Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens | Paper Citation Record

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This paper cites GPT-4 Technical Report.

Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens GPT-4 Technical Report

Reference 1

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

Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Claude 3.5 sonnet

Reference 3

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Non-Determinism of "Deterministic" LLM Settings

Reference 4

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Qwen Technical Report

Reference 5

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Qwen2.5-VL Technical Report

Reference 6

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Eureka: Evaluating and Understanding Large Foundation Models

Reference 7

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This paper cites Language Models are Few-Shot Learners.

Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Language Models are Few-Shot Learners

Reference 8

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

Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Are We on the Right Way for Evaluating Large Vision-Language Models?

Reference 9

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This paper cites How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites.

Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Reference 10

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This paper cites NVLM: Open Frontier-Class Multimodal LLMs.

Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens NVLM: Open Frontier-Class Multimodal LLMs

Reference 11

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This paper cites MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models.

Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 12

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This paper cites GPT-4o System Card.

Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens GPT-4o System Card

Reference 13

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This paper cites Editing Models with Task Arithmetic.

Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Editing Models with Task Arithmetic

Reference 14

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This paper cites MANTIS: Interleaved Multi-Image Instruction Tuning.

Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens MANTIS: Interleaved Multi-Image Instruction Tuning

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This paper cites Kembhavi, M.

Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Kembhavi, M

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Unresolved cited work

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Unresolved cited work

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This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens LLaVA-OneVision: Easy Visual Task Transfer

Reference 19

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Unresolved cited work

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Unresolved cited work

Reference 21

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Unresolved cited work

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Unresolved cited work

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Unresolved cited work

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens POINTS: Improving Your Vision-language Model with Affordable Strategies

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Ovis: Structural Embedding Alignment for Multimodal Large Language Model

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Mathew, D

Reference 29

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Unearthing Skill-Level Insights for Understanding Trade-Offs of Foundation Models

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Gptv system card, 2024

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens DINOv2: Learning Robust Visual Features without Supervision

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Unresolved cited work

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Radford, J

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens LLaMA: Open and Efficient Foundation Language Models

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Bounding Box Stability against Feature Dropout Reflects Detector Generalization across Environments

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens MMBench: Is Your Multi-modal Model an All-around Player?

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Unresolved cited work

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Unresolved cited work

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Unveiling the Tapestry of Consistency in Large Vision-Language Models

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Benchmarking Multi-Image Understanding in Vision and Language Models: Perception, Knowledge, Reasoning, and Multi-Hop Reasoning

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Limitations

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Guidelines: • The answer NA means that the paper does not include theoretical results

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Guidelines: • The answer NA means that the paper does not include experiments

Reference 49

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Guidelines: • The answer NA means that paper does not include experiments requiring code

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Guidelines: • The answer NA means that the paper does not include experiments

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Guidelines: • The answer NA means that the paper does not include experiments

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Guidelines: • The answer NA means that the paper does not include experiments

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Guidelines: • The answer NA means that there is no societal impact of the work performed

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Guidelines: • The answer NA means that the paper poses no such risks

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Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens We will elaborate on it on the Appendix

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:03:25.856187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a65abba1-5764-4268-b798-b0d52d976d09 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not release new assets.

Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Guidelines: • The answer NA means that the paper does not release new assets

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:03:25.835417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f4685861-48c2-48e5-87dd-eb3e2c22db39 · outbound

This paper cites an unresolved cited work.

Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:03:25.780005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 634334d2-a7d1-4e3d-9c9c-61e4764e8d8b · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:03:25.665905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b6332e21-180d-40d5-93e2-f7353a5f43ba · outbound

This paper cites Answer: [NA] Justification: We use LLM for paper writing.

Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens Answer: [NA] Justification: We use LLM for paper writing

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:03:25.647440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers