Paper Citation Record · LEDGER

Can LLMs Replace Humans During Code Chunking?

As of 15 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2506.19897.

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

pith.paper-citation-record.v1
2506.19897 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:10:14.989957Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T22:36:19.807806Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T13:55:45.556432Z

Reference resolution

27 of 27 outbound references displayed

verified exact2
verified fuzzy2
unresolved23
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c50aab40-83ee-4f55-881a-35e992abb6e8 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Can LLMs Replace Humans During Code Chunking? Evaluating Large Language Models Trained on Code

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:12.499635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 253665d3-dae7-41dc-8c83-d709e142f4f9 · outbound

This paper cites Expectation vs. experi- ence: Evaluating the usability of code generation tools powered by large language models,.

Can LLMs Replace Humans During Code Chunking? Expectation vs. experi- ence: Evaluating the usability of code generation tools powered by large language models,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:12.590198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25ce10f5-eb2e-4ee6-9e6c-9d0b3b0c2144 · outbound

This paper cites Large language models for software engi- neering: A systematic literature review,.

Can LLMs Replace Humans During Code Chunking? Large language models for software engi- neering: A systematic literature review,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:12.690494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2550cb3e-afb3-4176-9f53-e7d039e26142 · outbound

This paper cites A Survey on Large Language Models for Code Generation.

Can LLMs Replace Humans During Code Chunking? A Survey on Large Language Models for Code Generation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:12.795971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6c0281a-ae17-4012-accd-e6cbeaa49312 · outbound

This paper cites Enhancing Computer Programming Education with LLMs: A Study on Effective Prompt Engineering for Python Code Generation.

Can LLMs Replace Humans During Code Chunking? Enhancing Computer Programming Education with LLMs: A Study on Effective Prompt Engineering for Python Code Generation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:12.892063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61a61d83-13b5-459d-86f8-8db2bba793df · outbound

This paper cites Automated Prompt Engineering for Cost-Effective Code Generation Using Evolutionary Algorithm.

Can LLMs Replace Humans During Code Chunking? Automated Prompt Engineering for Cost-Effective Code Generation Using Evolutionary Algorithm

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:12.988811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0dcd1567-5f49-422a-90ba-1236fbad8bc3 · outbound

This paper cites Instruct or Interact? Exploring and Eliciting LLMs' Capability in Code Snippet Adaptation Through Prompt Engineering.

Can LLMs Replace Humans During Code Chunking? Instruct or Interact? Exploring and Eliciting LLMs' Capability in Code Snippet Adaptation Through Prompt Engineering

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:10:15.527061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e1dc4ed0-5404-4edc-833f-eb3350b6f6ed · outbound

This paper cites Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation.

Can LLMs Replace Humans During Code Chunking? Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:13.178316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84079924-ee48-41c7-a48d-ade682bc4cb2 · outbound

This paper cites Roformer: En- hanced transformer with rotary position embedding,.

Can LLMs Replace Humans During Code Chunking? Roformer: En- hanced transformer with rotary position embedding,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:13.267620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56c6bfad-949a-4a9d-a6eb-cf5ae2efa3df · outbound

This paper cites Extending LLMs' Context Window with 100 Samples.

Can LLMs Replace Humans During Code Chunking? Extending LLMs' Context Window with 100 Samples

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:13.361574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c95185f-e228-4079-87b5-ebba51055912 · outbound

This paper cites LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding.

Can LLMs Replace Humans During Code Chunking? LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:13.430542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20b66f5a-cea0-475c-8eb9-691a7a985fc6 · outbound

This paper cites Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systems.

Can LLMs Replace Humans During Code Chunking? Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systems

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:13.561868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb0711fe-c097-42e2-8ab0-9d0f346950b5 · outbound

This paper cites From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries.

Can LLMs Replace Humans During Code Chunking? From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:13.615723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6753816a-99fd-4d4f-82cd-46b501a0ffc2 · outbound

This paper cites Retrieval- augmented generation for knowledge-intensive nlp tasks,.

Can LLMs Replace Humans During Code Chunking? Retrieval- augmented generation for knowledge-intensive nlp tasks,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:13.735826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3863d5f4-010e-4564-9aa4-3e8a86c7ba21 · outbound

This paper cites The Chronicles of RAG: The Retriever, the Chunk and the Generator.

Can LLMs Replace Humans During Code Chunking? The Chronicles of RAG: The Retriever, the Chunk and the Generator

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:13.871322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a1594dc-30ba-4a4c-bb5e-dad75efd2451 · outbound

This paper cites Is Semantic Chunking Worth the Computational Cost?.

Can LLMs Replace Humans During Code Chunking? Is Semantic Chunking Worth the Computational Cost?

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:13.972177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 335e4952-2baa-404a-83e5-55af81989b46 · outbound

This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

Can LLMs Replace Humans During Code Chunking? Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:14.040908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57238bcb-4a46-4818-b45e-24b003eaf827 · outbound

This paper cites Meta-Chunking: Learning Text Segmentation and Semantic Completion via Logical Perception.

Can LLMs Replace Humans During Code Chunking? Meta-Chunking: Learning Text Segmentation and Semantic Completion via Logical Perception

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:14.102192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e258654-2f81-4f3b-aa2a-86371125e089 · outbound

This paper cites LumberChunker: Long-Form Narrative Document Segmentation.

Can LLMs Replace Humans During Code Chunking? LumberChunker: Long-Form Narrative Document Segmentation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:14.191814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcda5b94-7e0a-468a-8d8a-5d3188b23941 · outbound

This paper cites Grounding Language Model with Chunking-Free In-Context Retrieval.

Can LLMs Replace Humans During Code Chunking? Grounding Language Model with Chunking-Free In-Context Retrieval

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:10:15.238527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 91233b36-a80b-42d4-95f5-b740e19a4d6f · outbound

This paper cites An Empirical Study on the Code Refactoring Capability of Large Language Models.

Can LLMs Replace Humans During Code Chunking? An Empirical Study on the Code Refactoring Capability of Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:14.412425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24017d1c-6b92-40f5-aeb5-d672ee523380 · outbound

This paper cites LLM$\times$MapReduce: Simplified Long-Sequence Processing using Large Language Models.

Can LLMs Replace Humans During Code Chunking? LLM$\times$MapReduce: Simplified Long-Sequence Processing using Large Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:14.487065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76f7d183-0b41-443d-a7e2-dd9aa3e4b916 · outbound

This paper cites Llm-based and retrieval-augmented control code generation,.

Can LLMs Replace Humans During Code Chunking? Llm-based and retrieval-augmented control code generation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:15.940195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ac872aab-64d6-4223-bb9b-5bdc16925ea5 · outbound

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

Can LLMs Replace Humans During Code Chunking? Python Symbolic Execution with LLM-powered Code Generation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:14.656505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16204900-8d36-4db0-98ce-112dcf1c74b1 · outbound

This paper cites Towards Translating Real-World Code with LLMs: A Study of Translating to Rust.

Can LLMs Replace Humans During Code Chunking? Towards Translating Real-World Code with LLMs: A Study of Translating to Rust

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:14.733838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b32f131-8d69-4bb8-8bd8-5a142e9ef2f0 · outbound

This paper cites Bridging Eras: Transforming Fortran legacies into Python with the power of large language models,.

Can LLMs Replace Humans During Code Chunking? Bridging Eras: Transforming Fortran legacies into Python with the power of large language models,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:15.748578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0a0004ad-4d77-4125-b298-0355ac986c19 · outbound

This paper cites Batch Prompting: Efficient Inference with Large Language Model APIs.

Can LLMs Replace Humans During Code Chunking? Batch Prompting: Efficient Inference with Large Language Model APIs

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:14.989957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 8d56a07d-99f5-4ec9-8e05-e25a98108b65 · inbound

SemChunk-C: Semantic Segmentation for C Code cites this paper.

SemChunk-C: Semantic Segmentation for C Code Can LLMs Replace Humans During Code Chunking?

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-01T13:55:45.558269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.