Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach | Paper Citation Record

Observation 501b7842-1eb0-4474-bac4-b19b7eab07d0 · outbound

This paper cites Fake news detection on social media: A data mining perspective,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Fake news detection on social media: A data mining perspective,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.650170Z

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Observation a90e0f00-698e-4da7-9a08-e766821cd45b · outbound

This paper cites Sciclops: Detecting and con- textualizing scientific claims for assisting manual fact-checking,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Sciclops: Detecting and con- textualizing scientific claims for assisting manual fact-checking,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.635264Z

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

Observation 148184a6-89c4-4efb-a7ec-32df4bc3a9a7 · outbound

This paper cites Tabfact: A large-scale dataset for table-based fact verification,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Tabfact: A large-scale dataset for table-based fact verification,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.620824Z

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

Observation d9714e39-e96f-4470-9596-9ae3f0106ebf · outbound

This paper cites FEVEROUS: Fact extraction and VERification over unstructured and structured in- formation,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach FEVEROUS: Fact extraction and VERification over unstructured and structured in- formation,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.604154Z

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Observation 25aae3cf-5599-4604-ae89-d073889f7375 · outbound

This paper cites Chain-of-table: Evolving tables in the reasoning chain for table understanding,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Chain-of-table: Evolving tables in the reasoning chain for table understanding,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.589879Z

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

Observation 05ae5d39-46b5-47bd-a3e1-f2b7017b36d1 · outbound

This paper cites Toward automated fact-checking: Detecting check-worthy factual claims by claimbuster,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Toward automated fact-checking: Detecting check-worthy factual claims by claimbuster,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.574736Z

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

Observation 8b536cbe-b101-4e3a-8852-285812ac4b1e · outbound

This paper cites Towards Automated Fact-Checking of Real-World Claims: Exploring Task Formulation and Assessment with LLMs.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Towards Automated Fact-Checking of Real-World Claims: Exploring Task Formulation and Assessment with LLMs

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:37.447012Z

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Observation d7eb5b2b-c587-48fc-97f1-f152204bdf81 · outbound

This paper cites Towards automatic numerical cross- checking: Extracting formulas from text,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Towards automatic numerical cross- checking: Extracting formulas from text,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.561330Z

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Observation 579ded7a-3adf-4c38-aadf-af8b08d7a92d · outbound

This paper cites [On- line].

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach [On- line]

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.546311Z

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

Observation 7715fea6-fad3-48be-8392-29779a1c0fc2 · outbound

This paper cites A survey on automated fact-checking,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach A survey on automated fact-checking,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.530016Z

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.

Observation 37d8c12d-75df-40a8-8cab-1ae7dfff1d8e · outbound

This paper cites Large language models are versatile decomposers: Decomposing evidence and questions for table-based reasoning,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Large language models are versatile decomposers: Decomposing evidence and questions for table-based reasoning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.514824Z

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.

Observation a21f683d-9ff9-4bc8-802b-bc26c33536ba · outbound

This paper cites Cracking tabular pre- sentation diversity for automatic cross-checking over numerical facts,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Cracking tabular pre- sentation diversity for automatic cross-checking over numerical facts,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.500208Z

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.

Observation c04293e1-dbf4-4cb1-b68d-c785e4269a3b · outbound

This paper cites Gpt-4 technical report,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Gpt-4 technical report,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.484973Z

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.

Observation 7b39e6e0-b121-4db6-954a-128e8faf6ddf · outbound

This paper cites Qwen2.5 Technical Report.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Qwen2.5 Technical Report

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:37.480277Z

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Observation 54c3810e-a2f0-463f-af03-99b65ca32025 · outbound

This paper cites Large language models and sentiment analysis in financial markets: A review, datasets, and case study,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Large language models and sentiment analysis in financial markets: A review, datasets, and case study,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.470352Z

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

Observation b6c666b1-c222-4c8a-a0aa-f23bc4b84d3b · outbound

This paper cites Structgpt: A general framework for large language model to reason over structured data,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Structgpt: A general framework for large language model to reason over structured data,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:37.489540Z

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Observation 20a8fca6-e0e4-4cdd-9edc-58b6bad09a0c · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach On the Opportunities and Risks of Foundation Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:37.493939Z

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Observation fcff3f98-88ab-4404-b395-f12137ae1095 · outbound

This paper cites Towards understanding factual knowledge of large language models,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Towards understanding factual knowledge of large language models,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.446065Z

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

Observation d13b3bb8-87d8-4d5e-ae8b-0d4db71dc4eb · outbound

This paper cites Hello gpt-4o,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Hello gpt-4o,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.430958Z

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.

Observation a8b70b42-9446-47ab-a2d1-cc1140e8323e · outbound

This paper cites Introducing openai o1,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Introducing openai o1,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.416573Z

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.

Observation 47eb0e41-5127-485a-9bef-de3164659ac6 · outbound

This paper cites Qwen2.5: A party of foundation models,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Qwen2.5: A party of foundation models,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:37.511339Z

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Observation 128c5279-2f9b-46b0-9cd2-4e73be7baae7 · outbound

This paper cites Deepseek-v3 technical report,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Deepseek-v3 technical report,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.392388Z

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.

Observation 39d4a470-b560-4666-a5fc-970d8f9da5bf · outbound

This paper cites Large language models as foundations for next-gen dense retrieval: A comprehensive empirical assessment,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Large language models as foundations for next-gen dense retrieval: A comprehensive empirical assessment,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.377964Z

Source-reported events for the cited work

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Observation 37d4e321-9d97-4197-9543-91bbf745ba46 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:37.522274Z

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Observation 8fa0c895-5585-4ed0-bfe5-a10d3c3c8f6f · outbound

This paper cites Large dual encoders are generalizable retrievers,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Large dual encoders are generalizable retrievers,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.352723Z

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.

Observation 2fde75fb-a65f-4be2-9504-c391948ee8a7 · outbound

This paper cites LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:37.529674Z

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Observation fce0c97f-3575-4ba1-b78f-5277783b16f0 · outbound

This paper cites PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:37.534258Z

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Observation cc6d1ce8-48a5-4bac-8df7-b87a3f93f2e1 · outbound

This paper cites Fine-tuning llama for multi-stage text retrieval,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Fine-tuning llama for multi-stage text retrieval,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.337705Z

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.

Observation 667d9ccb-03ca-4919-8d91-d3076e233e4f · outbound

This paper cites Generative representational instruction tuning,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Generative representational instruction tuning,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.322112Z

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.

Observation dd673a1c-49c5-4134-aeda-0ab0b7182b69 · outbound

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

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:37.548312Z

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Observation 410c76bc-a6c0-4563-a2f8-d7276f3a0b70 · outbound

This paper cites Investigating table-to-text generation capabilities of large language models in real- world information seeking scenarios,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Investigating table-to-text generation capabilities of large language models in real- world information seeking scenarios,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.307158Z

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.

Observation 9ba0764e-4cc8-4f1b-9d3f-6c05cdc88989 · outbound

This paper cites Uncovering limitations of large language models in information seeking from tables,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Uncovering limitations of large language models in information seeking from tables,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.292407Z

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.

Observation be004820-a85c-4b33-9033-d91f83e0cf01 · outbound

This paper cites Exploring the numerical reasoning capabilities of language models: A comprehensive analysis on tabular data,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Exploring the numerical reasoning capabilities of language models: A comprehensive analysis on tabular data,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.277511Z

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.

Observation daf6909d-1d37-4863-bedd-7c5a1c642e74 · outbound

This paper cites Introducing chatgpt,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Introducing chatgpt,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.261687Z

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.

Observation f548123c-3342-42d0-852b-1fd51d948f8f · outbound

This paper cites Tablellama: Towards open large generalist models for tables,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Tablellama: Towards open large generalist models for tables,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.243799Z

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.

Observation ee362d3c-11d7-441b-b5fc-1adc8df37d8a · outbound

This paper cites Table meets llm: Can large language models understand structured table data? a benchmark and empirical study,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Table meets llm: Can large language models understand structured table data? a benchmark and empirical study,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.227425Z

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.

Observation 692f33be-34b4-4dbd-9cdf-85779d5335ec · outbound

This paper cites Multimodal graph causal embedding for multimedia-based recommendation,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Multimodal graph causal embedding for multimedia-based recommendation,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.211431Z

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.

Observation 6a7b2ee2-741c-44cc-9a6d-4baecd363cbf · outbound

This paper cites Graph diffusion-based representation learning for sequential recommendation,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Graph diffusion-based representation learning for sequential recommendation,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.197351Z

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.

Observation 031d7a47-20b5-4742-8ee0-f41fbe1947e8 · outbound

This paper cites Dual variational graph reconstruction learning for social recommendation,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Dual variational graph reconstruction learning for social recommendation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.181699Z

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.

Observation 6308bd98-4281-453d-b0b2-5c5559d1b7dc · outbound

This paper cites Training language models to follow instructions with human feedback,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Training language models to follow instructions with human feedback,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:37.596200Z

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Observation 3c0ff44b-d624-41ad-8a7e-d81762610c01 · outbound

This paper cites Efficient and robust approxi- mate nearest neighbor search using hierarchical navigable small world graphs,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Efficient and robust approxi- mate nearest neighbor search using hierarchical navigable small world graphs,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:37.601106Z

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Observation 0820a175-4ba5-4ede-8cfd-9ff493740a5d · outbound

This paper cites The Faiss library.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach The Faiss library

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:37.605673Z

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Observation ed1aebed-d55b-4184-ac5c-fd83ed5f0317 · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach A simple framework for contrastive learning of visual representations,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:37.611136Z

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Observation e8542944-0195-4aad-b72f-233e31f6b879 · outbound

This paper cites Feature-aware contrastive learning with bidirectional transformers for sequential recommendation,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Feature-aware contrastive learning with bidirectional transformers for sequential recommendation,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.138276Z

Source-reported events for the cited work

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Observation 34bd8ad3-cce1-416b-9499-089d8eb32f4e · outbound

This paper cites Improving language understanding by generative pre-training,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Improving language understanding by generative pre-training,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:37.727810Z

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Observation 20279f59-52bb-49b3-8a81-b205ffdd9871 · outbound

This paper cites The traveling-salesman problem,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach The traveling-salesman problem,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:37.732140Z

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Observation 8b63f0a5-8a94-4261-9f6f-14b713397c19 · outbound

This paper cites In-context pretraining: Language modeling beyond document boundaries,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach In-context pretraining: Language modeling beyond document boundaries,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.103618Z

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.

Observation f1173884-bab5-48f2-ac28-8a71f02f4078 · outbound

This paper cites Transformers: State-of-the-art natural language processing,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Transformers: State-of-the-art natural language processing,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.088564Z

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.

Observation 6ec83589-912e-41b0-9a11-325ba3032990 · outbound

This paper cites Zero: memory optimizations toward training trillion parameter models,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Zero: memory optimizations toward training trillion parameter models,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.071949Z

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.

Observation 8193801a-8c88-461a-9892-8e52fd29835c · outbound

This paper cites Efficient memory management for large language model serving with pagedattention,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Efficient memory management for large language model serving with pagedattention,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.056199Z

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.

Observation b5f5a07d-048f-4391-8218-33a15d753969 · outbound

This paper cites Guideline learning for in- context information extraction,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Guideline learning for in- context information extraction,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.039234Z

Source-reported events for the cited work

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Observation da008c1c-c598-4345-b518-1509eb256a5b · outbound

This paper cites Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:37.761578Z

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Observation b97d6713-fbab-47ff-8c9b-aef5d039cc7a · outbound

This paper cites Gpt-4o mini: advancing cost-efficient intelli- gence,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Gpt-4o mini: advancing cost-efficient intelli- gence,

Reference 53

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Observation 4aeef6df-b6d0-4137-81d7-a632d92e550c · outbound

This paper cites Openai o3-mini,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Openai o3-mini,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:38.006237Z

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.

Observation 22edae2d-33b6-4cb5-ae83-ef946fbf5d5c · outbound

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

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:37.775312Z

Source-reported events for the cited work

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Observation 196a1a38-366a-41d4-ac85-c588aca01c45 · outbound

This paper cites Unsupervised Dense Information Retrieval with Contrastive Learning.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach Unsupervised Dense Information Retrieval with Contrastive Learning

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:37.780125Z

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Observation 2ead5db0-69e2-4f98-a7a7-144c9731591a · outbound

This paper cites The llama 3 herd of models,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach The llama 3 herd of models,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:37.978395Z

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.

Observation d927932e-33b3-40d4-96d9-223ad134cb0f · outbound

This paper cites An integrated data processing framework for pretraining foundation models,.

Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach An integrated data processing framework for pretraining foundation models,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:09:37.963730Z

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.

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