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Paper Citation Record · LEDGER

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors

As of 14 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2508.02997.

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

pith.paper-citation-record.v1
2508.02997 v3

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T04:50:11.457129Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

31 of 31 outbound references displayed

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  • unresolved31
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation 1b60e0a0-ab38-455e-89a3-2c32b7e908d3 · outbound

This paper cites Program Synthesis with Large Language Models.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors Program Synthesis with Large Language Models

Reference 1

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source=pdf_text observed=2026-08-06T04:50:09.167346Z digest=sha256:b1d9acb76363f07fb0d5fe55fae447eadbe16580d9d2f13b93a3540dfad795cc

Observation 625c3eb4-5062-4bab-8d07-44a6c839d1a8 · outbound

This paper cites Teaching Large Language Models to Self-Debug.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors Teaching Large Language Models to Self-Debug

Reference 2

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source=pdf_text observed=2026-08-06T04:50:09.233179Z digest=sha256:1e60fe92f7de4458695be1f4eecafb5db58477ce53497a70481c1eccc3349e47

Observation 6219e041-5cb9-4b36-903a-353e4645a0f6 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 3

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source=pdf_text observed=2026-08-06T04:50:09.340276Z digest=sha256:0d0507e806f6553824b3f2cd3aa5faf85bb92959d5bfe7404117230e7516618f

Observation b5451de2-b14f-4b1d-973f-b135398e3052 · outbound

This paper cites an unresolved cited work.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors Unresolved cited work

Reference 4

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source=pdf_text observed=2026-08-06T04:50:09.417530Z digest=sha256:9eca797908cd2f01a0545dbd976d97cf439ec80eeb356ad3412a4e18f30c2d19

Observation 2fc54e83-8c35-4f47-b74c-f90440e2a2f4 · outbound

This paper cites an unresolved cited work.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors Unresolved cited work

Reference 5

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source=pdf_text observed=2026-08-06T04:50:09.486827Z digest=sha256:b689c70b385df93114b99c095c62fc69ce8e82c9a4ae55c442850b94be911d59

Observation 08edfa25-c01c-4812-a319-06b0b81273b9 · outbound

This paper cites an unresolved cited work.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors Unresolved cited work

Reference 6

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source=pdf_text observed=2026-08-06T04:50:09.626530Z digest=sha256:db489be5047eda9d1dc796fd8c12d06b5b9b8cb4e02e4b281a0dbd416216b949

Observation 56f89262-56c3-4dc6-8d5f-6ce74f4bc4d0 · outbound

This paper cites ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation

Reference 7

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source=pdf_text observed=2026-08-06T04:50:09.670033Z digest=sha256:6d82c66e9df99442977c2e9714c47ea8fee0629f4a634cbd510518690aa1d5ae

Observation d253d973-85cf-4ae4-9af6-32510839fc5b · outbound

This paper cites InCoder: A Generative Model for Code Infilling and Synthesis.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors InCoder: A Generative Model for Code Infilling and Synthesis

Reference 8

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source=pdf_text observed=2026-08-06T04:50:09.738567Z digest=sha256:08b85cd178e77b396799db59f55b8a55c35161a50c61061dd57c11bce5b645d2

Observation 730f7699-356e-47ce-b280-176f9d036f8c · outbound

This paper cites 2024.Copilot.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors 2024.Copilot

Reference 9

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source=pdf_text observed=2026-08-06T04:50:09.804523Z digest=sha256:ed2f880691c85d316db26bb2afedcbcaa7702b111a9352c9c425751eb87aac19

Observation 0a8776ff-9193-467e-8a94-33e38674ba64 · outbound

This paper cites Code Llama: Open Foundation Models for Code.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors Code Llama: Open Foundation Models for Code

Reference 10

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source=pdf_text observed=2026-08-06T04:50:09.878397Z digest=sha256:fb79dfa7b89daeb8d3320c33ec83ae3bb203f9019433f3d40117b9a0d3158564

Observation c077f6bf-90b0-451f-8612-318b93611e64 · outbound

This paper cites DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

Reference 11

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source=pdf_text observed=2026-08-06T04:50:09.968783Z digest=sha256:bcbebe343104d7dfba184a2961cc9e9188b204511fb6cb9870d185556924f275

Observation c952b424-b620-4eef-b93d-bb4851a32b34 · outbound

This paper cites Measuring Coding Challenge Competence With APPS.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors Measuring Coding Challenge Competence With APPS

Reference 12

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source=pdf_text observed=2026-08-06T04:50:10.057478Z digest=sha256:9f4343a8d81d912b52e163127acecb9ae82ba11c1c38a3a4f2b018711d3f5580

Observation 68c2ba9f-832c-4508-bab7-f9af7d99fe18 · outbound

This paper cites SWE-bench: Can Language Models Resolve Real-World GitHub Issues?.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Reference 13

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source=pdf_text observed=2026-08-06T04:50:10.122298Z digest=sha256:bff9cb465cf0fdd4258bc53bc38224b1243c85f29cfc1949b54b671bffc5e544

Observation 490a725b-39aa-4a54-9d87-eaad4a51d8d7 · outbound

This paper cites xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval

Reference 14

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source=pdf_text observed=2026-08-06T04:50:10.207313Z digest=sha256:7047876971c0e1445d6856c5e1b63982ad23237d15c17cc3939f149d255f98d0

Observation 84bd3359-2ab3-458d-8d1c-c9915553bb4f · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors Gonzalez, Hao Zhang, and Ion Stoica

Reference 15

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source=pdf_text observed=2026-08-06T04:50:10.298089Z digest=sha256:ffd85579fef53c185ba7f4ff2289157fc5dd3597e69ea01f87356b5fbe9aed6b

Observation 79ba02aa-58c1-4488-beba-11db4c01afe4 · outbound

This paper cites NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

Reference 16

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source=pdf_text observed=2026-08-06T04:50:10.360634Z digest=sha256:c75352f37491c727e3640efd2a14497d7e1e7af207e850663fb97b8b87af146b

Observation 2cf70bed-8811-4b51-adfc-0d11a8f73c86 · outbound

This paper cites EvoCodeBench: An Evolving Code Generation Benchmark Aligned with Real-World Code Repositories.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors EvoCodeBench: An Evolving Code Generation Benchmark Aligned with Real-World Code Repositories

Reference 17

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source=pdf_text observed=2026-08-06T04:50:10.449465Z digest=sha256:e89fd7fc9812bc3f78d8233a315dd86ff972cfc780f2f539a5e6b60574f65afb

Observation 6c477ef1-30ed-4a73-a47e-d6e507da45ba · outbound

This paper cites RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems

Reference 18

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source=pdf_text observed=2026-08-06T04:50:10.516832Z digest=sha256:af6866f0f6f639dc1f1707926a44cb3f1c17868140ff04485e2d72f0555e1626

Observation af796a5f-f0b6-4d16-a43c-3e75e1bbdc95 · outbound

This paper cites StarCoder 2 and The Stack v2: The Next Generation.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors StarCoder 2 and The Stack v2: The Next Generation

Reference 19

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source=pdf_text observed=2026-08-06T04:50:10.603590Z digest=sha256:23c82f9f80c2781a30de8e79ba013cb46f7449ac090f5bb514fc3c6be8ae1760

Observation 2421d692-e67b-4b81-8f99-4b8f5bbfd266 · outbound

This paper cites CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

Reference 20

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source=pdf_text observed=2026-08-06T04:50:10.703106Z digest=sha256:a0ebf0de87be81a97911bc4acca629f0ece031d3e0ccfff0e19204da7de94081

Observation a8ce4160-9a7d-4189-a22f-5673cf1da4c3 · outbound

This paper cites CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis

Reference 21

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source=pdf_text observed=2026-08-06T04:50:10.766003Z digest=sha256:5a1f752c61ab3a68fac85e1bbcb96bff1bbc6f34f47dc262897fddfc7cca4aeb

Observation 4bce0eea-3575-4527-a092-4cc9eec40ec0 · outbound

This paper cites 2024.ChatGPT.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors 2024.ChatGPT

Reference 22

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source=pdf_text observed=2026-08-06T04:50:10.832893Z digest=sha256:1fb9f6389c1b1e8e1220d172ccaf8fd1791855a3b1518ee87a745752f9f4954a

Observation 57688342-e41b-44d8-aea7-a6722e227c7c · outbound

This paper cites an unresolved cited work.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors Unresolved cited work

Reference 23

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source=pdf_text observed=2026-08-06T04:50:10.903700Z digest=sha256:3618bb6ab3cfab8c041081334a59b28a45e1044836131ffd885d15d54c7be10d

Observation 1ce2af1a-ce6a-4c92-8489-12631c6fa70e · outbound

This paper cites an unresolved cited work.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors Unresolved cited work

Reference 24

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source=pdf_text observed=2026-08-06T04:50:10.978815Z digest=sha256:4501f11dec6319409b620b8e73d4b8e0413ece343eca6e214518145afe17bc2a

Observation 8fcf28cc-cc02-4a9d-bda4-c861f9934d6d · outbound

This paper cites Qwen2.5 Technical Report.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors Qwen2.5 Technical Report

Reference 25

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source=pdf_text observed=2026-08-06T04:50:11.075802Z digest=sha256:cfdc02f659cb40f9d5bb87ad67708f262a696a174c9827932ae432ec690c1552

Observation 25896465-a028-4832-9062-496ff4d7cdfb · outbound

This paper cites an unresolved cited work.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors Unresolved cited work

Reference 26

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source=pdf_text observed=2026-08-06T04:50:11.134002Z digest=sha256:8b4d561fa0537b9fc494dbda72e3be46e96e7e4be1ae06954fb4ba6e514c6513

Observation c3fa5402-5d21-466b-a5f7-ef42c7783443 · outbound

This paper cites an unresolved cited work.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors Unresolved cited work

Reference 27

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source=pdf_text observed=2026-08-06T04:50:11.193479Z digest=sha256:825695de5e3401df551e96c7509b7bb19ba796564370549b97e7f036b7057aeb

Observation dd1d73c3-5a3d-4fa0-a07c-8ec66a9901ba · outbound

This paper cites CoderUJB: An Executable and Unified Java Benchmark for Practical Programming Scenarios.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors CoderUJB: An Executable and Unified Java Benchmark for Practical Programming Scenarios

Reference 28

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source=pdf_text observed=2026-08-06T04:50:11.262325Z digest=sha256:04da64882eb79238b7e54b07efe6b637fe425da64c1842357a4562fba5ad8554

Observation d5c53be6-88cf-48d8-93e6-6d938ee7d5f4 · outbound

This paper cites RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation

Reference 29

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source=pdf_text observed=2026-08-06T04:50:11.390913Z digest=sha256:fdf5fe7ccca665bdd252177bc2ac282e2cd70ee6d39ff565a7aef5e41c6fe998

Observation ffcd3222-ef42-4132-b689-7e504c7be5ed · outbound

This paper cites CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges

Reference 30

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source=pdf_text observed=2026-08-06T04:50:11.457129Z digest=sha256:61122f2bad07477f42d2e323f58ac5dd0af36d447bcb967eb93c236f739a3a76

Observation da2ac29a-b449-49e2-bb1c-a8162083970f · outbound

This paper cites CodeScore: Evaluating Code Generation by Learning Code Execution.

CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors CodeScore: Evaluating Code Generation by Learning Code Execution

Reference 2023

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source=pdf_text observed=2026-08-06T04:50:09.552297Z digest=sha256:3062e1bec4301bb85d650100f11c54cf43e7dbd6abafb6dcf28ec0526885bd78

Pith citing papers

No inbound Pith citation observations are available.