Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models | Paper Citation Record

Observation 631e9839-7de6-4999-af51-42243f110e65 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

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Observation 6ddc1952-1dee-4862-9890-42268bd94048 · outbound

This paper cites Nocaps: Novel object captioning at scale.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Nocaps: Novel object captioning at scale

Reference 2

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Observation 0db50ae8-30bd-466e-8375-101706f9fe39 · outbound

This paper cites Knowledge-augmented large language models for personalized contextual query suggestion.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Knowledge-augmented large language models for personalized contextual query suggestion

Reference 3

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This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 4

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This paper cites Vizwiz: nearly real-time answers to visual questions.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Vizwiz: nearly real-time answers to visual questions

Reference 5

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Observation 33d679d2-e71a-408f-90ba-4a047f57d07b · outbound

This paper cites Social attention and real-world scenes: The roles of action, competition and social content.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Social attention and real-world scenes: The roles of action, competition and social content

Reference 6

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This paper cites Scene text visual question answering.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Scene text visual question answering

Reference 7

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Observation 65bc4c41-99ff-43cd-913e-d3fdb17c219e · outbound

This paper cites Language models are few-shot learners.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Language models are few-shot learners

Reference 8

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Observation 96c0aac1-fee7-415d-a2cc-9be9d422b595 · outbound

This paper cites Scaling Synthetic Data Creation with 1,000,000,000 Personas.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Scaling Synthetic Data Creation with 1,000,000,000 Personas

Reference 9

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Observation c9ed0198-6485-442a-a353-993e3df005d6 · outbound

This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Microsoft COCO Captions: Data Collection and Evaluation Server

Reference 10

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Observation 4c81064e-18ec-427c-9a3a-b3032c52a262 · outbound

This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90\ See https://vicuna.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Vicuna: An open-source chatbot impressing gpt-4 with 90\ See https://vicuna

Reference 11

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This paper cites PSSAT: A perturbed semantic structure awareness transferring method for perturbation-robust slot filling.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models PSSAT: A perturbed semantic structure awareness transferring method for perturbation-robust slot filling

Reference 12

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Observation f9b300c4-d038-414e-817e-b865dc6e5c26 · outbound

This paper cites Bridging the kb-text gap: Leveraging structured knowledge-aware pre-training for KBQA.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Bridging the kb-text gap: Leveraging structured knowledge-aware pre-training for KBQA

Reference 13

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Observation e5cb79ef-19c3-4208-abd5-f210fa180614 · outbound

This paper cites Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models

Reference 14

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Observation 7c1a6041-2196-46a5-bb0b-3d73670fa235 · outbound

This paper cites Toward General Instruction-Following Alignment for Retrieval-Augmented Generation.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Toward General Instruction-Following Alignment for Retrieval-Augmented Generation

Reference 15

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This paper cites How abilities in large language models are affected by supervised fine-tuning data composition.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models How abilities in large language models are affected by supervised fine-tuning data composition

Reference 16

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This paper cites Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation

Reference 17

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Observation ce3fbb98-f0f9-452c-a837-943f87a1300d · outbound

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

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 18

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Observation 50d0e50e-0f4b-4613-b756-aafce5b4297d · outbound

This paper cites Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

Reference 19

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Observation 46b36204-5aa2-4e16-b841-9c99115a075d · outbound

This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 20

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This paper cites ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving

Reference 21

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

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Making the v in vqa matter: Elevating the role of image understanding in visual question answering

Reference 22

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Observation ccb7a151-396f-45c7-a3b9-89981a09fdaf · outbound

This paper cites Retrieval augmented language model pre-training.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Retrieval augmented language model pre-training

Reference 23

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Observation e0a6ea6b-b7b0-4799-8294-e37b6f24dee3 · outbound

This paper cites Measuring massive multitask language understanding, 2021.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Measuring massive multitask language understanding, 2021

Reference 24

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This paper cites CogAgent: A Visual Language Model for GUI Agents.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models CogAgent: A Visual Language Model for GUI Agents

Reference 25

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This paper cites MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Reference 26

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This paper cites Gqa: A new dataset for real-world visual reasoning and compositional question answering.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Gqa: A new dataset for real-world visual reasoning and compositional question answering

Reference 27

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This paper cites Privacy through pseudonymity in user-adaptive systems.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Privacy through pseudonymity in user-adaptive systems

Reference 28

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Observation 907f6be3-6733-44c3-aac1-d80a8ec0b777 · outbound

This paper cites The personalization of conversational agents in health care: systematic review.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models The personalization of conversational agents in health care: systematic review

Reference 29

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Observation 73e3cb67-49dc-4fd9-8440-7b50e96a670d · outbound

This paper cites InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models

Reference 30

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This paper cites u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \

Reference 31

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

Reference 32

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Llava-med: Training a large language-and-vision assistant for biomedicine in one day

Reference 33

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Llava-next: Improved reasoning, ocr, and world knowledge, 2024 a

Reference 34

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Visual instruction tuning

Reference 35

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This paper cites Lost in the middle: How language models use long contexts.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Lost in the middle: How language models use long contexts

Reference 36

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models MMBench: Is Your Multi-modal Model an All-around Player?

Reference 37

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models DeepSeek-VL: Towards Real-World Vision-Language Understanding

Reference 38

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts

Reference 39

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This paper cites MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct

Reference 40

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Ok-vqa: A visual question answering benchmark requiring external knowledge

Reference 41

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Docvqa: A dataset for vqa on document images

Reference 42

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Image segmentation using deep learning: A survey

Reference 43

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Ocr-vqa: Visual question answering by reading text in images

Reference 44

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Hello gpt-4o, 2024

Reference 45

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Gpt-4v (ision) system card

Reference 46

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

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Training language models to follow instructions with human feedback

Reference 47

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Generative ai for customizable learning experiences

Reference 48

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?

Reference 49

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Making visual sense of oracle bones for you and me

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:58:45.115888Z

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Observation 9b92e2db-2933-46b8-8ab4-6842667cd714 · outbound

This paper cites Ai and personalization.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Ai and personalization

Reference 51

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Observation 889bbf71-2bea-4062-8c8e-153f4e5f6f13 · outbound

This paper cites Deep convolutional neural networks for image classification: A comprehensive review.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Deep convolutional neural networks for image classification: A comprehensive review

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:58:45.079181Z

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Observation e5f14e05-9e2a-4290-a2d9-b6779fea53c1 · outbound

This paper cites Machine translation using deep learning: An overview.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Machine translation using deep learning: An overview

Reference 53

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This paper cites CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery

Reference 54

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Social influence and group identity

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:58:45.020248Z

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Observation 43f2169a-d2fa-493c-b9b0-e5d90161c01e · outbound

This paper cites PandaGPT: One Model To Instruction-Follow Them All.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models PandaGPT: One Model To Instruction-Follow Them All

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.681381Z

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Observation 60750400-0c64-49f3-8f2e-8cbe2cf32ed8 · outbound

This paper cites Individuals and groups in social psychology.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Individuals and groups in social psychology

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:58:44.996095Z

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Observation 82638d57-f78c-46ab-8a9c-edcde57147ad · outbound

This paper cites Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.692882Z

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Observation 5793f5e4-6056-4073-bed9-6b3f68edb97e · outbound

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

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Gemini: A Family of Highly Capable Multimodal Models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.698637Z

Source-reported events for the cited work

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Observation 3ece82b2-58bc-4f17-a781-52d7e3f880ae · outbound

This paper cites LaMDA: Language Models for Dialog Applications.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models LaMDA: Language Models for Dialog Applications

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.704364Z

Source-reported events for the cited work

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Observation 32ac0533-7349-4d3c-bba2-3a8ace65b05a · outbound

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

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models LLaMA: Open and Efficient Foundation Language Models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.710580Z

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Observation dadbc503-a094-43af-b542-441b1a132c0b · outbound

This paper cites CogVLM: Visual Expert for Pretrained Language Models.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models CogVLM: Visual Expert for Pretrained Language Models

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.716199Z

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Observation c2718c4b-f368-43ac-ba93-365dc2bff340 · outbound

This paper cites General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.722047Z

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Observation 70b9f28f-a92c-4c73-9dc4-84dba104b9f6 · outbound

This paper cites Personalized Large Language Models.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Personalized Large Language Models

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.727204Z

Source-reported events for the cited work

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Observation ecf8f96d-90c7-4adc-a39b-fdaf917d5b6c · outbound

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Semantic parsing by large language models for intricate updating strategies of zero-shot dialogue state tracking

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.733135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a2840da-cc7c-4548-b54e-a0c574a08145 · outbound

This paper cites A personalized recommendation system with combinational algorithm for online learning.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models A personalized recommendation system with combinational algorithm for online learning

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:58:44.973624Z

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Observation c015c35e-0cf1-480d-b8a2-90a4c24c26d5 · outbound

This paper cites LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.747092Z

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Observation d1ed2b6b-03fb-456a-92ec-44953d7b5657 · outbound

This paper cites mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.753000Z

Source-reported events for the cited work

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Observation 3f4071c6-3d95-48d8-91fc-6fcd2733ea34 · outbound

This paper cites MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.757280Z

Source-reported events for the cited work

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Observation 1bbe3653-9142-45ff-968e-cde9f4e7368d · outbound

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.763019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76dfa791-c02a-46b1-9785-d0b68b50a7a8 · outbound

This paper cites MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.767347Z

Source-reported events for the cited work

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Observation 169bfd2a-f537-4779-8c1e-ffe4530ec6d2 · outbound

This paper cites MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.771382Z

Source-reported events for the cited work

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Observation e2489bfd-bdb8-4413-b276-694b944b2386 · outbound

This paper cites Scaling Relationship on Learning Mathematical Reasoning with Large Language Models.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Scaling Relationship on Learning Mathematical Reasoning with Large Language Models

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.776877Z

Source-reported events for the cited work

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Observation 8f28d167-8cd8-4186-99c4-4be10471f6a9 · outbound

This paper cites MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.782502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b18da9f8-9b06-43dc-ac0b-bf3b40577730 · outbound

This paper cites MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.789098Z

Source-reported events for the cited work

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Observation 0c97adbc-f256-419b-9e92-7da13b58b7e4 · outbound

This paper cites MAmmoTH2: Scaling Instructions from the Web.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models MAmmoTH2: Scaling Instructions from the Web

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.794129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02e21430-a93b-4918-aaf4-649a4ea95099 · outbound

This paper cites Evaluating Large Language Models at Evaluating Instruction Following.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Evaluating Large Language Models at Evaluating Instruction Following

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.799919Z

Source-reported events for the cited work

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Observation 55366806-e098-4c45-8ef3-860f8c55455f · outbound

This paper cites MM-LLMs: Recent Advances in MultiModal Large Language Models.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models MM-LLMs: Recent Advances in MultiModal Large Language Models

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.805154Z

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Unavailable: canonical work link unavailable.

Observation a230f91a-5c7f-41a5-9ffb-f731541a4d79 · outbound

This paper cites Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems? In European Conference on Computer Vision, pp.\ 169--186.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems? In European Conference on Computer Vision, pp.\ 169--186

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:58:44.943283Z

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Observation ad5fc014-eec6-4c74-b725-cf8124b49acf · outbound

This paper cites A Survey of Large Language Models.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models A Survey of Large Language Models

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.814376Z

Source-reported events for the cited work

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Observation f35c8cb9-0359-463d-9ea6-edb9bd87a4cd · outbound

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Object detection with deep learning: A review

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.819656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation befa72f8-d176-40f0-a75f-65648ab78507 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.825922Z

Source-reported events for the cited work

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Observation 89bf8cac-88b6-427d-b616-668b65262bde · outbound

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Instruction-Following Evaluation for Large Language Models

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.830736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ba37466-42e2-47e4-9582-dbb7d810b0e6 · outbound

This paper cites Math-PUMA: Progressive Upward Multimodal Alignment to Enhance Mathematical Reasoning.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Math-PUMA: Progressive Upward Multimodal Alignment to Enhance Mathematical Reasoning

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.836724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97f20e49-4ca3-4fab-852a-f720c084889b · outbound

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models HYDRA: Model Factorization Framework for Black-Box LLM Personalization

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.843207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 175a73d8-2690-4578-b94c-0eaceb6d8e94 · outbound

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models write newline

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.848709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb39c316-9209-42af-a770-8112aad90dc6 · outbound

This paper cites @esa (Ref.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models @esa (Ref

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.854735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 800a1a3e-3817-41fb-9ec5-ebd979d6e730 · outbound

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Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Unresolved cited work

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.861637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f18b5d6a-8c82-4beb-bb2b-35aaa5952579 · outbound

This paper cites an unresolved cited work.

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models Unresolved cited work

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-11T13:58:43.866369Z

Source-reported events for the cited work

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