Improving Large Vision and Language Models by Learning from a Panel of Peers | Paper Citation Record

Observation d1f574b3-4004-49c8-8e3c-55cab892c658 · outbound

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

Improving Large Vision and Language Models by Learning from a Panel of Peers Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

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Observation 4c935171-7f80-46b8-8f73-3b7aace4c50b · outbound

This paper cites GPT-4 Technical Report.

Improving Large Vision and Language Models by Learning from a Panel of Peers GPT-4 Technical Report

Reference 2

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This paper cites The Llama 3 Herd of Models.

Improving Large Vision and Language Models by Learning from a Panel of Peers The Llama 3 Herd of Models

Reference 3

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This paper cites Theoretical guarantees on the best-of-n alignment policy.

Improving Large Vision and Language Models by Learning from a Panel of Peers Theoretical guarantees on the best-of-n alignment policy

Reference 4

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Observation 320ed74c-f5d9-420b-9c91-687bf8a4bf98 · outbound

This paper cites PaliGemma: A versatile 3B VLM for transfer.

Improving Large Vision and Language Models by Learning from a Panel of Peers PaliGemma: A versatile 3B VLM for transfer

Reference 5

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Observation 06e2aed1-d8a9-4e36-ab9f-129346445690 · outbound

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

Improving Large Vision and Language Models by Learning from a Panel of Peers Are We on the Right Way for Evaluating Large Vision-Language Models?

Reference 6

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Observation f1af862d-6277-4e88-9be9-a3154e63f00d · outbound

This paper cites Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models.

Improving Large Vision and Language Models by Learning from a Panel of Peers Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

Reference 7

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Observation 28d8a27c-bb4b-46e4-aaa9-8f61d7bf5d73 · outbound

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

Improving Large Vision and Language Models by Learning from a Panel of Peers How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Reference 8

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Observation d9b9f837-3e73-4667-bc9c-7e7ea26dece4 · outbound

This paper cites Gonzalez, Ion Stoica, and Eric P.

Improving Large Vision and Language Models by Learning from a Panel of Peers Gonzalez, Ion Stoica, and Eric P

Reference 9

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Observation cf6b9be5-c95d-4ac1-9d4a-fa4e70c5cf4f · outbound

This paper cites Self-Improving Robust Preference Optimization.

Improving Large Vision and Language Models by Learning from a Panel of Peers Self-Improving Robust Preference Optimization

Reference 10

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Observation 7849c7cf-8ac5-4f82-b7c4-a15dd2047b5f · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Improving Large Vision and Language Models by Learning from a Panel of Peers Training Verifiers to Solve Math Word Problems

Reference 11

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Observation 25de84a8-6ce4-4abb-94c1-6dcbf63da87a · outbound

This paper cites Reward model ensembles help mitigate overop- timization.

Improving Large Vision and Language Models by Learning from a Panel of Peers Reward model ensembles help mitigate overop- timization

Reference 12

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Observation f2b6f9bb-88c2-47ae-98a6-2035402afea5 · outbound

This paper cites Nvlm: Open frontier-class multimodal llms.

Improving Large Vision and Language Models by Learning from a Panel of Peers Nvlm: Open frontier-class multimodal llms

Reference 13

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Observation 0103621a-185e-4f2d-b475-f7f8af9f2323 · outbound

This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness.

Improving Large Vision and Language Models by Learning from a Panel of Peers Flashattention: Fast and memory-efficient exact attention with io-awareness

Reference 14

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Observation 90da2d0b-414b-43cc-878b-39ed1d204b76 · outbound

This paper cites Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models.

Improving Large Vision and Language Models by Learning from a Panel of Peers Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models

Reference 15

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Observation f011d6d3-4fcb-48eb-8931-1cdf418b6bb7 · outbound

This paper cites Enhancing Large Vision Language Models with Self-Training on Image Comprehension.

Improving Large Vision and Language Models by Learning from a Panel of Peers Enhancing Large Vision Language Models with Self-Training on Image Comprehension

Reference 16

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Observation 66190a49-3bc1-4536-b81d-6a3e48288695 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Improving Large Vision and Language Models by Learning from a Panel of Peers An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 17

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Observation 42bc6ae6-ce22-4d6e-9970-e4b71f5ef03c · outbound

This paper cites VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models.

Improving Large Vision and Language Models by Learning from a Panel of Peers VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models

Reference 18

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Observation b32c752a-098d-4cc0-a4ed-b31bd5e24d6c · outbound

This paper cites VILA$^2$: VILA Augmented VILA.

Improving Large Vision and Language Models by Learning from a Panel of Peers VILA$^2$: VILA Augmented VILA

Reference 19

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Observation 02edb8e5-c7e7-422a-8a61-a909e22db5cd · outbound

This paper cites HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models.

Improving Large Vision and Language Models by Learning from a Panel of Peers HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models

Reference 20

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Observation e7d8a105-9d58-482a-a151-9c16b8c07adb · outbound

This paper cites CogVLM2: Visual Language Models for Image and Video Understanding.

Improving Large Vision and Language Models by Learning from a Panel of Peers CogVLM2: Visual Language Models for Image and Video Understanding

Reference 21

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Observation 34a2a7b2-1722-49e8-82e6-09870a987a03 · outbound

This paper cites Mistral 7B.

Improving Large Vision and Language Models by Learning from a Panel of Peers Mistral 7B

Reference 22

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Observation 9c3c92b1-5766-418b-8d6c-ac768568c5ef · outbound

This paper cites A diagram is worth a dozen images.

Improving Large Vision and Language Models by Learning from a Panel of Peers A diagram is worth a dozen images

Reference 23

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This paper cites What matters when building vision-language models?.

Improving Large Vision and Language Models by Learning from a Panel of Peers What matters when building vision-language models?

Reference 24

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Observation 3662a7e8-8af8-48b9-8b10-acc81c44ad5d · outbound

This paper cites Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation.

Improving Large Vision and Language Models by Learning from a Panel of Peers Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation

Reference 25

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

Improving Large Vision and Language Models by Learning from a Panel of Peers Seed-bench: Benchmark- ing multimodal large language models

Reference 26

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Observation dba96a56-05ab-4b2b-b413-3f53fbd6fc1d · outbound

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

Improving Large Vision and Language Models by Learning from a Panel of Peers LLaVA-OneVision: Easy Visual Task Transfer

Reference 27

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Observation 9d6d39c8-6cdd-40ae-8751-8b9193767366 · outbound

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

Improving Large Vision and Language Models by Learning from a Panel of Peers Blip: Bootstrapping language-image pre-training for unified vision- language understanding and generation

Reference 28

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Observation 82399be2-2ebe-412b-b0e7-3220ae1b890c · outbound

This paper cites Silkie: Preference Distillation for Large Visual Language Models.

Improving Large Vision and Language Models by Learning from a Panel of Peers Silkie: Preference Distillation for Large Visual Language Models

Reference 29

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Observation c33c94a5-eab8-4461-85ea-8c3b8f5d4bc2 · outbound

This paper cites Multi-modal Preference Alignment Remedies Degradation of Visual Instruction Tuning on Language Models.

Improving Large Vision and Language Models by Learning from a Panel of Peers Multi-modal Preference Alignment Remedies Degradation of Visual Instruction Tuning on Language Models

Reference 30

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Observation 30ec6ac9-7ef6-4634-bebb-c6747c8d80e3 · outbound

This paper cites Evaluating object hallucination in large vision- language models.

Improving Large Vision and Language Models by Learning from a Panel of Peers Evaluating object hallucination in large vision- language models

Reference 31

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This paper cites Improved baselines with visual instruction tuning.

Improving Large Vision and Language Models by Learning from a Panel of Peers Improved baselines with visual instruction tuning

Reference 32

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This paper cites Llava-next: Improved reason- ing, ocr, and world knowledge, 2024.

Improving Large Vision and Language Models by Learning from a Panel of Peers Llava-next: Improved reason- ing, ocr, and world knowledge, 2024

Reference 33

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This paper cites Visual instruction tuning.

Improving Large Vision and Language Models by Learning from a Panel of Peers Visual instruction tuning

Reference 34

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Improving Large Vision and Language Models by Learning from a Panel of Peers MMBench: Is Your Multi-modal Model an All-around Player?

Reference 35

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This paper cites OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models.

Improving Large Vision and Language Models by Learning from a Panel of Peers OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models

Reference 36

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This paper cites Learn to explain: Multimodal reasoning via thought chains for science question answering.

Improving Large Vision and Language Models by Learning from a Panel of Peers Learn to explain: Multimodal reasoning via thought chains for science question answering

Reference 37

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Observation e97ef82c-565c-4adc-a8db-04befdf0f731 · outbound

This paper cites Mathvista: Evaluating mathemat- ical reasoning of foundation models in visual contexts.

Improving Large Vision and Language Models by Learning from a Panel of Peers Mathvista: Evaluating mathemat- ical reasoning of foundation models in visual contexts

Reference 38

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Observation 13178546-7ca2-40d5-bc30-fc30b9c5f40e · outbound

This paper cites WildVision: Evaluating Vision-Language Models in the Wild with Human Preferences.

Improving Large Vision and Language Models by Learning from a Panel of Peers WildVision: Evaluating Vision-Language Models in the Wild with Human Preferences

Reference 39

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

Improving Large Vision and Language Models by Learning from a Panel of Peers Chartqa: A benchmark for question answering about charts with visual and logical reasoning

Reference 40

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Improving Large Vision and Language Models by Learning from a Panel of Peers SimPO: Simple Preference Optimization with a Reference-Free Reward

Reference 41

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Improving Large Vision and Language Models by Learning from a Panel of Peers Ocr-vqa: Visual question answering by reading text in images

Reference 42

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Observation 3f687fb6-424b-45d1-b9a3-966342625502 · outbound

This paper cites Cognitive perspectives on peer learning.

Improving Large Vision and Language Models by Learning from a Panel of Peers Cognitive perspectives on peer learning

Reference 43

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Improving Large Vision and Language Models by Learning from a Panel of Peers Gpt-4o system card, 2024

Reference 44

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Observation 597baf8e-52f1-47be-b60e-8c8c75ca7ca0 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Improving Large Vision and Language Models by Learning from a Panel of Peers DINOv2: Learning Robust Visual Features without Supervision

Reference 45

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This paper cites Im2text: Describing images using 1 million captioned photographs.

Improving Large Vision and Language Models by Learning from a Panel of Peers Im2text: Describing images using 1 million captioned photographs

Reference 46

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Observation 75b0c464-63e7-407c-97fe-aa939aa3cdd9 · outbound

This paper cites Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744,.

Improving Large Vision and Language Models by Learning from a Panel of Peers Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744,

Reference 47

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Observation 90f58df7-b687-4767-a59c-96d4a4f2d225 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Improving Large Vision and Language Models by Learning from a Panel of Peers Learning transferable visual models from natural language supervi- sion

Reference 48

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Observation b85784c7-5972-4bb2-be94-8da74896a9ff · outbound

This paper cites Direct prefer- ence optimization: Your language model is secretly a reward model.

Improving Large Vision and Language Models by Learning from a Panel of Peers Direct prefer- ence optimization: Your language model is secretly a reward model

Reference 49

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Observation c7b418e8-b013-4630-a495-c252d4887b88 · outbound

This paper cites LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs.

Improving Large Vision and Language Models by Learning from a Panel of Peers LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

Reference 50

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

Improving Large Vision and Language Models by Learning from a Panel of Peers Laion-5b: An open large-scale dataset for training next gen- eration image-text models

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:28.802576Z

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Observation a79e8db8-136f-46f2-9a94-1aeb06a99d5e · outbound

This paper cites 10 Towards vqa models that can read.

Improving Large Vision and Language Models by Learning from a Panel of Peers 10 Towards vqa models that can read

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:28.788522Z

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Observation 9ab19677-5ba0-4af5-8d41-9ecc26741426 · outbound

This paper cites Aligning Large Multimodal Models with Factually Augmented RLHF.

Improving Large Vision and Language Models by Learning from a Panel of Peers Aligning Large Multimodal Models with Factually Augmented RLHF

Reference 53

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Observation 67920b50-78f7-444c-84e6-d4870286071c · outbound

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

Improving Large Vision and Language Models by Learning from a Panel of Peers Gemini: A Family of Highly Capable Multimodal Models

Reference 54

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This paper cites Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs.

Improving Large Vision and Language Models by Learning from a Panel of Peers Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs

Reference 55

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Observation bd83f7df-7bb9-49f0-ba36-e49354fd136e · outbound

This paper cites Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models.

Improving Large Vision and Language Models by Learning from a Panel of Peers Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models

Reference 56

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Observation 97f328e9-a648-4b06-ba86-243fe4c8799c · outbound

This paper cites To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning.

Improving Large Vision and Language Models by Learning from a Panel of Peers To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning

Reference 57

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Observation ac0dddc9-b246-4a3f-adfb-aae0a8e5797f · outbound

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Improving Large Vision and Language Models by Learning from a Panel of Peers Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:27.618880Z

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Observation 13502761-0064-481f-a9ac-e087347d6afa · outbound

This paper cites Self-Taught Evaluators.

Improving Large Vision and Language Models by Learning from a Panel of Peers Self-Taught Evaluators

Reference 59

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Observation fe9fa07f-4118-41da-b60a-83beba9e54c7 · outbound

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Improving Large Vision and Language Models by Learning from a Panel of Peers Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement

Reference 60

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Observation abf26adc-4c7d-4de0-b06e-55ef50970bdd · outbound

This paper cites HelpSteer2: Open-source dataset for training top-performing reward models.

Improving Large Vision and Language Models by Learning from a Panel of Peers HelpSteer2: Open-source dataset for training top-performing reward models

Reference 61

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Observation 8cbc05ac-6824-4707-82ad-3e89cd1b6897 · outbound

This paper cites Helpsteer: Multi-attribute helpfulness dataset for steerlm.

Improving Large Vision and Language Models by Learning from a Panel of Peers Helpsteer: Multi-attribute helpfulness dataset for steerlm

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:28.774047Z

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Observation 940e9c55-ee50-4923-8c90-8447152a6c2a · outbound

This paper cites Pytorch image models.

Improving Large Vision and Language Models by Learning from a Panel of Peers Pytorch image models

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:28.759248Z

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Observation bcd8c5eb-a312-4d5d-9586-bbe4bcfb2a80 · outbound

This paper cites Gpt-4v (ision) is a human-aligned evaluator for text-to-3d generation.

Improving Large Vision and Language Models by Learning from a Panel of Peers Gpt-4v (ision) is a human-aligned evaluator for text-to-3d generation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:28.743120Z

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Observation 6cc5a664-6b16-4ddd-9aa2-fbd97503f7b5 · outbound

This paper cites Self-Play Preference Optimization for Language Model Alignment.

Improving Large Vision and Language Models by Learning from a Panel of Peers Self-Play Preference Optimization for Language Model Alignment

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:27.650830Z

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Observation c5fecd07-c731-45a4-85ac-1c5864a6c4b5 · outbound

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Improving Large Vision and Language Models by Learning from a Panel of Peers Grok-1.5 vision preview, 2024

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:28.728428Z

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Observation 066bc149-5148-4573-8a15-86059f0bc9a7 · outbound

This paper cites LLaVA-Critic: Learning to Evaluate Multimodal Models.

Improving Large Vision and Language Models by Learning from a Panel of Peers LLaVA-Critic: Learning to Evaluate Multimodal Models

Reference 67

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Observation baadd476-aa13-4853-a946-7bf01cdf7f50 · outbound

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Improving Large Vision and Language Models by Learning from a Panel of Peers The Perfect Blend: Redefining RLHF with Mixture of Judges

Reference 68

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Observation fb65bd6c-1ccb-4633-b0cf-d7c324804aec · outbound

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Improving Large Vision and Language Models by Learning from a Panel of Peers xgen-mm (blip-3): A family of open large multimodal models

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:27.668920Z

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Observation 83b74cf1-87ec-45fe-90d2-88c05ee447f7 · outbound

This paper cites Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional human feedback.

Improving Large Vision and Language Models by Learning from a Panel of Peers Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional human feedback

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:28.713157Z

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Observation f2ef592e-4043-490e-bf7f-5fa2444b0a74 · outbound

This paper cites Rlaif-v: Aligning mllms through open-source ai feedback for super gpt-4v trustworthiness.

Improving Large Vision and Language Models by Learning from a Panel of Peers Rlaif-v: Aligning mllms through open-source ai feedback for super gpt-4v trustworthiness

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:27.677331Z

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Unavailable: canonical work link unavailable.

Observation 42ec46ea-b362-462d-9aa1-88f10452c4f3 · outbound

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Improving Large Vision and Language Models by Learning from a Panel of Peers Mm-vet: Evaluating large multimodal models for integrated capabilities

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:28.698235Z

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Observation 8180939e-c5b8-47c3-9251-d259117020b4 · outbound

This paper cites Florence: A New Foundation Model for Computer Vision.

Improving Large Vision and Language Models by Learning from a Panel of Peers Florence: A New Foundation Model for Computer Vision

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:27.685759Z

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Observation 46ff7d88-df70-4e82-ba8b-5a1480e5df3a · outbound

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Improving Large Vision and Language Models by Learning from a Panel of Peers Self-Rewarding Language Models

Reference 74

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Observation c205a9dc-0742-4e1d-bede-ceea4f8e680e · outbound

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

Improving Large Vision and Language Models by Learning from a Panel of Peers Mmmu: A massive multi-discipline multi- modal understanding and reasoning benchmark for expert agi

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:28.683747Z

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Observation 03f86c78-4642-404b-a7cd-beae779d7761 · outbound

This paper cites Sigmoid loss for language image pre-training.

Improving Large Vision and Language Models by Learning from a Panel of Peers Sigmoid loss for language image pre-training

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:27.699358Z

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Unavailable: canonical work link unavailable.

Observation bdb9d7e0-39e1-48e3-879a-fda3f07ec52d · outbound

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Improving Large Vision and Language Models by Learning from a Panel of Peers SVIT: Scaling up Visual Instruction Tuning

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:27.703738Z

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Unavailable: canonical work link unavailable.

Observation b5d7a50d-8486-46ac-bcce-8160e888fd94 · outbound

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Improving Large Vision and Language Models by Learning from a Panel of Peers PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:27.708238Z

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Observation 8b2d4026-ad5a-41cd-bf59-bca8ef8d7882 · outbound

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Improving Large Vision and Language Models by Learning from a Panel of Peers Aligning Modalities in Vision Large Language Models via Preference Fine-tuning

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:27.712961Z

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Observation c5bb8057-b04a-42c5-bca4-1400387f4776 · outbound

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Improving Large Vision and Language Models by Learning from a Panel of Peers Calibrated Self-Rewarding Vision Language Models

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:27.717430Z

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Unavailable: canonical work link unavailable.

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Improving Large Vision and Language Models by Learning from a Panel of Peers Self-Supervised Visual Preference Alignment

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:27.721849Z

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Unavailable: canonical work link unavailable.

Observation b463cf89-7f55-479e-8d87-a6594ee11399 · outbound

This paper cites Fine-Tuning Language Models from Human Preferences.

Improving Large Vision and Language Models by Learning from a Panel of Peers Fine-Tuning Language Models from Human Preferences

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:27.726448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5740f99-3e2c-4ca3-bb51-b46176fd490a · outbound

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Improving Large Vision and Language Models by Learning from a Panel of Peers Unresolved cited work

Reference 83

Resolution: unresolved; raw_fallback, observed 2026-08-05T12:27:28.659736Z

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Observation acdac01d-a280-4cea-a386-e0382703e33a · outbound

This paper cites an unresolved cited work.

Improving Large Vision and Language Models by Learning from a Panel of Peers Unresolved cited work

Reference 84

Resolution: unresolved; raw_fallback, observed 2026-08-05T12:27:28.644530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d2caee6f-78d4-4a12-b36b-5cee87651a6c · outbound

This paper cites an unresolved cited work.

Improving Large Vision and Language Models by Learning from a Panel of Peers Unresolved cited work

Reference 85

Resolution: unresolved; raw_fallback, observed 2026-08-05T12:27:28.629984Z

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Observation 5c4fa842-9a44-4f81-b912-1ab012c1b2fc · outbound

This paper cites an unresolved cited work.

Improving Large Vision and Language Models by Learning from a Panel of Peers Unresolved cited work

Reference 86

Resolution: unresolved; raw_fallback, observed 2026-08-05T12:27:28.615834Z

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Observation cc9c01e3-a089-4554-bcb7-abdc2d80a1c7 · outbound

This paper cites • Rating Guidelines: Models receive detailed explanations for scoring each dimension.

Improving Large Vision and Language Models by Learning from a Panel of Peers • Rating Guidelines: Models receive detailed explanations for scoring each dimension

Reference 87

Resolution: malformed identifier; raw_fallback, observed 2026-08-05T12:27:28.601990Z

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Coverage vector

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Outbound references

Pith citing papers