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Paper Citation Record · LEDGER

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries?

As of 18 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 0 inbound Pith citation observations for arXiv:2508.09631.

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

pith.paper-citation-record.v1
2508.09631 v1

Coverage vector

measured 75 of 75 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T20:59:10.513434Z

measured 75 of 75 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

75 of 75 outbound references displayed

  • verified exact1
  • verified fuzzy51
  • unresolved23
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bd607dec-8547-48f4-b0dd-35ede7778fdd · outbound

This paper cites GPT-4 Technical Report.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? GPT-4 Technical Report

Reference 1

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no resolver link, observed 2026-08-05T20:59:02.735673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:59:02.735673Z digest=sha256:805bce76d2eb0001ca110accbf5ce1af096333a6250e675806c0e5d4a2895d91

Observation 181a4154-1188-441c-b0b9-f6dc148e94ea · outbound

This paper cites A multi-agent deep reinforcement learning ap- proach for enhancement of covid-19 ct image segmentation.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? A multi-agent deep reinforcement learning ap- proach for enhancement of covid-19 ct image segmentation

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 966e747f-ad34-45d8-af67-d9a61c83606b · outbound

This paper cites Prompt augmentation for self-supervised text-guided image manipulation.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Prompt augmentation for self-supervised text-guided image manipulation

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:02.919704Z digest=sha256:17f5912e17271dc894a2951ddef850219078941451840587064a00030316a8a8

Observation 3135d801-d900-4f9c-8e10-33c70d3e9cec · outbound

This paper cites Video abstracts are associ- ated with an increase in research reports citations, views and social attention: a cross-sectional study.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Video abstracts are associ- ated with an increase in research reports citations, views and social attention: a cross-sectional study

Reference 4

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raw_fallback, observed 2026-08-05T20:59:14.656857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 4a357561-df9d-4761-8658-9c749cd0368f · outbound

This paper cites A reinforced lunar dynamo recorded by chang’e-6 farside basalt.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? A reinforced lunar dynamo recorded by chang’e-6 farside basalt

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 5a0c50d7-c2db-416c-86c5-367190e50394 · outbound

This paper cites Videocrafter2: Overcoming data limitations for high-quality video diffu- sion models.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Videocrafter2: Overcoming data limitations for high-quality video diffu- sion models

Reference 6

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raw_fallback, observed 2026-08-05T20:59:14.624731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:03.219330Z digest=sha256:e9d5eea3873973a6ff873afd9cdf9419d1d9578ef9bc08b76840e4029002c9cc

Observation 46f63307-f076-434f-bf9f-f48a8a94d3b3 · outbound

This paper cites Hallo2: Long-Duration and High-Resolution Audio-Driven Portrait Image Animation.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Hallo2: Long-Duration and High-Resolution Audio-Driven Portrait Image Animation

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 199d7580-e496-420b-9cff-f2ef2d910461 · outbound

This paper cites Collaborating with language models for embodied reason- ing.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Collaborating with language models for embodied reason- ing

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation f84f7e33-0701-43c3-9dd4-5b3433fd0d0b · outbound

This paper cites Deepseek-r1: Incentivizing reasoning capa- bility in llms via reinforcement learning, 2025.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Deepseek-r1: Incentivizing reasoning capa- bility in llms via reinforcement learning, 2025

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:03.462824Z digest=sha256:8dada20e2f2cfc6b3141786e9c8dc5c61060d9b2ededbb721b95162083210131

Observation 66538412-dfbb-438c-b52a-3c242bf2df58 · outbound

This paper cites Lon- grope: extending llm context window beyond 2 million to- kens.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Lon- grope: extending llm context window beyond 2 million to- kens

Reference 10

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raw_fallback, observed 2026-08-05T20:59:14.578390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 69783143-aa2a-4d13-8c1f-1e325ebd2a10 · outbound

This paper cites Self- collaboration code generation via chatgpt.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Self- collaboration code generation via chatgpt

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 216ee991-b97f-4c47-b14f-4a0d28960b75 · outbound

This paper cites Cosyvoice 2: Scalable streaming speech synthe- sis with large language models.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Cosyvoice 2: Scalable streaming speech synthe- sis with large language models

Reference 12

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raw_fallback, observed 2026-08-05T20:59:14.544392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 04b945f9-6623-4ff2-875d-6d7bf316680d · outbound

This paper cites The Llama 3 Herd of Models.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? The Llama 3 Herd of Models

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:59:04.066882Z digest=sha256:cf20d91949d04b5a3e56ba7c86c374affdcdff360ba0d618aeafc0f3a24a551f

Observation 7d344fde-804b-4056-9242-111ab948f51c · outbound

This paper cites Audio-visual tools in science communication: the video abstract in ecology and environ- mental sciences.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Audio-visual tools in science communication: the video abstract in ecology and environ- mental sciences

Reference 14

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raw_fallback, observed 2026-08-05T20:59:14.527114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:04.228409Z digest=sha256:28bbbb28fdc4a6ad1bfce69c0ed3548ff30b59fb611421c17392455a3530e48a

Observation c80ef098-ae34-4e4f-b5d3-3f1473e2129c · outbound

This paper cites ComfyGen: Prompt-Adaptive Workflows for Text-to-Image Generation.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? ComfyGen: Prompt-Adaptive Workflows for Text-to-Image Generation

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation fe34ccfe-fae5-4d78-9899-f7342dd0d126 · outbound

This paper cites High-fidelity and freely controllable talking head video generation.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? High-fidelity and freely controllable talking head video generation

Reference 16

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raw_fallback, observed 2026-08-05T20:59:14.510429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 8f4f9bd7-f922-4eb5-9ea8-b2e18a7635b4 · outbound

This paper cites Large language model based multi- agents: A survey of progress and challenges.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Large language model based multi- agents: A survey of progress and challenges

Reference 17

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raw_fallback, observed 2026-08-05T20:59:14.494726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation bdc08d0b-710c-4f5f-8b13-a2591a832f9e · outbound

This paper cites An integrated guide for designing video abstracts using freeware and their emerging role in academic research advancement.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? An integrated guide for designing video abstracts using freeware and their emerging role in academic research advancement

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation eaef8c30-fd0c-47ea-8b0a-b24477495482 · outbound

This paper cites StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 718092d0-fba9-4d7d-bcce-2e47aa00c640 · outbound

This paper cites Video dif- fusion models.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Video dif- fusion models

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:59:05.058383Z digest=sha256:89c5d7e0e2c87aac8e4cfa522522d54d5f36295e44ed6e5e59bf01faf2a65f67

Observation ade0a0f1-11e9-4d97-8c5e-97362c7bb3df · outbound

This paper cites Metagpt: Meta pro- gramming for a multi-agent collaborative framework.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Metagpt: Meta pro- gramming for a multi-agent collaborative framework

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 5281388e-4b5f-4289-b47e-7c37e5633705 · outbound

This paper cites Inner monologue: Em- bodied reasoning through planning with language models.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Inner monologue: Em- bodied reasoning through planning with language models

Reference 22

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:05.301399Z digest=sha256:003609845084e2add9eaad29a0d5cf479fbcf42b5060ff9e5d800ef64c2e3c9a

Observation a78616ec-a85c-422c-95de-7d617d4c9a73 · outbound

This paper cites OpenAI o1 System Card.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? OpenAI o1 System Card

Reference 23

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:59:05.459446Z digest=sha256:c74d7b4ec72889c0bdc2994f72368d5b7c20f4a35e2dc3706c10bb0b9e7e3e14

Observation 91ce7756-a91a-4318-b381-6a81314b433e · outbound

This paper cites A comprehensive survey on process-oriented automatic text summarization with exploration of llm-based methods.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? A comprehensive survey on process-oriented automatic text summarization with exploration of llm-based methods

Reference 24

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Source-reported events for the cited work

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Observation 8b3abde2-4ef6-4fde-baff-7fb616bf7a2e · outbound

This paper cites Agentreview: Explor- ing peer review dynamics with llm agents.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Agentreview: Explor- ing peer review dynamics with llm agents

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation bbd03354-40b4-4244-81be-f43f6ff917a8 · outbound

This paper cites Text2video-zero: Text- to-image diffusion models are zero-shot video generators.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Text2video-zero: Text- to-image diffusion models are zero-shot video generators

Reference 26

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:59:05.865067Z digest=sha256:a2ff378a7035abaf671d29144a4322af3ae29cfadeffd6cbf2fb539a9d6f9034

Observation 1f7818f5-d1a7-4ac2-a237-4cbd8515414a · outbound

This paper cites �����������������������������.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? �����������������������������

Reference 27

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raw_fallback, observed 2026-08-05T20:59:14.396207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:05.981612Z digest=sha256:8a281956400c03411b782986abd4f07ea743497188a7c2a7e083d9f814781638

Observation c2ee3abd-2882-48a1-b8c4-ee5a8ea20915 · outbound

This paper cites A human-inspired reading agent with gist memory of very long contexts.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? A human-inspired reading agent with gist memory of very long contexts

Reference 28

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raw_fallback, observed 2026-08-05T20:59:14.382951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 80548bb0-0241-4358-9ae5-f0ad80d3b8ba · outbound

This paper cites Literature reviews with llm-based tools.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Literature reviews with llm-based tools

Reference 29

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raw_fallback, observed 2026-08-05T20:59:14.369545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation d4de2705-9de8-4dbf-a9e3-baf1f1153087 · outbound

This paper cites Camel: Communicative agents for” mind” exploration of large language model society.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Camel: Communicative agents for” mind” exploration of large language model society

Reference 30

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raw_fallback, observed 2026-08-05T20:59:14.354116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation ee0b6822-1159-40f1-aa9d-b4e9329baff4 · outbound

This paper cites an unresolved cited work.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Unresolved cited work

Reference 31

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unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation b529b061-7937-4e4d-8dab-809fb88315a2 · outbound

This paper cites Long-context llms struggle with long in-context learn- ing.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Long-context llms struggle with long in-context learn- ing

Reference 32

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raw_fallback, observed 2026-08-05T20:59:14.326098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:06.436942Z digest=sha256:be97b222906452f5299264947a72613ec2a4478eb24ddde6a45ab07bcbfec0cb

Observation 8ce3db84-1715-420f-8bb6-b4966485d396 · outbound

This paper cites ���������������������������������.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? ���������������������������������

Reference 33

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raw_fallback, observed 2026-08-05T20:59:14.311703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:06.530555Z digest=sha256:9cbb34ad4237e595a064b01575e0fb275b09abdd9498f0b9dedba5b3e901a826

Observation 4f8543bb-6364-47b8-bc70-dadeb349ee1c · outbound

This paper cites �������������������������������.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? �������������������������������

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:14.298239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:06.581561Z digest=sha256:ae1a2b9adc2e173a89658c7e36059f9e6046f00d5a3ffd721ac40784a4e67e57

Observation cdea6d74-196f-4a0c-9f36-b3e7098bfa17 · outbound

This paper cites Sora: Creating video from text.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Sora: Creating video from text

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-05T20:59:14.283394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:06.665743Z digest=sha256:d3d4f76505fe60a590b8ff0c1c6612c9b81d6f7afce6bcf71dbf6741969e96f9

Observation 675ecbb2-3768-4bea-ac37-133ad5f94acd · outbound

This paper cites ������������������������� ���������������.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? ������������������������� ���������������

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-05T20:59:14.268148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:06.817622Z digest=sha256:a521754e088756c199abc076946c15da876e9ec4994dea10952d9a769263d7c6

Observation 36bc5b4f-a87f-4d91-83a6-cf50b588a89b · outbound

This paper cites ���������������������.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? ���������������������

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:14.253984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:06.960833Z digest=sha256:9dde690266475ef8636515907077014d718ff0b80f3ec5ea93e537464583d42f

Observation b95066d2-853b-4ed3-bda8-05310409df13 · outbound

This paper cites Commu- nicative agents for software development.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Commu- nicative agents for software development

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:14.239715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:07.080780Z digest=sha256:08194020c15fb06930153cbcd5b005f464e31f5a8de9be6e2acfd68692342e19

Observation df772ebf-16c8-4335-9cf7-212a12b876e8 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Learning transferable visual models from natural language supervi- sion

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:14.224845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:07.254869Z digest=sha256:bb17766fec332b9d3822bfdbddeeeb2a99264e0b44e9139cdbba397b4ba105f7

Observation 8ba469c0-f546-4338-8ea7-ae62ec411099 · outbound

This paper cites Hierarchical text-conditional image gener- ation with clip latents.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Hierarchical text-conditional image gener- ation with clip latents

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:14.208764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:07.378732Z digest=sha256:7577645ab43116d0aeb5e7bf4a1b311d8d7610fdf4de66c812d8e84406bebc79

Observation cf4d7d98-e1f8-44db-9cc3-ee9879a7d4e9 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? High-resolution image synthesis with latent diffusion models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-05T20:59:07.490409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:59:07.490409Z digest=sha256:a7360815711d2a332a814f1596aab3fd29eb050055746c747c89576c617dec44

Observation 8a7d03e6-ab0d-45a2-b3ac-d338537790cc · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? U- net: Convolutional networks for biomedical image segmen- tation

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-05T20:59:07.643324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:59:07.643324Z digest=sha256:7a55b7671a21ff2cdcb34063a1cdb7ad2652366130b6fae5b6cc148b2e32fa90

Observation 4d5c261a-ba97-4928-819e-30bfbb34506d · outbound

This paper cites The emergence of Large Language Models (LLM) as a tool in literature reviews: an LLM automated systematic review.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? The emergence of Large Language Models (LLM) as a tool in literature reviews: an LLM automated systematic review

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-05T20:59:07.760848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:59:07.760848Z digest=sha256:2428fe2b46f73f1616e1f4609854d3ec88e3450dedbf8872c2fdc3b1d15fc13f

Observation e63ce154-6c21-4b1a-8759-4ce339fbf499 · outbound

This paper cites Laion- 400m: Open dataset of clip-filtered 400 million image-text pairs.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Laion- 400m: Open dataset of clip-filtered 400 million image-text pairs

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:14.061830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:07.846949Z digest=sha256:fcda84b27801d53165634ec357d82b57026b57368b12c6bbdc803b4df7402eb2

Observation f26a784b-8600-46a9-bdb1-f95f848a721e · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:14.046135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:07.908390Z digest=sha256:b40e473b9715164702ab0e5383652848e51b9703789f828a28a45c7597127607

Observation 018a5949-2663-40ac-ae79-17b7ed0c6dc7 · outbound

This paper cites Llm-planner: Few-shot grounded planning for embodied agents with large language models.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Llm-planner: Few-shot grounded planning for embodied agents with large language models

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:14.029876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:07.995028Z digest=sha256:e16d7f0d549bca87fda2f94949d5db89f08698c8f616a548a9bf742e50fa72ae

Observation 69fa7caa-086f-4a4a-9ca7-792b14fc5556 · outbound

This paper cites �����������������������������.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? �����������������������������

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:14.013184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:08.081064Z digest=sha256:5f851a38f5c708492d2f158ebe299221f114a2c6d81d88da37c7287bb4b9cee5

Observation 64591026-cfb8-4a07-8a53-436b4d6d3fa1 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Gemini: A Family of Highly Capable Multimodal Models

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-05T20:59:08.158182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:59:08.158182Z digest=sha256:33068e8ed133ab47660495445a7dffd224f02149ac8fd81c05c6d5f9d3bd397c

Observation ce1a136e-9015-4c84-aac6-4decf542c6d5 · outbound

This paper cites Overview of the nlpcc2024 shared task 6: Scientific literature survey generation.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Overview of the nlpcc2024 shared task 6: Scientific literature survey generation

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:13.858227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:08.261528Z digest=sha256:bf6be850ebbd43cf330580224a6d3718023bec190a1041a76137d59e302c5234

Observation 4dbe650d-73c3-488c-95e2-43c6b9b3ed71 · outbound

This paper cites Videotetris: Towards compositional text-to- video generation.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Videotetris: Towards compositional text-to- video generation

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:13.675738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:08.330052Z digest=sha256:b2ddf44c227e42e5c02ac235febaa49748b27e2a93354d31068a5d54d106acd7

Observation bb6bfd49-5c7c-409b-8dcf-22d6ce01cc56 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? LLaMA: Open and Efficient Foundation Language Models

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-05T20:59:08.380544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:59:08.380544Z digest=sha256:8c19c2f16c7fb432425f11b312019e1dd92a81341f1ea28b7627387f6c3ba60e

Observation 41f36e07-5938-46a3-8e58-d71ff2c3ee07 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-05T20:59:08.432000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:59:08.432000Z digest=sha256:77ab7ecaf473a1678b158e95c772bd3dacee0df7f2cc07bdfa6ea304228c1620

Observation 96c46766-2cb5-40c7-bcac-53ab418d57f3 · outbound

This paper cites SPAgent: Adaptive Task Decomposition and Model Selection for General Video Generation and Editing.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? SPAgent: Adaptive Task Decomposition and Model Selection for General Video Generation and Editing

Reference 53

Resolution
verified exact
local_arxiv, observed 2026-08-05T20:59:10.771660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:08.513719Z digest=sha256:addcececaf5e4eed836f43699750f6d719546ef99086233a1eb93b76845adef0

Observation d76aa040-8b3c-4188-8178-c89ddeea4cd7 · outbound

This paper cites Uhlenbeck and S.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Uhlenbeck and S

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:13.480361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:08.627402Z digest=sha256:cf7e4517fd8260bf4d5b3fc1af84aedb1d69baf51f1f56ff763af936f99a094c

Observation 4df1337d-daf8-4387-a61d-e722f0e4e568 · outbound

This paper cites Rectified diffusion: Straightness is not your need in rectified flow.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Rectified diffusion: Straightness is not your need in rectified flow

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:13.311393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:08.723473Z digest=sha256:f3f6e9ac0d0f20454b9c55a67a4710baaf6ee682832e720d69de5dc82a95cd61

Observation 401c8b56-8787-4f4d-8d51-98a4fff38f78 · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-05T20:59:08.785967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:59:08.785967Z digest=sha256:8b0abce62ae031692529cb76b1b81a699e64698d1e29c433d0f0132907cfc63b

Observation 0725b670-8b7e-4139-bb12-b87ac3609b10 · outbound

This paper cites Videocomposer: Compositional video synthesis with motion controllability.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Videocomposer: Compositional video synthesis with motion controllability

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-05T20:59:08.874390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:59:08.874390Z digest=sha256:fd0f1039c6b6d53346d954e14689e931d7b34a6229f956ef6513de75179612cd

Observation 453233f5-152a-4958-a43b-e4a6c131ba96 · outbound

This paper cites Autosurvey: Large language models can automatically write surveys.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Autosurvey: Large language models can automatically write surveys

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:13.022445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:08.966055Z digest=sha256:1229f6a3450b2cfcfe8a56dfabc5b61c18f060c98dac357c3c067998c639ab9f

Observation 6c86c74f-a012-499b-b2b3-f8e47f6344b1 · outbound

This paper cites Genartist: Multimodal llm as an agent for unified image gen- eration and editing.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Genartist: Multimodal llm as an agent for unified image gen- eration and editing

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:12.915812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:09.092879Z digest=sha256:81b4837081bb9e89ac76a36355ebb85788654efa9d763398933d3a7b94005e56

Observation a78268f3-3348-4e57-8225-909d14a5aa92 · outbound

This paper cites ����� � � � ������ � ��� � ��� � ������������.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? ����� � � � ������ � ��� � ��� � ������������

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:12.818208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:09.188373Z digest=sha256:e9aecd9eb06e8286af4d49657faf2a79d8132fbec4d0207698e640ee773f191c

Observation 0514b448-c3f2-4472-bf65-144d56492b25 · outbound

This paper cites Art-v: Auto-regressive text-to- video generation with diffusion models.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Art-v: Auto-regressive text-to- video generation with diffusion models

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:12.667767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:09.292007Z digest=sha256:f8e0a56522407ac97ede772d498a9a826245443782af0282a92d603c33f15694

Observation b792cbd0-43b1-49a6-bc6c-7dea03343f89 · outbound

This paper cites A survey on llm- generated text detection: Necessity, methods, and future di- rections.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? A survey on llm- generated text detection: Necessity, methods, and future di- rections

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:12.491862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:09.364793Z digest=sha256:7bf4a578437eedfa5d046838fa85efde1243a09aedb46d17e6ce34522499494c

Observation 59afe758-44f3-4f6e-b573-3f48654a272c · outbound

This paper cites DreamFactory: Pioneering Multi-Scene Long Video Generation with a Multi-Agent Framework.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? DreamFactory: Pioneering Multi-Scene Long Video Generation with a Multi-Agent Framework

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-05T20:59:09.468398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:59:09.468398Z digest=sha256:9d41058eae494d91572eafe037c0017a2d882e78443801ecd4a0564536826cae

Observation 4fbec8e1-6d0c-46f9-b9e9-8f2fa852184d · outbound

This paper cites Dynamicrafter: Animating open-domain images with video diffusion priors.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Dynamicrafter: Animating open-domain images with video diffusion priors

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:12.327560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:09.557965Z digest=sha256:e9214db2789364dfdd820285e8f26fa364d1ecf1b0a23ba9c6722f9dbb8df7a4

Observation adb46fcd-a11d-4ad0-93b8-01fa5b065ce2 · outbound

This paper cites Improving diffusion-based image synthesis with context pre- diction.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Improving diffusion-based image synthesis with context pre- diction

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:12.170661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:09.658357Z digest=sha256:ec19854343bfa41a979ebbf4449e03c214b081a2f7e0608b390fe5a322ce1d86

Observation 83cba100-f016-431d-a25f-537e56c8ce6f · outbound

This paper cites Mastering text-to-image diffu- sion: Recaptioning, planning, and generating with multi- modal llms.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Mastering text-to-image diffu- sion: Recaptioning, planning, and generating with multi- modal llms

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:12.002526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:09.707507Z digest=sha256:21b99e142bc3099c365e101ec67260634a2411703a0f371e11d15d6ec384c40f

Observation 2a0a7d6a-653a-4692-aeca-5910134ff8f7 · outbound

This paper cites MMaDA: Multimodal Large Diffusion Language Models.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? MMaDA: Multimodal Large Diffusion Language Models

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-05T20:59:09.817438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:59:09.817438Z digest=sha256:8c4821066e7074f64beed04e2e6c6187316c2ff268526f3e54a24b5c79c3fece

Observation 7f856a5c-6af4-4d3e-8d73-9442bfd21238 · outbound

This paper cites Mora: Enabling Generalist Video Generation via A Multi-Agent Framework.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Mora: Enabling Generalist Video Generation via A Multi-Agent Framework

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-05T20:59:09.911612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:59:09.911612Z digest=sha256:513c8f977fb2593bc227e1f5a6639770836607977d0528c665acfed11e83cf8b

Observation 46cd90fe-0cc2-4c21-9215-6b161393b60a · outbound

This paper cites Make pixels dance: High- dynamic video generation.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Make pixels dance: High- dynamic video generation

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:11.846403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:09.996250Z digest=sha256:c7d893c3f48cb5eb9f76e92a2bd98827e54dc9d46259186a2202a76818afc590

Observation 1b96f418-426a-4e91-80b4-a1d444153c92 · outbound

This paper cites I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-05T20:59:10.056404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:59:10.056404Z digest=sha256:50b9a860078534cc9523c33471bff8a3d53e384c83c59170ec5b4eb26759bdd4

Observation 87a0569a-1f1b-4005-a1fe-c37ec06cceea · outbound

This paper cites PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides

Reference 71

Resolution
unresolved
no resolver link, observed 2026-08-05T20:59:10.143664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:59:10.143664Z digest=sha256:13c9634af064227b29758ce6abfd0703546b876d17d3008af9eb28fb8eb1a0c0

Observation 77f15047-6a28-4d7a-90da-541849c436d2 · outbound

This paper cites Chatgpt research group for optimizing the crystallinity of mofs and cofs.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Chatgpt research group for optimizing the crystallinity of mofs and cofs

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:11.706694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:10.300348Z digest=sha256:07c795b6d9710c4b3fc9b46e9f95cb749e79a4db09c46eb98666141fef4d2910

Observation a08dc8f5-a417-4f3d-9422-8bde7995bfc5 · outbound

This paper cites Is llm a reliable re- viewer? a comprehensive evaluation of llm on automatic pa- per reviewing tasks.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? Is llm a reliable re- viewer? a comprehensive evaluation of llm on automatic pa- per reviewing tasks

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:11.526185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:10.374769Z digest=sha256:b10c4c1b7bf7c1e115f9062b6ddbbf4f8a431035c39dc3e3ef6864f7cc4f471d

Observation 23d7e805-660b-44cc-ad7a-89a991c68b59 · outbound

This paper cites An intelligent agentic system for complex image restoration problems.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? An intelligent agentic system for complex image restoration problems

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:11.382886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:10.446810Z digest=sha256:450945d68ad4e73e1c84067b0e0bada43456efe1b906e196a6695e38e8a10fc4

Observation 20ea2ffd-24fd-4dda-8e77-5db6753a6820 · outbound

This paper cites The impact of video abstract on citation counts: evidence from a retrospective cohort study of new journal of physics.

AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? The impact of video abstract on citation counts: evidence from a retrospective cohort study of new journal of physics

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:59:11.216277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-05T20:59:10.513434Z digest=sha256:d9b68be21d4a2783d03993f77b267aa8f44b7164031dcc023f4336aa90f240fe

Pith citing papers

No inbound Pith citation observations are available.