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Paper Citation Record · LEDGER

Synthetic Dialogue Dataset Generation using LLM Agents

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2401.17461.

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

pith.paper-citation-record.v1
2401.17461 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:10:19.380284Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T17:02:24.240613Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 576df5ef-aac9-4c9f-8e19-5c6a6191d2c2 · inbound

MIMDE: Exploring the Use of Synthetic vs Human Data for Evaluating Multi-Insight Multi-Document Extraction Tasks cites this paper.

MIMDE: Exploring the Use of Synthetic vs Human Data for Evaluating Multi-Insight Multi-Document Extraction Tasks Synthetic Dialogue Dataset Generation using LLM Agents

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-12T05:58:26.123358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:58:26.123358Z digest=sha256:64c35be52a22d508f4af9fb05151154c44649deb8febaf58686ca3c7dd4f69c0

Observation 21d32cc4-2c8e-4ef4-bda0-96754a00f592 · inbound

ResoFilter: Fine-grained Synthetic Data Filtering for Large Language Models through Data-Parameter Resonance Analysis cites this paper.

ResoFilter: Fine-grained Synthetic Data Filtering for Large Language Models through Data-Parameter Resonance Analysis Synthetic Dialogue Dataset Generation using LLM Agents

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-11T11:59:28.182130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:59:28.182130Z digest=sha256:ebc486a9a6c2a8487be16e390a7e8f04c2d53d69a56832da5fdbb0d4732e1d26

Observation 1ada69b1-80bf-4472-a7eb-d6e712f7677b · inbound

DeepThink: Aligning Language Models with Domain-Specific User Intents cites this paper.

DeepThink: Aligning Language Models with Domain-Specific User Intents Synthetic Dialogue Dataset Generation using LLM Agents

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-08T19:08:57.496779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T19:08:57.496779Z digest=sha256:261fc7c6d3a918a837ffc271bdebea9d97a0fe606713f5ca6af1751ab9510b32

Observation 220944f6-2a3a-4268-b3af-10d298f7bd51 · inbound

Measuring Diversity in Synthetic Datasets cites this paper.

Measuring Diversity in Synthetic Datasets Synthetic Dialogue Dataset Generation using LLM Agents

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-08T04:54:50.557083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T04:54:50.557083Z digest=sha256:b40f6eba2761860359f5320246f04dc3d38a8e04f07cb1684614205f906451ea

Observation 002c633e-e099-48bf-8e39-e1ee29d0c66e · inbound

Improving Automated Secure Code Reviews: A Synthetic Dataset for Code Vulnerability Flaws cites this paper.

Improving Automated Secure Code Reviews: A Synthetic Dataset for Code Vulnerability Flaws Synthetic Dialogue Dataset Generation using LLM Agents

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-16T11:10:19.380284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:10:19.380284Z digest=sha256:abc1efe68e04904a2231f8ec5b83d0c30855381c96948c47b7c25185684b5849

Observation 779e10bb-b056-4eaa-afea-d535442dcd85 · inbound

Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration cites this paper.

Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration Synthetic Dialogue Dataset Generation using LLM Agents

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-07T05:33:52.790878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:33:52.790878Z digest=sha256:e7764bf20edd19ebc06863beda73d83494f0403cc62d9a1fe15b044d41cfc830

Observation ab493dd5-e709-427f-b346-f5071158885a · inbound

DatasetAgent: A Novel Multi-Agent System for Auto-Constructing Datasets from Real-World Images cites this paper.

DatasetAgent: A Novel Multi-Agent System for Auto-Constructing Datasets from Real-World Images Synthetic Dialogue Dataset Generation using LLM Agents

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-06T18:19:58.827943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:19:58.827943Z digest=sha256:321b878633b61d746d736ca33fd495a0a3f752a50fc547fffac44ba9de5445bb

Observation 9f3562d8-d76f-428c-b6fc-862e55cf20a0 · inbound

Separation Logic of Generic Resources via Sheafeology cites this paper.

Separation Logic of Generic Resources via Sheafeology Synthetic Dialogue Dataset Generation using LLM Agents

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-06T05:22:28.669764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T05:22:28.669764Z digest=sha256:b08d2895fb454a52477ac980c8189517d5d9f64c8bed9cd4d9bd9effaeb2b1a4

Observation 927e7aa8-7667-4a07-b965-6b988d96385a · inbound

DiscussLLM: Teaching Large Language Models When to Speak cites this paper.

DiscussLLM: Teaching Large Language Models When to Speak Synthetic Dialogue Dataset Generation using LLM Agents

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-21T22:10:42.323259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-21T22:09:03.740109Z digest=sha256:88353ee3c50766813b3b0e00e417495a078d11021bcf0d142f349dedf4ef458b

Observation 0078284d-ed96-4856-91de-475d2c58f8f0 · inbound

UniD$^3$: A Knowledge Graph-Enhanced RAG Framework for Drug-Disease Discovery and Reasoning cites this paper.

UniD$^3$: A Knowledge Graph-Enhanced RAG Framework for Drug-Disease Discovery and Reasoning Synthetic Dialogue Dataset Generation using LLM Agents

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-06-28T17:02:24.241995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-06-28T16:59:11.402443Z digest=sha256:0ed9b6ee4dc92a53101780ca8929b214ec0df7cc6f0d62dfd043cb6ec5875df5