Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring | Paper Citation Record

Observation 4ad9393f-be89-466d-878f-0caa3ec0c182 · outbound

This paper cites GPT-4 Technical Report.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring GPT-4 Technical Report

Reference 1

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Observation 30ba1944-0bfd-42ac-a281-8d30b8c7c3d1 · outbound

This paper cites Qwen2.5-VL Technical Report.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Qwen2.5-VL Technical Report

Reference 2

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This paper cites Sharegpt4v: Improving large multi-modal models with better captions.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Sharegpt4v: Improving large multi-modal models with better captions

Reference 3

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This paper cites Sharegpt4video: Improving video understanding and generation with better captions.Advances in Neural Information Processing Systems, 37:19472– 19495, 2024.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Sharegpt4video: Improving video understanding and generation with better captions.Advances in Neural Information Processing Systems, 37:19472– 19495, 2024

Reference 4

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This paper cites Learning with Instance-Dependent Label Noise: A Sample Sieve Approach.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Learning with Instance-Dependent Label Noise: A Sample Sieve Approach

Reference 5

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This paper cites Imagenet: A large- scale hierarchical image database.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Imagenet: A large- scale hierarchical image database

Reference 6

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This paper cites Vlmevalkit: An open-source toolkit for evaluating large multi-modality models.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Vlmevalkit: An open-source toolkit for evaluating large multi-modality models

Reference 7

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This paper cites Vector-valued property elicitation.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Vector-valued property elicitation

Reference 8

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Observation 14ba60b2-1b58-487e-ac07-5b72ea824f5d · outbound

This paper cites Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned

Reference 9

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Observation 2c1b38ed-daa9-42f2-93fe-2a0f6e84ebd8 · outbound

This paper cites Strictly proper scoring rules, prediction, and estimation.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Strictly proper scoring rules, prediction, and estimation

Reference 10

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Observation b7e437ff-ee46-408d-b8be-aa5edf99531d · outbound

This paper cites Scaling Laws and Interpretability of Learning from Repeated Data.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Scaling Laws and Interpretability of Learning from Repeated Data

Reference 11

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Observation 0e9d375d-710f-41b2-9868-d7a52447f854 · outbound

This paper cites An information theoretic framework for designing information elicitation mechanisms that reward truth-telling.ACM Transactions on Economics and Computation (TEAC), 7(1):1–33, 2019.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring An information theoretic framework for designing information elicitation mechanisms that reward truth-telling.ACM Transactions on Economics and Computation (TEAC), 7(1):1–33, 2019

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:39.009870Z

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Observation 70533f49-1bab-44e3-ae3d-91547fc7710a · outbound

This paper cites Deduplicating training data makes language mod- els better.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Deduplicating training data makes language mod- els better

Reference 13

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Observation 0de36734-6d83-4398-bdc7-e44b15cb81d5 · outbound

This paper cites Seed-bench: Benchmarking multimodal large language models.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Seed-bench: Benchmarking multimodal large language models

Reference 14

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This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 15

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This paper cites BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 16

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Observation 22da6bf4-8d74-45ee-8498-3377cf10238f · outbound

This paper cites Align before fuse: Vision and language representation learning with momentum distillation.Advances in neural information processing systems, 34:9694–9705, 2021.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Align before fuse: Vision and language representation learning with momentum distillation.Advances in neural information processing systems, 34:9694–9705, 2021

Reference 17

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Observation 5bc5edde-9799-4984-b87b-e4fb546d2577 · outbound

This paper cites Georeasoner: Geo-localization with reasoning in street views using a large vision-language model.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Georeasoner: Geo-localization with reasoning in street views using a large vision-language model

Reference 18

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Observation bb6c0603-795f-4f8a-ae16-3ecaf460942b · outbound

This paper cites A survey of multimodel large language models.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring A survey of multimodel large language models

Reference 19

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Observation 0191fa64-8446-4708-adc2-14f584b20a37 · outbound

This paper cites Improved baselines with visual instruction tuning.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Improved baselines with visual instruction tuning

Reference 20

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Observation e4a3d8ae-3b10-41ca-ba52-10ed7c1b53c1 · outbound

This paper cites Visual instruction tuning.Advances in neural information processing systems, 36, 2024.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Visual instruction tuning.Advances in neural information processing systems, 36, 2024

Reference 21

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Observation b0e39791-a56a-44c4-af38-3cb6460f469d · outbound

This paper cites Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond

Reference 22

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Observation 12d76d91-23a7-4af8-a09b-50373bb53c8e · outbound

This paper cites Classification with noisy labels by importance reweighting.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Classification with noisy labels by importance reweighting

Reference 23

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Observation b2ac3370-907e-4a51-954e-6ea52654f70b · outbound

This paper cites Peer loss functions: Learning from noisy labels without knowing noise rates.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Peer loss functions: Learning from noisy labels without knowing noise rates

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:38.284256Z

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Observation c87d66bf-942d-4383-b018-bf7a84b3cedc · outbound

This paper cites Auditing for federated learning: A model elicitation approach.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Auditing for federated learning: A model elicitation approach

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:38.093262Z

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Observation 46d9ba33-eec9-4b95-968a-4838c0934b48 · outbound

This paper cites Incentives for Federated Learning: a Hypothesis Elicitation Approach.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Incentives for Federated Learning: a Hypothesis Elicitation Approach

Reference 26

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This paper cites Mmbench: Is your multi-modal model an all-around player? InEuropean conference on computer vision, pages 216–233.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Mmbench: Is your multi-modal model an all-around player? InEuropean conference on computer vision, pages 216–233

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:37.788379Z

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Observation cc976fd7-c43b-4104-81af-e25e912b1dd7 · outbound

This paper cites Inter-gps: Interpretable geometry problem solving with formal language and symbolic reasoning.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Inter-gps: Interpretable geometry problem solving with formal language and symbolic reasoning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:37.410291Z

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This paper cites Learn to explain: Multimodal reasoning via thought chains for science question answering.Advances in Neural Information Processing Systems, 35:2507–2521, 2022.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Learn to explain: Multimodal reasoning via thought chains for science question answering.Advances in Neural Information Processing Systems, 35:2507–2521, 2022

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:37.146429Z

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This paper cites ChartQA: A benchmark for question answering about charts with visual and logical reasoning.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring ChartQA: A benchmark for question answering about charts with visual and logical reasoning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:36.902481Z

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Observation 4fc54a44-230e-441f-b27f-4f425efcf94c · outbound

This paper cites Infographicvqa.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Infographicvqa

Reference 31

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Observation afb1f0cd-e555-43f4-ad44-d723aacfee33 · outbound

This paper cites Docvqa: A dataset for vqa on document images.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Docvqa: A dataset for vqa on document images

Reference 32

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Observation 6611d50b-d8cf-4e65-874a-1de07b6804db · outbound

This paper cites Measures of the value of information.Proceedings of the National Academy of Sciences, 42(9):654–655, 1956.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Measures of the value of information.Proceedings of the National Academy of Sciences, 42(9):654–655, 1956

Reference 33

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Observation e972d6d3-5951-4852-9941-156466a4ab31 · outbound

This paper cites Mm1: methods, analysis and insights from multimodal llm pre-training.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Mm1: methods, analysis and insights from multimodal llm pre-training

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:36.624517Z

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Observation c3283e3e-017f-4ff2-bbc8-70f754b0f427 · outbound

This paper cites Learning with noisy labels.Advances in neural information processing systems, 26, 2013.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Learning with noisy labels.Advances in neural information processing systems, 26, 2013

Reference 35

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Observation 4f60497d-c39a-4ce8-892d-7765de9c0bc6 · outbound

This paper cites GPT-4o System Card.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring GPT-4o System Card

Reference 36

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This paper cites Token cleaning: Fine-grained data selection for llm supervised fine-tuning.arXiv preprint arXiv:2502.01968, 2025.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Token cleaning: Fine-grained data selection for llm supervised fine-tuning.arXiv preprint arXiv:2502.01968, 2025

Reference 37

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This paper cites Improving data efficiency via curating LLM-driven rating systems.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Improving data efficiency via curating LLM-driven rating systems

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:36.398236Z

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Observation c6f811bb-816f-4c85-bb27-f34c07dfd9ba · outbound

This paper cites Deep learning on a data diet: Finding important examples early in training.Advances in neural information processing systems, 34:20596–20607, 2021.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Deep learning on a data diet: Finding important examples early in training.Advances in neural information processing systems, 34:20596–20607, 2021

Reference 39

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This paper cites Estimating training data influence by tracing gradient descent.Advances in Neural Information Processing Systems, 33:19920–19930, 2020.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Estimating training data influence by tracing gradient descent.Advances in Neural Information Processing Systems, 33:19920–19930, 2020

Reference 40

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This paper cites Learning transferable visual models from natural language supervision.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Learning transferable visual models from natural language supervision

Reference 41

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Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Unresolved cited work

Reference 42

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Observation d0470a9d-3d60-48c5-8e65-48916d947574 · outbound

This paper cites A-okvqa: A benchmark for visual question answering using world knowledge.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring A-okvqa: A benchmark for visual question answering using world knowledge

Reference 43

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Observation 56106ba5-1a86-4f0b-ba97-ab0d8b347d06 · outbound

This paper cites On the diversity and realism of distilled dataset: An efficient dataset distillation paradigm.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring On the diversity and realism of distilled dataset: An efficient dataset distillation paradigm

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:36.170849Z

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Observation 7b3927fe-ff0c-474b-b51b-96e9b17d00ca · outbound

This paper cites Data pruning via moving-one-sample-out.Advances in Neural Information Processing Systems, 36, 2024.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Data pruning via moving-one-sample-out.Advances in Neural Information Processing Systems, 36, 2024

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:35.840485Z

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Observation 20c001ef-ef46-433d-bc5d-bbc8ba25af34 · outbound

This paper cites Cambrian-1: A fully open, vision-centric exploration of multimodal llms.Advances in Neural Information Processing Systems, 37:87310–87356, 2024.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Cambrian-1: A fully open, vision-centric exploration of multimodal llms.Advances in Neural Information Processing Systems, 37:87310–87356, 2024

Reference 46

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This paper cites The all-seeing project v2: Towards general relation comprehension of the open world.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring The all-seeing project v2: Towards general relation comprehension of the open world

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:35.541443Z

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Observation af091e3c-2d7c-41b3-a49a-e8e79d5cb94c · outbound

This paper cites Sample elicitation.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Sample elicitation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:35.282413Z

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Observation fde1f571-bed4-4688-ad2d-71d0729f0b87 · outbound

This paper cites To smooth or not? when label smoothing meets noisy labels.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring To smooth or not? when label smoothing meets noisy labels

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:34.964366Z

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Observation 1a554789-048c-4340-a941-dc18e09cb828 · outbound

This paper cites Measuring and Reducing LLM Hallucination without Gold-Standard Answers.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Measuring and Reducing LLM Hallucination without Gold-Standard Answers

Reference 50

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Observation 7ccea7ff-2882-4d31-b123-f4cae785a0af · outbound

This paper cites Learning with noisy labels revisited: A study using real-world human annotations.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Learning with noisy labels revisited: A study using real-world human annotations

Reference 51

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This paper cites Icm-assistant: Instruction-tuning multimodal large language models for rule-based explainable image content moderation.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Icm-assistant: Instruction-tuning multimodal large language models for rule-based explainable image content moderation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:34.759912Z

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Observation bc6e41dc-8225-416a-a68d-596d15e7c493 · outbound

This paper cites Less: Selecting influential data for targeted instruction tuning.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Less: Selecting influential data for targeted instruction tuning

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:34.601313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cd9fed96-cc9f-4762-a865-081c58f7fc22 · outbound

This paper cites LESS: Selecting influential data for targeted instruction tuning.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring LESS: Selecting influential data for targeted instruction tuning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:34.295376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e3d59799-1e31-4512-a6e6-12104163dc4c · outbound

This paper cites Qwen2.5 Technical Report.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Qwen2.5 Technical Report

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:31.670492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce9d56b3-2f6b-41a0-83bc-a3d7535b8331 · outbound

This paper cites Robust noisy correspondence learning with equivariant similarity consistency.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Robust noisy correspondence learning with equivariant similarity consistency

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:34.116037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 39bb99c5-8a07-4aca-b3c6-5d2e52d1d2fb · outbound

This paper cites Teaching LMMs for Image Quality Scoring and Interpreting.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Teaching LMMs for Image Quality Scoring and Interpreting

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:18:32.636438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3b42e55c-aa2e-4695-bf44-64d7a03d3da7 · outbound

This paper cites Mitigating noisy correspondence by geometrical structure consistency learning.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Mitigating noisy correspondence by geometrical structure consistency learning

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:33.941718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b8512f64-df14-436e-bcad-a79f90feac1d · outbound

This paper cites Unmasking and improving data credibility: A study with datasets for training harmless language models.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Unmasking and improving data credibility: A study with datasets for training harmless language models

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:33.588451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 68c9754c-4a86-40c4-a2af-a13d9079a6fa · outbound

This paper cites Spatial Relationship.

Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring Spatial Relationship

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:33.309558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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