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Paper Citation Record · LEDGER

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents

As of 7 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 3 inbound Pith citation observations for arXiv:2507.04009.

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

pith.paper-citation-record.v1
2507.04009 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:00:57.651118Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T02:30:33.667986Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T06:27:24.602548Z

Reference resolution

15 of 15 outbound references displayed

  • verified exact0
  • verified fuzzy5
  • unresolved10
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 34d92c10-3497-4568-8af6-ffb8519c82af · outbound

This paper cites an unresolved cited work.

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:00:59.247232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:00:56.975051Z digest=sha256:e9a37783bd2c070041775562a6bcc8c4915399bcaa9cb3d6d4466bbe079276db

Observation 36d54536-f167-494f-8e3f-88700851076e · outbound

This paper cites an unresolved cited work.

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents Unresolved cited work

Reference 2

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:00:59.017243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:00:57.133889Z digest=sha256:22612cab044e5d4972e48ac631973b8d6551b1db243550a758478011e68f570e

Observation 13b98738-b50d-41e8-836c-b6bc57a65787 · outbound

This paper cites Evaluation Method:.

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents Evaluation Method:

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:00:58.833425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:00:57.267159Z digest=sha256:2ffa36c8772fe1c93315ddd7205b17ecb02c8c21ab3d2b508b53f86ce7e0ab8b

Observation 1368a9cd-6727-49e6-afd3-f224425ba63b · outbound

This paper cites In The Twelfth Inter- national Conference on Learning Representations.

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents In The Twelfth Inter- national Conference on Learning Representations

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:00:59.657904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:00:56.290691Z digest=sha256:8673caf2f2c76f18eef95380d5907aa6cb84de71589078a0ef9fdc38293efc21

Observation e902a8e1-d254-45a5-845d-efebf1709496 · outbound

This paper cites DeepSeek-V3 Technical Report.

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents DeepSeek-V3 Technical Report

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-06T20:00:56.479445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:00:56.479445Z digest=sha256:5cf8bab18ce08a12e2061c6529068880adc6dfc6414434c86c4a22258511531c

Observation 24e4a5e5-13fc-4058-85e3-0faa9280c5c7 · outbound

This paper cites Qwen3 Technical Report.

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents Qwen3 Technical Report

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T20:00:56.601453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:00:56.601453Z digest=sha256:49defd38c23601bdb1c2ae81814ad0e64690d7cbd789f0f2481766437b0547e5

Observation f39ec340-5fbe-4baf-886c-3bb038d5fa71 · outbound

This paper cites Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction.

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T20:00:56.735409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:00:56.735409Z digest=sha256:f678695c8f3f468e95c1b9124fbabeb905a52a0971ba17260269fd6b22012f73

Observation 27186558-5ee5-4af0-94bb-81dbcc107fab · outbound

This paper cites an unresolved cited work.

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents Unresolved cited work

Reference 12

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:00:58.647750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:00:57.329418Z digest=sha256:9705b96094b2cc0fc99e0b03365c077d2c64419de200f7964af8c36dc4213dca

Observation 97554f1c-c8e2-4396-836c-2d45edcf5eee · outbound

This paper cites an unresolved cited work.

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents Unresolved cited work

Reference 13

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:00:58.409740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:00:57.473207Z digest=sha256:263937ce5af0e3e2c44f4cb91387c767949672e8f40d3176fc20d673e71a5998

Observation eb835723-f3cc-4a5a-938d-438e417ddeae · outbound

This paper cites an unresolved cited work.

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents Unresolved cited work

Reference 14

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:00:58.210500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:00:57.553293Z digest=sha256:31ed4096e76f148cb95f5f7e304ce1fcd23a5b0a901f72549044a293ee8f8f56

Observation b8ede898-6dd1-47ef-96a0-c163b3ab5ef8 · outbound

This paper cites correctness.

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents correctness

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:00:58.050882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:00:57.651118Z digest=sha256:f3d156ee6fff1ef3bd40a3f237222d161cc06ece9e6a9b25274afe3359902e3a

Observation e48144f0-2ab9-45fb-90d8-86eebb6f50a8 · outbound

This paper cites In International Conference on Learning Representations.

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents In International Conference on Learning Representations

Reference 2021

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:00:59.845147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:00:56.178142Z digest=sha256:e875698b45629fd950f73a71494653ba4a44c4a0a8ffb2352ca49ee6d29764a6

Observation daa47417-8c7c-49f4-8709-8bdd66ef4e04 · outbound

This paper cites Advances in Neural Information Pro- cessing Systems, 36:46595–46623.

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents Advances in Neural Information Pro- cessing Systems, 36:46595–46623

Reference 2023

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:00:59.407766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:00:56.835967Z digest=sha256:a826a78cc18fa586d71bdb1008b94b4dea9049efa6fa5273251fc3397205361d

Observation 82803e05-adef-47e4-97d3-62496c05490e · outbound

This paper cites https://github.

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents https://github

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-06T20:00:56.054221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:00:56.054221Z digest=sha256:50800b16873bf67fbef7384252ff453dac6ef945317f7dc69efb8295ae489a02

Observation dde05f2d-f7b4-4d0d-9660-c04418cba721 · outbound

This paper cites GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation.

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-06T20:00:56.113141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:00:56.113141Z digest=sha256:5e191fa1086bf5a11d89f0792ac73a13942cc121a16be226040f6171e4eea8dc

Pith citing papers

Observation e2c1e0eb-253e-437b-ba48-d56990a7624b · inbound

OpsLLM: Construction of Large Language Model for Software Operations with Multi-stage Learning cites this paper.

OpsLLM: Construction of Large Language Model for Software Operations with Multi-stage Learning Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-10T22:15:48.624800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-10T20:07:07.548384Z digest=sha256:48fb8c315f3d233a018ad9ab240e26043a1c7389b9746bfd8833cbfe787d6b88

Observation a1a5c31f-b2d1-4d79-ac21-ef82865b8c0b · inbound

OpsLLM: Construction of Large Language Model for Software Operations with Multi-stage Learning cites this paper.

OpsLLM: Construction of Large Language Model for Software Operations with Multi-stage Learning Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-13T06:27:24.604139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-13T06:25:16.650306Z digest=sha256:0c4dc1f5f6de816c14b0714da7ef585e4d8b3ab16341da7f133c41b4d394fade

Observation 5875d3a8-1187-4099-a349-9a4a1244a743 · inbound

OpsLLM: Construction of Large Language Model for Software Operations with Multi-stage Learning cites this paper.

OpsLLM: Construction of Large Language Model for Software Operations with Multi-stage Learning Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-03T02:30:33.667986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T02:30:33.667986Z digest=sha256:e7c37243151664654d51f969946321643f91a703330c7df7c1e1cc40d0d34149