BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models | Paper Citation Record

Observation f6605ebf-5760-41ff-8d5f-207cf9fa725f · outbound

This paper cites A Survey of Multimodal Large Language Model from A Data-centric Perspective.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models A Survey of Multimodal Large Language Model from A Data-centric Perspective

Reference 1

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Observation 798ada72-7bba-43f2-b70f-b9c6fd8f1891 · outbound

This paper cites Understanding the limits of vision language models through the lens of the binding problem,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Understanding the limits of vision language models through the lens of the binding problem,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:56.466850Z

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Observation 7f9318cc-ccdc-47cf-adaf-5f451659b3aa · outbound

This paper cites Vision language models are blind,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Vision language models are blind,

Reference 3

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Observation 8e4113d0-fc0c-4237-9c6b-e888e47cdaff · outbound

This paper cites Bridging vision language model (VLM) evaluation gaps with a framework for scalable and cost-effective benchmark generation.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Bridging vision language model (VLM) evaluation gaps with a framework for scalable and cost-effective benchmark generation

Reference 4

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Observation 918d0586-7f49-4275-99ac-26ebcfd182cd · outbound

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

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Vlmevalkit: An open-source toolkit for evaluating large multi-modality models,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:56.145205Z

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Observation 9771b082-b0d9-4e98-a987-f8f79d71e0b5 · outbound

This paper cites UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T10:35:40.558440Z

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Observation 8a236e54-b53d-4350-96ea-4fa5bbd5bdf2 · outbound

This paper cites MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T10:35:40.742944Z

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Observation 15fec2b0-8f47-4e75-8cd6-cc5e476f6d72 · outbound

This paper cites A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T10:35:40.831220Z

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Observation 45644ae1-fae6-48d9-998e-b400adfe02e6 · outbound

This paper cites Chatbot arena: An open platform for evaluating llms by human preference,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Chatbot arena: An open platform for evaluating llms by human preference,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T10:35:40.936899Z

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Observation 1671100a-2f05-4f81-afdc-d5e48cd0d7d9 · outbound

This paper cites Are We on the Right Way for Evaluating Large Vision-Language Models?.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Are We on the Right Way for Evaluating Large Vision-Language Models?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T10:35:41.061652Z

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Observation 162f5834-9397-4106-a575-7443c5b65715 · outbound

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

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Laion-5b: An open large-scale dataset for training next generation image-text models,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:55.963492Z

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Observation 04a75bf2-b7eb-4506-850e-3d362dd4e933 · outbound

This paper cites Reproducible scaling laws for contrastive language- image learning,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Reproducible scaling laws for contrastive language- image learning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:55.782803Z

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Observation eebbae27-2485-4fef-bf04-5ac753034236 · outbound

This paper cites Mapping global dynamics of benchmark creation and saturation in artificial intelligence,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Mapping global dynamics of benchmark creation and saturation in artificial intelligence,

Reference 13

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Observation 7be181f3-e544-4f7b-9081-70c9b9536f2b · outbound

This paper cites Sugarcrepe: Fixing hackable benchmarks for vision- language compositionality,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Sugarcrepe: Fixing hackable benchmarks for vision- language compositionality,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:55.414344Z

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Observation e514ba06-7d22-4451-b669-6cee76028f3f · outbound

This paper cites Blender - a 3d modelling and rendering package,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Blender - a 3d modelling and rendering package,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:55.265725Z

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Observation 637f5d01-345a-491e-bf80-7c2d8ca3673a · outbound

This paper cites Is a picture worth a thousand words? delving into spatial reasoning for vision language models,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Is a picture worth a thousand words? delving into spatial reasoning for vision language models,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:55.111765Z

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Observation 910cafb6-7591-4ee6-b69e-bc74298e466a · outbound

This paper cites Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data

Reference 17

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Observation c4e5a6f5-c21c-49d0-b702-ec3f46dff493 · outbound

This paper cites Gpt-4.1, technical report,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Gpt-4.1, technical report,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:54.925232Z

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Observation a015eb3e-0c87-42fa-8231-ad83279b42e7 · outbound

This paper cites The llama 4 herd.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models The llama 4 herd

Reference 19

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Observation 914a2a46-733e-489c-add6-3b10a3d2a725 · outbound

This paper cites Visual instruction tuning,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Visual instruction tuning,

Reference 20

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Observation 7cc571b9-2bde-4424-9621-7bbba127e374 · outbound

This paper cites Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:54.584548Z

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Observation befd5ea0-e3ed-4888-9489-2620759315e3 · outbound

This paper cites MMBench: Is Your Multi-modal Model an All-around Player?.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models MMBench: Is Your Multi-modal Model an All-around Player?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T10:35:42.333123Z

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Observation eac71a37-6b61-4086-8b6b-49f56f195732 · outbound

This paper cites Towards vqa models that can read,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Towards vqa models that can read,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:54.377220Z

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Observation 9bf2c512-191e-4dd6-88b1-14c17ed52933 · outbound

This paper cites SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

Reference 24

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Observation 6cb976dd-c83a-4ef1-a2ea-d79b09f94e3f · outbound

This paper cites Making the v in vqa matter: Elevating the role of image understanding in visual question answering,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Making the v in vqa matter: Elevating the role of image understanding in visual question answering,

Reference 25

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Observation c9fe4d05-4ff3-4bef-90f8-08dae7389772 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 26

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Observation 83aee8e2-3d05-4441-911c-1dc9bd8deac6 · outbound

This paper cites Qwen2.5 Technical Report.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Qwen2.5 Technical Report

Reference 28

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Observation 3a6e5bd1-65ee-44e7-8efc-7b10b5a64350 · outbound

This paper cites GPT-4 Technical Report.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models GPT-4 Technical Report

Reference 29

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Observation bef3366d-d986-4a23-ad57-4dd43c100369 · outbound

This paper cites Video SimpleQA: Towards Factuality Evaluation in Large Video Language Models.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Video SimpleQA: Towards Factuality Evaluation in Large Video Language Models

Reference 30

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Observation 76b335eb-4e55-4c8a-8f85-d8fcba66c3b4 · outbound

This paper cites Inst-it: Boosting multimodal instance understanding via explicit visual prompt instruction tuning,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Inst-it: Boosting multimodal instance understanding via explicit visual prompt instruction tuning,

Reference 31

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Observation d5dd899c-b52d-40f1-970e-71f55c8f0382 · outbound

This paper cites Lvlm-count: Enhancing the counting ability of large vision-language models,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Lvlm-count: Enhancing the counting ability of large vision-language models,

Reference 32

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Observation 2346d8d3-942b-4089-a9a4-0944cc30390e · outbound

This paper cites Countgd: Multi-modal open-world counting,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Countgd: Multi-modal open-world counting,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:54.198197Z

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Observation 2c341945-1a06-40ae-b78b-f6d0eb76d961 · outbound

This paper cites Mutually-aware feature learning for few-shot object counting,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Mutually-aware feature learning for few-shot object counting,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:54.011104Z

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Observation 8c0c95e2-0056-4772-b6f7-f7940a896872 · outbound

This paper cites Point segment and count: A generalized framework for object counting,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Point segment and count: A generalized framework for object counting,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:53.787828Z

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Observation 0cd3df1a-b09a-4cb4-b3a5-a6eaa402bac7 · outbound

This paper cites Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models

Reference 36

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Observation 332a7eb4-7a85-4d22-99b8-daabd9206de5 · outbound

This paper cites Tallyqa: Answering complex counting questions,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Tallyqa: Answering complex counting questions,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:53.589079Z

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Observation ffd9d218-205a-4628-9721-cc2eae846a7c · outbound

This paper cites Counting everyday objects in everyday scenes,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Counting everyday objects in everyday scenes,

Reference 38

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Observation 11207b17-0230-4abb-8952-74b945e601ff · outbound

This paper cites Pixel-wise crowd understanding via synthetic data,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Pixel-wise crowd understanding via synthetic data,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:53.240946Z

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Observation 2db4891d-c71b-4b55-a394-126750cb2bec · outbound

This paper cites Nwpu-moc: a benchmark for fine-grained multicategory object counting in aerial images,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Nwpu-moc: a benchmark for fine-grained multicategory object counting in aerial images,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:53.030822Z

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Observation 0ea5f7f5-25b9-4990-8352-8fe4af0d8431 · outbound

This paper cites An Empirical Analysis on Spatial Reasoning Capabilities of Large Multimodal Models.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models An Empirical Analysis on Spatial Reasoning Capabilities of Large Multimodal Models

Reference 41

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Observation 61d2f55d-3e29-4091-92b3-383a5c5938ae · outbound

This paper cites SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models

Reference 42

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Observation ef369034-fb0f-4a96-8549-ffb33799306d · outbound

This paper cites AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?

Reference 43

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Observation 823ada39-8eba-41a3-8437-542e6c3aacf1 · outbound

This paper cites Text-to-Image Cross-Modal Generation: A Systematic Review.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Text-to-Image Cross-Modal Generation: A Systematic Review

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:35:47.774922Z

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Observation 89735524-0b96-4f22-8318-7b852d34b7ce · outbound

This paper cites A Survey on Hallucination in Large Vision-Language Models.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models A Survey on Hallucination in Large Vision-Language Models

Reference 45

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Observation 54da10d8-9089-4e01-a6e2-12d0846bc6e2 · outbound

This paper cites Task Me Anything.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Task Me Anything

Reference 46

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Observation 816c111e-c13c-4b08-a45c-7ce7ba3fbb20 · outbound

This paper cites ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models

Reference 47

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Observation 80a0f0c5-2036-4c6f-85be-c78e0e6a84f2 · outbound

This paper cites A new benchmark: On the utility of synthetic data with blender for bare supervised learning and downstream domain adaptation,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models A new benchmark: On the utility of synthetic data with blender for bare supervised learning and downstream domain adaptation,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:52.822487Z

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Observation feb10324-1938-47ff-a4ff-fc8f6981218f · outbound

This paper cites PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding

Reference 49

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Observation 44e4c400-def7-4953-afee-b2e14ea5c6b2 · outbound

This paper cites A survey of synthetic data augmentation methods in machine vision,.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models A survey of synthetic data augmentation methods in machine vision,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:52.600624Z

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Observation 436010c1-1f0f-436c-b484-c25ccb20e378 · outbound

This paper cites PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding

Reference 51

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Observation 1309fa9b-8503-40c2-9b3a-5df24b3b0822 · outbound

This paper cites BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T10:35:45.109989Z

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Observation 5777d1c2-26d9-4cd0-a2f6-e16c98c6cd54 · outbound

This paper cites Mistral small 3.1.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Mistral small 3.1

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:52.424049Z

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Gemma 3 Technical Report

Reference 54

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Llama 3.2,

Reference 55

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models The Llama 3 Herd of Models

Reference 56

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Mapping global dynamics of benchmark creation and saturation in artificial intelligence,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:52.093931Z

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices

Reference 58

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding,

Reference 59

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Blenderproc2: A procedural pipeline for photorealistic rendering,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:51.622054Z

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Chain-of- thought prompting elicits reasoning in large language models,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:51.403411Z

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Unresolved cited work

Reference 62

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Unresolved cited work

Reference 63

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Unresolved cited work

Reference 64

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Observation f7d78d32-2fb2-447f-abca-f06843419468 · outbound

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Unresolved cited work

Reference 65

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models The number of pieces in the image is:

Reference 66

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Unresolved cited work

Reference 67

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Unresolved cited work

Reference 68

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Unresolved cited work

Reference 69

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Observation 043c2210-f07d-4cd5-ab43-a5debabaae8e · outbound

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Unresolved cited work

Reference 70

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Observation 203d8c50-9dde-4cfd-95cc-7b73e850ef25 · outbound

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models base_pile_config

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:35:48.957617Z

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Observation c8be7de6-96ee-4304-bf8a-6d2b47a710e5 · outbound

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:35:48.729607Z

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Observation dfdb1b10-7a28-4e95-a9da-2ef007d99477 · outbound

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:35:48.560472Z

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Observation b00bbe77-9d72-47ef-9626-39c7706fe46f · outbound

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BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Unresolved cited work

Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:35:48.380569Z

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Observation 2ecdd454-bcc9-4298-aa46-4a2174b5754a · outbound

This paper cites an unresolved cited work.

BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models Unresolved cited work

Reference 75

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Coverage vector

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Outbound references

Pith citing papers