Granularity cites this paper.

AnchorAttention: Difference-Aware Sparse Attention with Stripe Granularity Challenges and Applications of Large Language Models

Reference 10

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source=arxiv_source observed=2026-08-07T12:50:53.416097Z digest=sha256:93cd3e8af642234ff8f92b88c5f2e0cc8086d7cca4c28160974a7aefceb5ecbc

Observation 3ff0d59d-4d6b-45b2-a638-e7ff03083d48 · inbound

DLM-One: Diffusion Language Models for One-Step Sequence Generation cites this paper.

DLM-One: Diffusion Language Models for One-Step Sequence Generation Challenges and Applications of Large Language Models

Reference 16

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source=pdf_text observed=2026-08-07T12:12:28.609834Z digest=sha256:6e35085c34e8a5f87aa73c843a7fb542f2fc4b050db43bbcf294ceeb796084ed

Observation caa62ccc-1abb-4958-bcc5-1c7c78906114 · inbound

Evaluation of LLMs for mathematical problem solving cites this paper.

Evaluation of LLMs for mathematical problem solving Challenges and Applications of Large Language Models

Reference 23

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source=pdf_text observed=2026-08-07T12:11:03.124472Z digest=sha256:b9433973ae537ae50ebc8ad94f92106544e1251589a5f0429f85d18c969596ca

Observation a80b1263-79c4-44ce-a641-64f3581c4dd5 · inbound

From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs cites this paper.

From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs Challenges and Applications of Large Language Models

Reference 17

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source=pdf_text observed=2026-08-07T00:35:00.344744Z digest=sha256:992b472c2adea0a3cd8458019d89fd86665eef1d8f607b95d0e61f2a6b4b854b

Observation ac902d58-f816-41f7-a2e2-0bbef8ce0ba2 · inbound

PhantomHunter: Detecting Unseen Privately-Tuned LLM-Generated Text via Family-Aware Learning cites this paper.

PhantomHunter: Detecting Unseen Privately-Tuned LLM-Generated Text via Family-Aware Learning Challenges and Applications of Large Language Models

Reference 21

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source=arxiv_source observed=2026-08-06T23:58:07.013592Z digest=sha256:a73dfac6838d6adb443defb1d3c9c2ef5d45a35eba73a603aff6ab086929eb97

Observation 78228841-48af-4eed-9109-bd4175f41806 · inbound

Hallucination Detection with Small Language Models cites this paper.

Hallucination Detection with Small Language Models Challenges and Applications of Large Language Models

Reference 8

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source=pdf_text observed=2026-08-06T23:11:41.445965Z digest=sha256:43e0ae07997b4c53b505ef45ab5f4300b4a10bdd1a7144c3cb90aa1354e36afa

Observation d16fd78a-5405-4cde-811f-ac8c9f3b5dbf · inbound

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead cites this paper.

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead Challenges and Applications of Large Language Models

Reference 158

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source=pdf_text observed=2026-08-06T21:36:34.616735Z digest=sha256:ec704fcb0f4570d226992481d87510e19ffcc4e7196e68dd5383ce9415bdc7de

Observation 19f6fe0f-ffcc-4feb-8e60-57b79d689e53 · inbound

Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications cites this paper.

Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications Challenges and Applications of Large Language Models

Reference 79

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source=pdf_text observed=2026-08-06T21:07:10.854479Z digest=sha256:af80eae9b5963c0b2b8a05cf6fe7e47409ecd4fce62ed2b4ee3be4eb196712c4

Observation ccb68d94-07bf-4603-b041-3447f2597702 · inbound

A Comprehensive Review of Human Error in Risk-Informed Decision Making: Integrating Human Reliability Assessment, Artificial Intelligence, and Human Performance Models cites this paper.

A Comprehensive Review of Human Error in Risk-Informed Decision Making: Integrating Human Reliability Assessment, Artificial Intelligence, and Human Performance Models Challenges and Applications of Large Language Models

Reference 119

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source=pdf_text observed=2026-08-07T05:08:34.137997Z digest=sha256:e49ad95b7316e2202dd9457f2522cd2399713bc7624fd55f04ffc1923a1c7a19

Observation 8ef395f0-49f1-499f-ad42-4ec94f2a89cd · inbound

Is It Time To Treat Prompts As Code? A Multi-Use Case Study For Prompt Optimization Using DSPy cites this paper.

Is It Time To Treat Prompts As Code? A Multi-Use Case Study For Prompt Optimization Using DSPy Challenges and Applications of Large Language Models

Reference 7

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source=pdf_text observed=2026-08-06T20:09:17.632250Z digest=sha256:6f6bad7146a45eec1baadca6d7b60663bfaba0b321f23f3cba1e630f5792cd0a

Observation 150e3d69-682f-447d-b926-28046ef4f037 · inbound

Exploring the Limits of Model Compression in LLMs: A Knowledge Distillation Study on QA Tasks cites this paper.

Exploring the Limits of Model Compression in LLMs: A Knowledge Distillation Study on QA Tasks Challenges and Applications of Large Language Models

Reference 8

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source=arxiv_source observed=2026-08-06T18:42:07.256439Z digest=sha256:76237a21ccad399f6d8c4946972e3567d7c7c83414f6b542c2119bb464e1c9ac

Observation 62010bf5-9951-496a-8fcb-813a7c7d3504 · inbound

Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques cites this paper.

Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques Challenges and Applications of Large Language Models

Reference 146

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source=pdf_text observed=2026-08-06T16:24:30.255613Z digest=sha256:a24908a80eae185926309dd722dfe7618ca9a40b902a3f009fab674e91a10c98

Observation 120d88d0-ddce-4ec5-b2de-ec651730f150 · inbound

Multi-Stage Prompt Inference Attacks on Enterprise LLM Systems cites this paper.

Multi-Stage Prompt Inference Attacks on Enterprise LLM Systems Challenges and Applications of Large Language Models

Reference 7

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source=pdf_text observed=2026-08-06T15:32:51.106135Z digest=sha256:65854744bca2e253b121eaa497c5336e1fd812a52ae3c4bb6c233b9100cfdf4b

Observation 2764dcfb-04ad-4bec-bc39-b1f42ee2eb62 · inbound

LOCOFY Large Design Models -- Design to code conversion solution cites this paper.

LOCOFY Large Design Models -- Design to code conversion solution Challenges and Applications of Large Language Models

Reference 5

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source=pdf_text observed=2026-08-06T15:20:34.360486Z digest=sha256:e1c1ab32c6312bdfb0b22cf3a8b0a63ef98c9f8888f2bc28d67484385ade7a77

Observation 16c5e78a-5b62-4fea-9144-3a5ce06dd795 · inbound

The Impact of Fine-tuning Large Language Models on Automated Program Repair cites this paper.

The Impact of Fine-tuning Large Language Models on Automated Program Repair Challenges and Applications of Large Language Models

Reference 2023

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source=pdf_text observed=2026-08-06T13:56:43.241891Z digest=sha256:df4a95b209916462044b30e9689bf44f9607bbfc18f9616a82171bde07db13aa

Observation 81935c7d-280f-4a0e-91e8-f1b27fcffd80 · inbound

What Language(s) Does Aya-23 Think In? How Multilinguality Affects Internal Language Representations cites this paper.

What Language(s) Does Aya-23 Think In? How Multilinguality Affects Internal Language Representations Challenges and Applications of Large Language Models

Reference 8

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source=pdf_text observed=2026-08-06T13:48:10.006473Z digest=sha256:3fcceb6a0ce4e4e109dfe739e5da247946d42591dbabb9a84465759e866b5a16

Observation bfbcc001-fb20-4b23-b8f1-d1fd16215564 · inbound

LeakyCLIP: Extracting Training Data from CLIP cites this paper.

LeakyCLIP: Extracting Training Data from CLIP Challenges and Applications of Large Language Models

Reference 19

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arxiv_id, observed 2026-05-22T12:34:52.521363Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-22T12:31:50.876655Z digest=sha256:f1d4b95090b56db3bd5cdcfe3fc48291a9df2381a075203614158f16abc29dcb

Observation 143b342c-80e6-45dc-83e4-2c4e6e928ea6 · inbound

Insights into User Interface Innovations from a Design Thinking Workshop at deRSE25 cites this paper.

Insights into User Interface Innovations from a Design Thinking Workshop at deRSE25 Challenges and Applications of Large Language Models

Reference 9

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source=pdf_text observed=2026-08-05T16:15:38.341862Z digest=sha256:a5912e38ec845a5b5efcafc198b475b37a0eeba086802367756f828219855646

Observation f9073330-54fb-41c9-b838-e9fa5d460af3 · inbound

Enhancing Robustness of Autoregressive Language Models against Orthographic Attacks via Pixel-based Approach cites this paper.

Enhancing Robustness of Autoregressive Language Models against Orthographic Attacks via Pixel-based Approach Challenges and Applications of Large Language Models

Reference 8

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source=arxiv_source observed=2026-08-05T14:34:17.588895Z digest=sha256:4affbc8eaa4f96de37089b82c6270d841dfcbecc07a09c6ab799737c1c59b333

Observation 5f25851f-02cd-448e-a791-9be3cf8900a7 · inbound

Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models cites this paper.

Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models Challenges and Applications of Large Language Models

Reference 5

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source=pdf_text observed=2026-08-05T14:22:56.777525Z digest=sha256:a4040cc58027aa4051afe4d57bd3e8953ecf0b304b0ac48b0944933efff9efaf

Observation 2a2105fd-c14b-4207-a658-4c2051cc8d3f · inbound

Psychologically Enhanced AI Agents cites this paper.

Psychologically Enhanced AI Agents Challenges and Applications of Large Language Models

Reference 13

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source=pdf_text observed=2026-08-05T10:17:48.680902Z digest=sha256:5f070424d95e2204c58858919c7f76bfd6bc35841e2e3f16b9bf7eff556e5b97

Observation 67651c30-a3da-4feb-ac8a-ceaf6a0aa93b · inbound

Backdoor Samples Detection Based on Perturbation Discrepancy Consistency in Pre-trained Language Models cites this paper.

Backdoor Samples Detection Based on Perturbation Discrepancy Consistency in Pre-trained Language Models Challenges and Applications of Large Language Models

Reference 3

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source=pdf_text observed=2026-08-05T13:44:38.382758Z digest=sha256:50854d08d1ca5ce1240913c39dafc201b740e13c50cfe61099b2846af9662328

Observation 5b73fe14-63c2-4979-b4e8-47f6de00b859 · inbound

What Is The Political Content in LLMs' Pre- and Post-Training Data? cites this paper.

What Is The Political Content in LLMs' Pre- and Post-Training Data? Challenges and Applications of Large Language Models

Reference 20

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arxiv_id, observed 2026-05-18T12:41:22.896613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-18T12:36:48.153588Z digest=sha256:0257a7c2ca5a126df3a57202a31723111cdab5baaaed86d41e3bba6ef3ce056e

Observation c2c51ff5-6b8c-4779-ba10-d78107a9cc50 · inbound

CacheTrap: Unveiling a Stealthier Gray-Box Trojan against LLMs cites this paper.

CacheTrap: Unveiling a Stealthier Gray-Box Trojan against LLMs Challenges and Applications of Large Language Models

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-17T04:14:00.215360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-17T04:13:13.231293Z digest=sha256:e43ecc2dbb31b67724c7b2c652f4e0b8f73aef0fad938e9b5fb74e519fd8fffc

Observation 5fd8fbc7-9b4f-4281-8076-a93656c3db71 · inbound

Real Faults in Model Context Protocol (MCP) Software: a Comprehensive Taxonomy cites this paper.

Real Faults in Model Context Protocol (MCP) Software: a Comprehensive Taxonomy Challenges and Applications of Large Language Models

Reference 86

Resolution
unresolved
no resolver link, observed 2026-08-04T05:55:44.667052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T05:55:44.667052Z digest=sha256:a9ab113fcb251fb5c6fa8a9161260b655249bd1be7a3015f824be7bbc1769143

Observation 0592d129-0a65-4a13-a39d-cf91a5573e10 · inbound

Retrieval-Augmented Large Language Models for Evidence-Informed Guidance on Cannabidiol Use in Older Adults cites this paper.

Retrieval-Augmented Large Language Models for Evidence-Informed Guidance on Cannabidiol Use in Older Adults Challenges and Applications of Large Language Models

Reference 60

Resolution
verified exact
arxiv_id, observed 2026-05-16T14:12:58.602633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-16T14:12:21.237364Z digest=sha256:9a2219c339f2c93fe3124d4acfe1ff2d637128bac497b7bd9971e9841d6e3765

Observation 33e68e1a-3f4c-49d9-9ac2-549279753d55 · inbound

NL2SQLBench: A Modular Benchmarking Framework for LLM-Enabled NL2SQL Solutions cites this paper.

NL2SQLBench: A Modular Benchmarking Framework for LLM-Enabled NL2SQL Solutions Challenges and Applications of Large Language Models

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:50:59.253903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T15:48:27.632459Z digest=sha256:6037ad1d0bd812a1326e597d3b6f8ace58481f8a5c625316f2a6696099eaad8b

Observation aea58b42-45b8-479f-9d0f-0ffb9a29cf81 · inbound

SAW-INT4: System-Aware 4-Bit KV-Cache Quantization for Real-World LLM Serving cites this paper.

SAW-INT4: System-Aware 4-Bit KV-Cache Quantization for Real-World LLM Serving Challenges and Applications of Large Language Models

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-10T03:14:08.308026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T03:09:51.453839Z digest=sha256:c09dc22a28139159cbaf3ebcd62a07781f56d3585bb61ef237a12ebf8cc86aef

Observation bd95cc2b-d59b-4c31-a720-0e9f0168833c · inbound

SplitFT: An Adaptive Federated Split Learning System For LLMs Fine-Tuning cites this paper.

SplitFT: An Adaptive Federated Split Learning System For LLMs Fine-Tuning Challenges and Applications of Large Language Models

Reference 7

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T09:26:25.188111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-07T11:05:54.779407Z digest=sha256:e044f8f3e9f344c972c2eb3b77de8a36418c054a01e3ba9aad6cd52f279636ad

Observation db806cf1-10e6-4a97-b525-74f6157cef81 · inbound

U-Define: Designing User Workflows for Hard and Soft Constraints in LLM-Based Planning cites this paper.

U-Define: Designing User Workflows for Hard and Soft Constraints in LLM-Based Planning Challenges and Applications of Large Language Models

Reference 48

Resolution
verified exact
arxiv_id, observed 2026-05-09T06:35:39.073134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-08T18:19:55.849451Z digest=sha256:a451bfd4825ec2f4c7301f75726fbdc4e8f4054ab9c2eacb7a39eafd56f51bbc

Observation dba94353-2723-4571-8794-b75fc9f7f641 · inbound

RouteHijack: Routing-Aware Attack on Mixture-of-Experts LLMs cites this paper.

RouteHijack: Routing-Aware Attack on Mixture-of-Experts LLMs Challenges and Applications of Large Language Models

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-11T15:46:18.935235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-09T19:22:00.217729Z digest=sha256:58856800b5ddea35367c3d834eb5151873c8545c00e2178bbcd0f68c43be95a4

Observation 8c80b0a8-832b-403f-a782-a998c1161e72 · inbound

Vocabulary Hijacking in LVLMs: Unveiling Critical Attention Heads by Excluding Inert Tokens to Mitigate Hallucination cites this paper.

Vocabulary Hijacking in LVLMs: Unveiling Critical Attention Heads by Excluding Inert Tokens to Mitigate Hallucination Challenges and Applications of Large Language Models

Reference 23

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T05:16:24.353175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-12T05:15:34.156717Z digest=sha256:9b269291f71d71f48d519dae37f6544a00f9321914b2e53d6e1d71a6115f5772

Observation 3f9d52d3-515c-4929-8528-81eb56ae8101 · inbound

ACL-Verbatim: hallucination-free question answering for research cites this paper.

ACL-Verbatim: hallucination-free question answering for research Challenges and Applications of Large Language Models

Reference 2

Resolution
metadata mismatch
arxiv_id, observed 2026-05-21T04:54:35.964091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-21T04:54:03.937265Z digest=sha256:212c26bd4f0ca091260bb20004c92b2d21cb84ac18e70bb7a3b08d3b157c1371

Observation 7a2b16e7-1e48-4779-9948-b39bc9762923 · inbound

Towards Large Model Feature Coding cites this paper.

Towards Large Model Feature Coding Challenges and Applications of Large Language Models

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-07-01T15:15:47.196082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-30T17:16:38.347066Z digest=sha256:46f78ce8475ee9e0f3511d9110d99f3092a835148a723500fdfcb26c94e938ee

Observation bdf3685d-18d9-4adc-8c01-87dd710dfb76 · inbound

SuperValid: Capability-Aligned OOD Validation for Generalizable Downstream Scaling cites this paper.

SuperValid: Capability-Aligned OOD Validation for Generalizable Downstream Scaling Challenges and Applications of Large Language Models

Reference 5

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T13:03:26.408520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-29T12:57:31.313438Z digest=sha256:24c340620ec3a8bdbeaca39ab166b965dcaef8590d370dfc6a2a2daa5e1d48a5

Observation 600ae53e-0984-4d20-8978-92e84c311393 · inbound

On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners cites this paper.

On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners Challenges and Applications of Large Language Models

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-07-04T09:59:45.739846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-26T09:13:55.624609Z digest=sha256:9522acb64e613b5c1741f82ad6e884074b5318a561a63bf171394a7ab05a76e7

Observation b6ed76fc-291a-4d43-be45-a2401d7594b1 · inbound

Agentic and Generative AI for Open-Source Intelligence and Cyber Investigations: Taxonomy, Evaluation, Challenges, and Future Directions cites this paper.

Agentic and Generative AI for Open-Source Intelligence and Cyber Investigations: Taxonomy, Evaluation, Challenges, and Future Directions Challenges and Applications of Large Language Models

Reference 57

Resolution
unresolved
no resolver link, observed 2026-07-12T03:57:47.560737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T03:57:47.560737Z digest=sha256:b8ed80f8075ab39367e28aa1f28b021ad4d952255fe3afde647cad92131609fd

Observation 94325d3d-9af1-4821-bac8-7556ecf53dd2 · inbound

Semantic Drift and the Stability of Operator Control in Reasoning-Class Decision Support Systems cites this paper.

Semantic Drift and the Stability of Operator Control in Reasoning-Class Decision Support Systems Challenges and Applications of Large Language Models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-07-14T15:43:24.809948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T15:43:24.809948Z digest=sha256:5d7a65b38f5eea6be47ca96601796e55a1946e0f4ff6f71413e24057779d7a6c

Observation d46f3ebd-914c-488a-8f6b-bef0aeb36972 · inbound

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model cites this paper.

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Challenges and Applications of Large Language Models

Reference 4

Resolution
unresolved
no resolver link, observed 2026-07-14T12:55:22.304868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T12:55:22.304868Z digest=sha256:e42d4c49f23cbfb05faa3aa394618100054f6b5887bb4f513c503b38b6ddc1d8

Observation 59dd257f-1a04-4805-af08-97d77543e8d6 · inbound

FastTPS: An Optimized Method for LLM Token Phase for AI accelerators cites this paper.

FastTPS: An Optimized Method for LLM Token Phase for AI accelerators Challenges and Applications of Large Language Models

Reference 6

Resolution
unresolved
no resolver link, observed 2026-07-14T06:16:09.070414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T06:16:09.070414Z digest=sha256:0358a3e51b638d02af9c3d011433c853dd5e9b226933c0a0b4aec7a78558dabc

Observation f1695a52-82c8-4f80-8166-af04f1ed617e · inbound

Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details cites this paper.

Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details Challenges and Applications of Large Language Models

Reference 190

Resolution
unresolved
no resolver link, observed 2026-08-05T15:25:40.166569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T15:25:40.166569Z digest=sha256:cb0bf27b8fa7d2c44eb793aeffea6c33f0f566b56a32f529dba37b3c4247be2e