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Paper Citation Record · LEDGER

MemoCoder: Automated Function Synthesis using LLM-Supported Agents

As of 17 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2507.18812.

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

pith.paper-citation-record.v1
2507.18812 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:13:23.708714Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

38 of 38 outbound references displayed

  • verified exact0
  • verified fuzzy1
  • unresolved37
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 825059a4-8477-4bf2-9301-5afda2e19490 · outbound

This paper cites CodeMirage: Hallucinations in Code Generated by Large Language Models.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents CodeMirage: Hallucinations in Code Generated by Large Language Models

Reference 2

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source=pdf_text observed=2026-08-15T18:13:22.598879Z digest=sha256:93ba1888f283c45da0e6f20e13c5e6aadf95a8f6d6ba1f375ece354d81204b0a

Observation 207e0922-27c3-40e8-b294-6f8bda9d28b3 · outbound

This paper cites Program Synthesis with Large Language Models.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Program Synthesis with Large Language Models

Reference 4

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source=pdf_text observed=2026-08-15T18:13:22.737775Z digest=sha256:75ffc480f30e40160374718613b19117cbd89b061a1b01971b20fdde69bc97b2

Observation 3d14e5cd-3a1f-4a03-ba08-cf5455b4c8e1 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Evaluating Large Language Models Trained on Code

Reference 6

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source=pdf_text observed=2026-08-15T18:13:22.751917Z digest=sha256:f4baae2d3b79a7fa04759358cc239771352a14b46a06c55de72be0afa24a5a9f

Observation ce8521b4-0f48-42a1-b91e-a8a8b5fc0af0 · outbound

This paper cites Sea Change in Software Development: Economic and Productivity Analysis of the AI-Powered Developer Lifecycle.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Sea Change in Software Development: Economic and Productivity Analysis of the AI-Powered Developer Lifecycle

Reference 7

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source=pdf_text observed=2026-08-15T18:13:22.758391Z digest=sha256:89f43f758e229242641707b1336c4b2256c39e748d5cd106147296a6dc76e23b

Observation 87f536a4-f03d-44b7-b20e-735fc9118afb · outbound

This paper cites an unresolved cited work.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Unresolved cited work

Reference 8

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source=pdf_text observed=2026-08-15T18:13:22.764872Z digest=sha256:bf7b2ed41d7b0d8221a2303c4a36dd7072322bd956493975505ff656ede5a71e

Observation 74e8cbca-cd1a-41ba-bc05-add88004a3fe · outbound

This paper cites The Llama 3 Herd of Models.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents The Llama 3 Herd of Models

Reference 9

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source=pdf_text observed=2026-08-15T18:13:22.770166Z digest=sha256:8f02ec505de0c26bceddb13ead9f5b3a38f8b553543bebee0d7387d5a0ad2cb7

Observation 33e82b85-6b94-4224-a1da-03964cc0b3f2 · outbound

This paper cites Retrieval-Augmented Code Generation for Universal Information Extraction.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Retrieval-Augmented Code Generation for Universal Information Extraction

Reference 10

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source=pdf_text observed=2026-08-15T18:13:22.777857Z digest=sha256:3d5936ffb437d2064c0f5a97a46783d30ff3d4c57c71d68cc22e1d44cc54bd53

Observation c9fb5053-a5a8-4944-8b7a-34f160b25fea · outbound

This paper cites an unresolved cited work.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Unresolved cited work

Reference 11

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source=pdf_text observed=2026-08-15T18:13:22.786104Z digest=sha256:2fa543679ba73bf2dedd3db75684065ba05e439310beffdf58faa389478110aa

Observation 3fd18708-acca-45c3-a15f-2601dad108c5 · outbound

This paper cites AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation

Reference 12

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source=pdf_text observed=2026-08-15T18:13:22.849631Z digest=sha256:8ddf1ae2c77db642ee8de26112f08e1bac2d2b8d9505572a4a889df5cadfa01b

Observation 4a180549-84e2-40b4-9995-b0ff4c2a30c6 · outbound

This paper cites Ashraful Islam et al.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Ashraful Islam et al

Reference 13

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source=pdf_text observed=2026-08-15T18:13:22.934124Z digest=sha256:393e3e5fe9dcaf47bfb890e161464c44f6ed15224a354ebd74ff126ed369e34e

Observation c97ebab3-3e0f-4615-81b5-8aac07e74d7a · outbound

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

MemoCoder: Automated Function Synthesis using LLM-Supported Agents LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 14

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source=pdf_text observed=2026-08-15T18:13:22.978751Z digest=sha256:c501947a333d69e13828c63a1a5cfa575a6da78132d567b1a26d5e42bc9d893f

Observation e084942d-a5fe-47c6-a9d3-ff2dd4a82165 · outbound

This paper cites SelfEvolve: A Code Evolution Framework via Large Language Models.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents SelfEvolve: A Code Evolution Framework via Large Language Models

Reference 15

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source=pdf_text observed=2026-08-15T18:13:22.987114Z digest=sha256:a0aa44727577c311a3396b3762e72b8c629485c327ad3683bbc3f9322489905c

Observation 8adcd7ab-9e52-4442-ac3b-6ad67b4a9419 · outbound

This paper cites an unresolved cited work.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Unresolved cited work

Reference 16

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source=pdf_text observed=2026-08-15T18:13:22.994547Z digest=sha256:d02f886d1750c132db0544e1a9e1485741bb5cdcad48b6f49bf054c94b1a955b

Observation ea3996c4-3737-4be1-95ee-7e6a288f1480 · outbound

This paper cites Vulnerability Handling of AI-Generated Code -- Existing Solutions and Open Challenges.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Vulnerability Handling of AI-Generated Code -- Existing Solutions and Open Challenges

Reference 17

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Observation eec6737b-9e4a-4336-ace2-7b0a1f70013f · outbound

This paper cites an unresolved cited work.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Unresolved cited work

Reference 18

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source=pdf_text observed=2026-08-15T18:13:23.007949Z digest=sha256:6f9e885bb9e26141034c1e525b276239b842e49ac47d8d47c6ece0e963379fa7

Observation 2eae60d2-c144-48f3-bbfb-cee81fe814b5 · outbound

This paper cites Lost in the Middle: How Language Models Use Long Contexts.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Lost in the Middle: How Language Models Use Long Contexts

Reference 19

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source=pdf_text observed=2026-08-15T18:13:23.074921Z digest=sha256:80d6379800fc4ae44648ea5e95df90d108b6e1576454f6490c560e0a06404f90

Observation 395d91f5-d23a-4f48-a9d5-2bf16374ff68 · outbound

This paper cites Insights into LLM Long-Context Failures: When Transformers Know but Don't Tell.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Insights into LLM Long-Context Failures: When Transformers Know but Don't Tell

Reference 20

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source=pdf_text observed=2026-08-15T18:13:23.081428Z digest=sha256:b8fd377190d6bc4f41dd51e0417d429ce26f11918b473eda7e98f0a3c5f63809

Observation fa1f8496-faaa-41e6-b071-7bea644cecc3 · outbound

This paper cites an unresolved cited work.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Unresolved cited work

Reference 21

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source=pdf_text observed=2026-08-15T18:13:23.087173Z digest=sha256:9c19dbbaf0e3ed9979b1efa6c17bc4ed600de04237c952664009fad54381a527

Observation 69f13df8-e158-454c-991e-aec8d6169c75 · outbound

This paper cites Prompt Refinement or Fine-tuning? Best Practices for using LLMs in Computational Social Science Tasks.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Prompt Refinement or Fine-tuning? Best Practices for using LLMs in Computational Social Science Tasks

Reference 22

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source=pdf_text observed=2026-08-15T18:13:23.093351Z digest=sha256:1120b4209d9097eed80aa80922109fa072fd32a400714fb3b51591151e1a7fa3

Observation 50852963-3ec9-4289-8c97-29fd5d74435b · outbound

This paper cites A Comprehensive Overview of Large Language Models.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents A Comprehensive Overview of Large Language Models

Reference 23

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source=pdf_text observed=2026-08-15T18:13:23.101935Z digest=sha256:3ee2002b87b1fb26c2c261c6fd2630bbbd1321f04d091822c3065e267375def9

Observation 1fcb9927-9631-44f0-a81e-f74f2a97e4f5 · outbound

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

MemoCoder: Automated Function Synthesis using LLM-Supported Agents CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis

Reference 24

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source=pdf_text observed=2026-08-15T18:13:23.110581Z digest=sha256:8f1e71b6b6291f5e5ed040198bccacb0333408ca31edf0cded329f1f1be5bca3

Observation e1ec2f19-87f3-4bcb-aae4-1f3c6f3afa2c · outbound

This paper cites an unresolved cited work.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Unresolved cited work

Reference 25

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source=pdf_text observed=2026-08-15T18:13:23.117741Z digest=sha256:524399f2fa0a056239c8f708ae286aa0ab734e4d3e8b783aa0f5877150e7b10a

Observation 7a45c8fc-7cd8-4300-8595-bf24245030c2 · outbound

This paper cites an unresolved cited work.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Unresolved cited work

Reference 26

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source=pdf_text observed=2026-08-15T18:13:23.123280Z digest=sha256:ab23a805e49fbbc883b844e7d3ffa0c1c13ef24050272c0ed9edff0bfc38affe

Observation ae0cdbef-5c4a-4326-91d1-bb0715c11741 · outbound

This paper cites Is Self-Repair a Silver Bullet for Code Generation?.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Is Self-Repair a Silver Bullet for Code Generation?

Reference 27

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source=pdf_text observed=2026-08-15T18:13:23.130293Z digest=sha256:d5fdcf2428a627156d535b6e711acbac3af3dc8f29fe55db6fc97e9bc7ec7436

Observation 276f0281-1890-415e-83b3-1c31a9ecbbc0 · outbound

This paper cites CodeCoR: An LLM-Based Self-Reflective Multi-Agent Framework for Code Generation.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents CodeCoR: An LLM-Based Self-Reflective Multi-Agent Framework for Code Generation

Reference 28

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Observation f0971b44-12eb-4535-95a6-563bc6ec3446 · outbound

This paper cites an unresolved cited work.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Unresolved cited work

Reference 29

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source=pdf_text observed=2026-08-15T18:13:23.272791Z digest=sha256:6e85a4e47c26b0fdc4380e75f6216ba6ff30cad5d789472124677b04a2be41e4

Observation 348ab8ee-fa43-46f3-a9a6-155d1e71cef5 · outbound

This paper cites The Impact of AI on Developer Productivity: Evidence from GitHub Copilot.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents The Impact of AI on Developer Productivity: Evidence from GitHub Copilot

Reference 30

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source=pdf_text observed=2026-08-15T18:13:23.279805Z digest=sha256:6f6bb7e8e0acb4d7d3cac9fb172cfe27c8909d3e90e527f688300a05b9290895

Observation c7d1fbb8-be7d-4b73-b1f8-198652bfaf65 · outbound

This paper cites Fine-Tuning and Prompt Engineering for Large Language Models-based Code Review Automation.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Fine-Tuning and Prompt Engineering for Large Language Models-based Code Review Automation

Reference 31

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Observation 4c5e0c22-a3be-476c-a9fe-9054ff700a7a · outbound

This paper cites Qwen2.5 Technical Report.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Qwen2.5 Technical Report

Reference 32

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source=pdf_text observed=2026-08-15T18:13:23.294871Z digest=sha256:ff653021276d6155f4748e0074b901a293610eaa713699a977f43f751f23ec18

Observation b3474108-3588-4d83-8d15-d2a9c3620d9b · outbound

This paper cites an unresolved cited work.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Unresolved cited work

Reference 33

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source=pdf_text observed=2026-08-15T18:13:23.302282Z digest=sha256:4503774282113042c449e616793f1809f1188fe8ae0d91f419a0dcef8aa706d0

Observation 8ea03d35-ae2b-4a38-a51c-5a9a9028cbcd · outbound

This paper cites Online Adaptation of Language Models with a Memory of Amortized Contexts.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Online Adaptation of Language Models with a Memory of Amortized Contexts

Reference 34

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source=pdf_text observed=2026-08-15T18:13:23.316322Z digest=sha256:d5347512493b7206f87378b83c73170241c573a54c4f0db07710f26e6cfdc4cf

Observation 5ddffc14-0155-4d03-8be0-9c0450018733 · outbound

This paper cites CodeT5+: Open Code Large Language Models for Code Understanding and Generation.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents CodeT5+: Open Code Large Language Models for Code Understanding and Generation

Reference 35

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source=pdf_text observed=2026-08-15T18:13:23.322139Z digest=sha256:a8fc59e44bf46750adf0633f8419b6c60d585a862c9aad8f222b7a3485a11531

Observation 12d99256-a7ff-4282-86ba-1d9aedf1e992 · outbound

This paper cites Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models

Reference 36

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source=pdf_text observed=2026-08-15T18:13:23.394935Z digest=sha256:57bc25d7d4b3af7a2f390c95713b37def37ed4508c8e79a94f4f3740a36bd077

Observation 15adaf5e-06be-435e-86ec-9eb3ab3bac45 · outbound

This paper cites an unresolved cited work.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Unresolved cited work

Reference 37

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T18:13:23.493604Z digest=sha256:4005b351a8447d9c0838dead7bfc1eb5f8bb839b584590a5874adc588f3c6b19

Observation 4c906f5f-61f9-486f-bf83-ee481302323e · outbound

This paper cites Planning with Large Language Models for Code Generation.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Planning with Large Language Models for Code Generation

Reference 38

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source=pdf_text observed=2026-08-15T18:13:23.702805Z digest=sha256:2e2692f9682e3475094a09eb8bb972baf5796e430e2a27140fcc32a0a15b6b19

Observation b163ec44-bd46-403e-93ac-41a8d7410f7e · outbound

This paper cites Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

Reference 39

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source=pdf_text observed=2026-08-15T18:13:23.576176Z digest=sha256:97910dc839d9c4317dd6a4036466133b2512f2777e81a1e2e98ab92401d3833c

Observation 262f98da-f2ff-46b2-b741-61817a750442 · outbound

This paper cites RefineCoder: Iterative Improving of Large Language Models via Adaptive Critique Refinement for Code Generation.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents RefineCoder: Iterative Improving of Large Language Models via Adaptive Critique Refinement for Code Generation

Reference 41

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source=pdf_text observed=2026-08-15T18:13:23.708714Z digest=sha256:6b8f734d82365d06dca1b19c37b400b022ea97cb1abe746789245f92f7bb940d

Observation 9d10f4eb-44d4-460e-9528-fb71be934b6f · outbound

This paper cites PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback

Reference 2023

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:13:23.309373Z digest=sha256:9f9e59a8ec75f729c1fbfaf0532e5e704c6bda345a83e4b88c4cdd7442bad95a

Pith citing papers

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