Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming | Paper Citation Record

Observation 04807e48-7c75-404b-98e6-f64fa0b194b2 · outbound

This paper cites 2026 agentic coding trends report,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming 2026 agentic coding trends report,

Reference 1

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Observation 1b7e812a-4b9b-4467-b6c8-bea9d7c6358d · outbound

This paper cites Top vibe coding statistics & trends,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Top vibe coding statistics & trends,

Reference 2

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Observation 284540bd-fafa-4be4-a285-828e7f762c5f · outbound

This paper cites Building software by rolling the dice: A qualitative study of vibe coding,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Building software by rolling the dice: A qualitative study of vibe coding,

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:47:49.680387Z

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Observation ccde977a-5b86-43a8-9b75-e8f928386801 · outbound

This paper cites Vibe coding in practice: Motivations, challenges, and a future outlook-a grey literature review.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Vibe coding in practice: Motivations, challenges, and a future outlook-a grey literature review

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:47:49.716476Z

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Observation a7845fc7-f5fc-44de-a3f7-d34684c8244b · outbound

This paper cites Intent formalization: A grand challenge for reliable coding in the age of ai agents.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Intent formalization: A grand challenge for reliable coding in the age of ai agents

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:47:49.690717Z

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Observation f9e088db-d83d-4baa-a3da-489db9ee355b · outbound

This paper cites State of code developer survey report,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming State of code developer survey report,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.899311Z

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Observation a6a5f5dc-4977-41d4-b015-07a2f92369a8 · outbound

This paper cites Lever: Learning to verify language-to-code generation with execution,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Lever: Learning to verify language-to-code generation with execution,

Reference 7

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Observation c0fad0a4-4988-4196-90f0-7fef6fef613e · outbound

This paper cites Automated unit test improvement using large language models at meta,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Automated unit test improvement using large language models at meta,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.905960Z

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Observation dca0bea0-9da2-4bbf-b6b0-f2febb47dc9d · outbound

This paper cites Rethinking verification for LLM code generation: From generation to testing,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Rethinking verification for LLM code generation: From generation to testing,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.909974Z

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Observation 46fcce58-85a8-4fa7-acdc-7e8b71610e43 · outbound

This paper cites Testeval: Benchmarking large language models for test case generation.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Testeval: Benchmarking large language models for test case generation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.889733Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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This paper cites Test case generation for requirements in natural language-an llm comparison study,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Test case generation for requirements in natural language-an llm comparison study,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.891406Z

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Observation b0594b4f-dce6-4bd0-8a31-85eed67ed50d · outbound

This paper cites Test-driven development and llm-based code generation,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Test-driven development and llm-based code generation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.887990Z

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Observation 25c5df30-f7ed-4f02-982f-e7d6921c2f99 · outbound

This paper cites Automated program refinement: Guide and verify code large language model with refinement calculus,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Automated program refinement: Guide and verify code large language model with refinement calculus,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.884615Z

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This paper cites Lemur: Integrating Large Language Models in Automated Program Verification.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Lemur: Integrating Large Language Models in Automated Program Verification

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:47:49.695700Z

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Observation 13b84743-7f3d-4a1c-99d2-fad3fa3e74a3 · outbound

This paper cites Clover: Clo sed-loop ver ifiable code generation,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Clover: Clo sed-loop ver ifiable code generation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.882877Z

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Observation 32904662-721a-4b93-b24b-92f68fa72493 · outbound

This paper cites Vecogen: Automating generation of formally verified c code with large language models,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Vecogen: Automating generation of formally verified c code with large language models,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.877550Z

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Observation 05c180f9-663c-4daf-8e40-58e0002a21a1 · outbound

This paper cites Astrogator: Towards Formal Verification of LLM-Generated Code.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Astrogator: Towards Formal Verification of LLM-Generated Code

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:47:49.708017Z

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Observation c9fc1882-bb6d-4346-a700-f0eada731d40 · outbound

This paper cites Deepassert: An llm-aided verification framework with fine-grained assertion generation for modules with extracted module specifications.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Deepassert: An llm-aided verification framework with fine-grained assertion generation for modules with extracted module specifications

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:47:49.672468Z

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Observation 50924598-3db4-4d5a-ad33-71fe5ba858e0 · outbound

This paper cites Certificates in ai: Learn but verify,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Certificates in ai: Learn but verify,

Reference 19

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Observation df3a4544-b611-4e1d-9568-8f8dcd8c90d1 · outbound

This paper cites Code-aware prompting: A study of coverage-guided test generation in regression setting using llm,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Code-aware prompting: A study of coverage-guided test generation in regression setting using llm,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.881137Z

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Observation 3dbcb06d-ff3b-45fb-a658-1c6b2f6064ae · outbound

This paper cites Coverup: Effective high coverage test generation for python,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Coverup: Effective high coverage test generation for python,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.875810Z

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This paper cites LLM Test Generation via Iterative Hybrid Program Analysis.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming LLM Test Generation via Iterative Hybrid Program Analysis

Reference 22

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Observation 906d5034-a5c3-45b5-8423-9bd86a4b315c · outbound

This paper cites On the effectiveness of llm-as-a-judge for code generation and summarization.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming On the effectiveness of llm-as-a-judge for code generation and summarization

Reference 23

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This paper cites Clarifygpt: A framework for enhancing llm-based code generation via requirements clarification,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Clarifygpt: A framework for enhancing llm-based code generation via requirements clarification,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.879437Z

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This paper cites Python code generation by asking clarification questions,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Python code generation by asking clarification questions,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.886346Z

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Observation 8cc6832b-f0a6-4bfd-a7c9-d6a54f75b35d · outbound

This paper cites Curiosity by Design: An LLM-based Coding Assistant Asking Clarification Questions.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Curiosity by Design: An LLM-based Coding Assistant Asking Clarification Questions

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:47:49.731620Z

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This paper cites Clarigen: Bridging instruction gaps via interactive clarification in code generation,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Clarigen: Bridging instruction gaps via interactive clarification in code generation,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.871305Z

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This paper cites Large Language Models Should Ask Clarifying Questions to Increase Confidence in Generated Code.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Large Language Models Should Ask Clarifying Questions to Increase Confidence in Generated Code

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:47:49.729174Z

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Observation e4df3a07-1c0f-467a-a38a-e90f8d560a6f · outbound

This paper cites Automated repair of ambiguous problem descriptions for llm-based code generation,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Automated repair of ambiguous problem descriptions for llm-based code generation,

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:47:49.734080Z

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Observation 01267889-4dac-4184-b198-a89d87d2c8b4 · outbound

This paper cites Contested: Consistency-aided tested code generation with llm,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Contested: Consistency-aided tested code generation with llm,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.912022Z

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This paper cites Persistent human feedback, llms, and static analyzers for secure code generation and vulnerability detec- tion,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Persistent human feedback, llms, and static analyzers for secure code generation and vulnerability detec- tion,

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:47:49.683066Z

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Observation 2b0aa94c-4917-402c-90c0-fa5a84cae7ce · outbound

This paper cites Llm- based test-driven interactive code generation: User study and empirical evaluation,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Llm- based test-driven interactive code generation: User study and empirical evaluation,

Reference 32

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Observation 7084ed77-2bde-47e7-859e-b17a1434529f · outbound

This paper cites Machine learning testing: Survey, landscapes and horizons,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Machine learning testing: Survey, landscapes and horizons,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.916200Z

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Observation 17dd80b6-a6d6-4ec1-8281-c3d64ab3ec84 · outbound

This paper cites Quantcode-eval: Bench- marking quantitative strategy code reproduction from finance papers,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Quantcode-eval: Bench- marking quantitative strategy code reproduction from finance papers,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.864067Z

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This paper cites BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-07-03T08:47:49.698213Z

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Observation f9c6c87e-41c7-48dd-bff4-f08264b617a4 · outbound

This paper cites Deepseek v4 flash,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Deepseek v4 flash,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.865842Z

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This paper cites Claude opus 4.7,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Claude opus 4.7,

Reference 37

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This paper cites Literate programming,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Literate programming,

Reference 38

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Source-reported events for the cited work

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Observation 04ff82e9-2254-4557-bd68-e61a0b611964 · outbound

This paper cites Natural language outlines for code: Literate programming in the llm era,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Natural language outlines for code: Literate programming in the llm era,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.867749Z

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Observation a465a4f8-ed6e-4ec5-bd67-00072d57733b · outbound

This paper cites Enhancing Code Generation via Bidirectional Comment-Level Mutual Grounding.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Enhancing Code Generation via Bidirectional Comment-Level Mutual Grounding

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:47:49.693245Z

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Observation c790d21c-15bd-4b89-aebb-5a5f09ab8f25 · outbound

This paper cites Bounded model checking.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Bounded model checking

Reference 41

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Source-reported events for the cited work

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Observation c86cfd54-010c-4958-aa19-4b0fae28b4a3 · outbound

This paper cites Jupyter notebook: The classic notebook interface,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Jupyter notebook: The classic notebook interface,

Reference 42

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This paper cites Model checking.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Model checking

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.918107Z

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Observation 18e7f770-3407-411c-9499-73eeb2c3438c · outbound

This paper cites Semantical considerations on floyd-hoare logic,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Semantical considerations on floyd-hoare logic,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.857494Z

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Observation 9faa30c5-5ca1-4c6e-b263-0b3734fc0c69 · outbound

This paper cites Toward natural language computa- tion i,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Toward natural language computa- tion i,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.907879Z

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Observation c76c6b6d-888e-47e1-833e-51e9799f050b · outbound

This paper cites Natural language programming: Styles, strategies, and contrasts,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Natural language programming: Styles, strategies, and contrasts,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.852438Z

Source-reported events for the cited work

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Observation 04c1ce66-dc1e-4cc6-8f8e-29dfaa4387ef · outbound

This paper cites Thought is all you need: Smart contract vulnerability detection with thought-augmented large language model,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Thought is all you need: Smart contract vulnerability detection with thought-augmented large language model,

Reference 47

Resolution: verified exact; doi, observed 2026-07-03T08:47:49.484664Z

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Observation de4033d5-2db5-45d1-ada0-2031fc6bbbcd · outbound

This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-07-03T08:47:49.688065Z

Source-reported events for the cited work

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Observation 0f823c59-3b58-43eb-adfe-c74aa2c39fbc · outbound

This paper cites Recovering traceability links between code and documentation,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Recovering traceability links between code and documentation,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.854058Z

Source-reported events for the cited work

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Observation 6c7cd852-5cbf-4b67-9e19-5e688d1cb790 · outbound

This paper cites Lissa: Toward generic traceability link recovery through retrieval-augmented generation,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Lissa: Toward generic traceability link recovery through retrieval-augmented generation,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.855959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6422d228-54ab-4031-bddf-6200b2dd5d2c · outbound

This paper cites Python Symbolic Execution with LLM-powered Code Generation.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Python Symbolic Execution with LLM-powered Code Generation

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:47:49.669884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 51279c04-c349-49cb-8ad8-42002350f5fc · outbound

This paper cites Langchain,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Langchain,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.847774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9dc8e942-8474-4e24-b17d-ed5161fbc605 · outbound

This paper cites O’Reilly Media, Inc.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming O’Reilly Media, Inc

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.849255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 88a40b60-ecad-40cc-8f59-768b27bc7f70 · outbound

This paper cites OpenAI GPT-5 System Card.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming OpenAI GPT-5 System Card

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-07-03T08:47:49.705414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ed1f350d-fdc7-422e-ac53-a114c01e4c48 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Evaluating Large Language Models Trained on Code

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-07-03T08:47:49.703083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b1a88177-429c-4c24-b62b-efe1ae6a8331 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.850851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5d66dfc2-2dd8-4ebc-812a-d63a7c983e90 · outbound

This paper cites Prometheus: Inducing fine-grained evaluation capability in language models,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Prometheus: Inducing fine-grained evaluation capability in language models,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.842791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bb36ecc9-ab0a-483d-8531-5fcf954847ae · outbound

This paper cites Fewer Hallucinations, More Verification: A Three-Stage LLM-Based Framework for ASR Error Correction,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Fewer Hallucinations, More Verification: A Three-Stage LLM-Based Framework for ASR Error Correction,

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:47:49.667135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d53ef912-4f10-48b1-96a1-9c064e8f1b09 · outbound

This paper cites Llm-check: Investigating detection of hallucinations in large language models.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Llm-check: Investigating detection of hallucinations in large language models

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.846188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a1b35f0c-a982-47be-8e9c-ba32de9f4d23 · outbound

This paper cites Calibration and correctness of language models for code,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Calibration and correctness of language models for code,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.844425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation babf031f-03bb-4385-9e3e-d4a0658ecb5b · outbound

This paper cites Validating LLM-Generated Programs with Metamorphic Prompt Testing.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Validating LLM-Generated Programs with Metamorphic Prompt Testing

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:47:49.685605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 934b17b9-b6c7-4779-a128-45047f231ae3 · outbound

This paper cites Effective LLM Code Refinement via Property-Oriented and Structurally Minimal Feedback.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Effective LLM Code Refinement via Property-Oriented and Structurally Minimal Feedback

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-07-03T08:47:49.674901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1c8e8402-333a-4e11-9db8-c11c5243e1b6 · outbound

This paper cites Guardians of the agents,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Guardians of the agents,

Reference 63

Resolution: verified exact; doi, observed 2026-07-03T08:47:49.482985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4f36e441-656c-4da9-92c8-e24af3fed75c · outbound

This paper cites Sharpen the spec, cut the code: A case for generative file system with sysspec,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Sharpen the spec, cut the code: A case for generative file system with sysspec,

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:47:49.677475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1e601808-21c1-4a8e-be32-dafcf8dd88ef · outbound

This paper cites FM-Agent: Scaling Formal Methods to Large Systems via LLM-Based Hoare-Style Reasoning.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming FM-Agent: Scaling Formal Methods to Large Systems via LLM-Based Hoare-Style Reasoning

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-07-03T08:47:49.661608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f41a2f54-49a0-4997-84a6-0e4c7dce194b · outbound

This paper cites Self-spec: Model-authored specifications for reliable llm code generation,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Self-spec: Model-authored specifications for reliable llm code generation,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.858979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 943aa4ae-73a2-45c6-8271-fea4d9d29e63 · outbound

This paper cites Spec Kit,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Spec Kit,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.837217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 42ee4403-a1f6-485d-a7f5-fbaba1c4140d · outbound

This paper cites PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation

Reference 68

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T08:47:49.664450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8d258cc4-1827-4fd0-9887-e93d927e1faa · outbound

This paper cites RvLLM: LLM runtime verification with domain knowledge,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming RvLLM: LLM runtime verification with domain knowledge,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.839246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bf82cc2f-9464-43c9-ae46-8d9c1c5a1022 · outbound

This paper cites Evaluating the ability of gpt-4o to generate verifiable specifications in verifast,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Evaluating the ability of gpt-4o to generate verifiable specifications in verifast,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.835482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3fe890ef-f7fc-4468-b009-01ce5a8c853d · outbound

This paper cites Llm-guided formal verification coupled with mutation testing.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Llm-guided formal verification coupled with mutation testing

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.833672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c3587158-0150-4846-84bb-b451b440b5b4 · outbound

This paper cites Grounded copilot: How programmers interact with code-generating models.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Grounded copilot: How programmers interact with code-generating models

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.841065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0e4f06e8-8fde-4f13-b3a7-6467a5015c1c · outbound

This paper cites Reading between the lines: Modeling user behavior and costs in ai-assisted programming,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Reading between the lines: Modeling user behavior and costs in ai-assisted programming,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.860661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bb859a4a-2c33-4499-945e-8041a6e3b853 · outbound

This paper cites Towards more effective ai-assisted programming: A systematic design exploration to improve visual studio intellicode’s user experience,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Towards more effective ai-assisted programming: A systematic design exploration to improve visual studio intellicode’s user experience,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.831862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3d8b213e-a722-4eb8-ae97-ee147aaf91d1 · outbound

This paper cites Sketch Then Generate: Providing Incremental User Feedback and Guiding LLM Code Generation through Language-Oriented Code Sketches.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Sketch Then Generate: Providing Incremental User Feedback and Guiding LLM Code Generation through Language-Oriented Code Sketches

Reference 75

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:47:49.710809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b4b58c8c-6e7e-4199-beb0-4af44efc413e · outbound

This paper cites Validating ai-generated code with live programming,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Validating ai-generated code with live programming,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.829580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d7dd145d-ff9c-42cb-aebb-48a2b0cce5d9 · outbound

This paper cites Hilde: Intentional code generation via human-in-the-loop decoding,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Hilde: Intentional code generation via human-in-the-loop decoding,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.827932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 549999ab-729b-4ba6-aacb-fb9b7882f30d · outbound

This paper cites Human-in-the- loop software development agents,.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming Human-in-the- loop software development agents,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T10:40:55.826223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b9f16e6d-8e35-411a-8ed3-6728eba63bfd · outbound

This paper cites When Prompts Go Wrong: Evaluating Code Model Robustness to Ambiguous, Contradictory, and Incomplete Task Descriptions.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming When Prompts Go Wrong: Evaluating Code Model Robustness to Ambiguous, Contradictory, and Incomplete Task Descriptions

Reference 79

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:47:49.713356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c9b2e786-f9ca-4038-baad-575b4c86c156 · outbound

This paper cites What Prompts Don't Say: Understanding and Managing Underspecification in LLM Prompts.

Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming What Prompts Don't Say: Understanding and Managing Underspecification in LLM Prompts

Reference 80

Resolution: verified exact; local_arxiv, observed 2026-07-03T08:47:49.700607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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