Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T04:07:20.752825Z
Paper Citation Record · LEDGER
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences
As of 21 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2506.11681.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
23 of 23 outbound references displayed
- verified exact0
- verified fuzzy11
- unresolved12
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation f4d56378-7bbd-4e86-b23f-d7b4f3ac01f7 · outbound
This paper cites Saggion and G.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences Saggion and G
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:07:21.065744Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation ecc6c88d-8cc1-4ed9-89d0-570420917c2c · outbound
This paper cites Data-Driven Sentence Simplification: Survey and Benchmark,.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences Data-Driven Sentence Simplification: Survey and Benchmark,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:07:21.056032Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation fd430b44-6b67-4863-a96a-dddc8a8310f3 · outbound
This paper cites Sentence Simplification via Large Language Models.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences Sentence Simplification via Large Language Models
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:07:20.689089Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a572c798-71eb-4b46-8727-88ff71b32cc8 · outbound
This paper cites GPT-4o: Multimodal Language Model,.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences GPT-4o: Multimodal Language Model,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:07:21.045726Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 37903acd-02da-4a57-a138-f025a2051102 · outbound
This paper cites Gemini: A Family of Highly Capable Multimodal Models.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences Gemini: A Family of Highly Capable Multimodal Models
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:07:20.695753Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a065aee9-d31d-4af3-b823-7ba40d46f3ad · outbound
This paper cites Introducing Perplexity Deep Research,.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences Introducing Perplexity Deep Research,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:07:21.035974Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 49c51ea4-edee-4977-80bc-455b7e3bd732 · outbound
This paper cites The Prompt Report: A Systematic Survey of Prompt Engineering Techniques.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences The Prompt Report: A Systematic Survey of Prompt Engineering Techniques
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:07:20.702857Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b0c474b0-8ec8-40d5-aa34-16241a01a799 · outbound
This paper cites Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:07:20.706134Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 06e3e99c-a91e-45d3-bfc8-bf4c689c6649 · outbound
This paper cites Chain-of-thought prompting elicits reasoning in large language models,.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences Chain-of-thought prompting elicits reasoning in large language models,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:07:21.024991Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 89c3f0a4-dab9-4733-9e95-06b0435d734a · outbound
This paper cites A Survey on In-context Learning.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences A Survey on In-context Learning
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:07:20.711774Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 06ab692b-3399-4020-a5a0-c0ed5e735a2d · outbound
This paper cites Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:07:20.715124Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 35532825-4ccf-412c-b91e-c92d29b9ed6f · outbound
This paper cites Meta-in-context learning in large language models,.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences Meta-in-context learning in large language models,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:07:21.012249Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e8aec661-d4a1-4a37-8eaa-da9e4866179b · outbound
This paper cites Large Language Model based Multi-Agents: A Survey of Progress and Challenges.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences Large Language Model based Multi-Agents: A Survey of Progress and Challenges
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:07:20.721584Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ccf76b2d-4a6e-490b-acca-e0f70eeafdea · outbound
This paper cites Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:07:20.724941Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ac392171-0ad0-4c85-a993-5aacf3404329 · outbound
This paper cites Multi-Stage Prompting for Next Best Agent Recommendations in Adaptive Workflows,.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences Multi-Stage Prompting for Next Best Agent Recommendations in Adaptive Workflows,
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:07:20.998161Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation f7b04c6d-bf80-4b09-a7b8-a17ac92af637 · outbound
This paper cites Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:07:20.731350Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cd95191b-5292-40de-ba17-104aaca2da7c · outbound
This paper cites Multi-Agent Collaboration Mechanisms: A Survey of LLMs.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences Multi-Agent Collaboration Mechanisms: A Survey of LLMs
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:07:20.734462Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e0ed1dcd-24cc-492b-a63d-c55a136f8fd0 · outbound
This paper cites LLM Multi-Agent Systems: Challenges and Open Problems.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences LLM Multi-Agent Systems: Challenges and Open Problems
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:07:20.737667Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e0266041-d159-46df-a022-dfcc99a9d8cd · outbound
This paper cites Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:07:20.740761Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 74f86f1e-7733-49ef-81a5-09f40a5129f8 · outbound
This paper cites Automated program synthesis from object-oriented natural language for computer games,.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences Automated program synthesis from object-oriented natural language for computer games,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:07:20.986806Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2cc26027-c466-4356-bd4b-2a246adaac46 · outbound
This paper cites Multi-phase context vectors for generating feedback for natural-language based programming,.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences Multi-phase context vectors for generating feedback for natural-language based programming,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:07:20.974563Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 633044bd-0897-40ec-a5c0-9d8e8f566d00 · outbound
This paper cites On the ethical considerations of text simplification,.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences On the ethical considerations of text simplification,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:07:20.954845Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 08d28c22-7f9f-4d74-bd0a-99b51cd40e36 · outbound
This paper cites Can knowledge graphs reduce hallucinations in LLMs? A survey,.
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences Can knowledge graphs reduce hallucinations in LLMs? A survey,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:07:20.919987Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Pith citing papers
No inbound Pith citation observations are available.