Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T11:27:33.494574Z
Paper Citation Record · LEDGER
As of 13 August 2026, this Paper Citation Record lists 79 of 79 outbound references and 6 inbound Pith citation observations for arXiv:2411.18203.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T11:27:33.494574Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T19:56:01.975427Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T22:36:16.802423Z
79 of 79 outbound references displayed
External citation measurements
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Observation 034a4a1f-25b6-421c-ac16-9da194944231 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Flamingo: a visual language model for few-shot learning
Reference 1
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Observation 8d605576-d546-41e7-a892-58bb37b3040a · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models
Reference 2
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Observation 1f148c67-6662-4cdc-bf32-6d7fac83682a · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
Reference 3
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Observation 2167781a-0df1-4004-8d67-5b91be868b77 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity
Reference 4
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Observation d8221068-bef4-43bd-b7a2-42b5c0ed0d0f · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning ShareGPT4V: Improving Large Multi-Modal Models with Better Captions
Reference 5
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Observation 9d0e1993-dd2c-499c-a8e4-8e3114a8f8e8 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Are We on the Right Way for Evaluating Large Vision-Language Models?
Reference 6
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Observation be18d887-8108-4785-aca9-1caeec776a58 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks
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Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning CAST: Cross-modal Alignment Similarity Test for Vision Language Models
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Observation 979b5fa9-23a3-4333-ae07-42be3cf973d2 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Gemini-1.5-pro, 2024
Reference 9
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Observation 95a58b8a-9adb-4645-9eb8-49aa8962c5b5 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model
Reference 10
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Observation 0b954e3b-be66-4f36-a2ed-e11590f3c993 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning PaLM-E: An Embodied Multimodal Language Model
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Observation 6be69ab7-8e1b-4659-ac89-bf5ecb436ff1 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Chatglm: A family of large language mod- els from glm-130b to glm-4 all tools, 2024
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Observation c9079af9-cc7f-4116-917a-db2bbdc4aea0 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning
Reference 13
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Observation 3731d655-9dc0-467b-a652-6bc6913c5e71 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Self-Correction is More than Refinement: A Learning Framework for Visual and Language Reasoning Tasks
Reference 14
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Observation 5a8f6af5-373d-4af3-b4b6-b2e5fd8a749e · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning V-STaR: Training Verifiers for Self-Taught Reasoners
Reference 15
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Observation 6860837d-de31-4a5d-947d-ed08abc4ad61 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning St-p3: End-to-end vision-based au- tonomous driving via spatial-temporal feature learning
Reference 16
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Observation 954f6cb4-c8c7-4c1f-9733-fc183994a71e · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Opera: Alleviating hallucination in multi- modal large language models via over-trust penalty and retrospection-allocation
Reference 17
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Observation d5c2c2d9-f8ab-423f-b1b2-7c8658dd7850 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning GPT-4o System Card
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Observation efff575c-602d-41a1-8d0a-52067462f1e5 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Vad: Vectorized scene representa- tion for efficient autonomous driving
Reference 19
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Observation 31478b72-4aa0-4807-8de0-ab17a6049e56 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning VIMA: General Robot Manipulation with Multimodal Prompts
Reference 20
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Observation 8b38f95b-ee23-47cf-ba57-cae2dcaf62b8 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Large language models are zero-shot reasoners
Reference 21
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Observation 6bb5eb8b-6966-45ff-9ac3-634d3d9238f2 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning In-context Reinforcement Learning with Algorithm Distillation
Reference 22
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Observation 1e646ab3-d26a-4239-9865-26d51064c042 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
Reference 23
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Observation a01a592e-44bc-406f-a3c8-a5ab89e09ed0 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Silkie: Preference Distillation for Large Visual Language Models
Reference 24
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Observation 9f764ecb-cd88-4e22-9e05-d9beeb962bcb · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Evaluating Object Hallucination in Large Vision-Language Models
Reference 25
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Observation 52f9c951-d830-489f-9ce0-fb42c12adb18 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Let's Verify Step by Step
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Observation 048b293e-b7d8-4666-bcab-cd50cb634d64 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Mitigating hallucination in large multi-modal models via robust instruction tuning
Reference 27
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Observation 28aa8fb3-447d-4c19-9214-0325a9f0d193 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Improved baselines with visual instruction tuning
Reference 28
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Observation 8bf43c82-feae-46e3-949b-2f7e1a3902c6 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Visual instruction tuning
Reference 29
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Observation 422cb8f1-5374-49ab-8131-14a6d80f8491 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Mmbench: Is your multi-modal model an all-around player? In European Conference on Computer Vision, pages 216–233
Reference 30
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Observation a0b62837-e951-479b-bf63-bf0766baa20d · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning DeepSeek-VL: Towards Real-World Vision-Language Understanding
Reference 31
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Observation 06df21f4-afa2-4141-b501-5b7627c19648 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Learn to explain: Multimodal reasoning via thought chains for science question answering
Reference 32
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Observation 87ede84d-8f10-41bb-a46d-ac44467b0c45 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Mathvista: Evaluating mathemat- ical reasoning of foundation models in visual contexts
Reference 33
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Observation 6495c531-cf02-4d26-9b78-8d16ed8d249f · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Self-refine: It- erative refinement with self-feedback
Reference 34
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Observation 8d63ecfa-947c-4889-b4fe-80eec2bde86e · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning LLM Critics Help Catch LLM Bugs
Reference 35
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Observation c2bc84c7-b20a-4dd5-bcf5-f641e5a9f4e6 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Llama-3.2-11b-vision, 2024
Reference 36
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Observation e3d8be13-5f55-4c14-9033-84d6af8a8726 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Rule Based Rewards for Language Model Safety
Reference 37
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Observation d1ecdada-f367-49fc-b211-fecd9b6778f8 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Gpt-4v(ision) system card, 2023
Reference 38
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Observation ba6bca92-4df6-40ab-8c03-ec2b0de626d0 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Hello GPT-4o
Reference 39
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Observation 4ab1b5ae-9ce7-4de7-bf41-ed108ee4c08f · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Gpt-4o mini: advancing cost-efficient intelligence,
Reference 40
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Observation 58cc6361-bb9f-4fd6-a1d4-e57357a9388f · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning REFINER: Reasoning Feedback on Intermediate Representations
Reference 41
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Observation d29ed5ec-50d7-43f7-9572-3f24f6753b9d · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Direct preference optimization: Your language model is secretly a reward model
Reference 42
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Observation 78046100-131f-421f-9de1-8f0bbfb6e49c · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Deepspeed: System optimizations enable train- ing deep learning models with over 100 billion parame- ters
Reference 43
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Observation e0cf320e-7c19-4f4f-bc7b-83ce7c498fbf · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Learning to summarize with human feed- back
Reference 44
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Observation 3c94c04f-de9e-4a45-b91e-7bfca6409f05 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Aligning Large Multimodal Models with Factually Augmented RLHF
Reference 45
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Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Policy gradient methods for reinforcement learning with function approximation
Reference 46
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Observation f02a630f-3b4c-44b0-83de-099917c43c33 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Gemini: A Family of Highly Capable Multimodal Models
Reference 47
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Observation 2bbdabe7-3460-4516-b7d9-ab3ac4b6feea · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning LLMs cannot find reasoning errors, but can correct them given the error location
Reference 48
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Observation db7a87f6-8cf6-4d55-818b-9e8a057a47ef · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
Reference 49
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Observation 17cfad8e-0140-4bac-9f76-730c358064b0 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement
Reference 50
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Observation 5b9e669a-383e-4016-9fcc-099afb58f0ed · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Chain-of-thought prompting elicits reasoning in large lan- guage models
Reference 51
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Observation 917ed366-babe-4e96-b341-d5776162cc89 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
Reference 52
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Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Grok-1.5 vision preview, 2024
Reference 53
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Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)
Reference 54
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Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Tree of thoughts: Deliberate problem solving with large language models
Reference 55
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Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Learning From Correctness Without Prompting Makes LLM Efficient Reasoner
Reference 56
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Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning MiniCPM-V: A GPT-4V Level MLLM on Your Phone
Reference 57
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Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Mmt-bench: A comprehensive multimodal benchmark for evaluating large vision-language models towards multitask agi, 2024
Reference 58
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Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning TextGrad: Automatic "Differentiation" via Text
Reference 59
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Observation bde391a1-1fb1-4797-91be-ec23c524300c · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B
Reference 60
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Observation 4c064ee6-a811-4e7e-87f3-006990c4a0b1 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning
Reference 61
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Observation a8d72bef-65e9-4fce-9748-e725d2b3ee7a · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output
Reference 62
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Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?
Reference 63
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Observation 191d3506-af80-45c3-a24a-ad877de06601 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language Model
Reference 64
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Observation 763db4c7-4943-45db-9dd3-d365522f91a6 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Automatic Chain of Thought Prompting in Large Language Models
Reference 65
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Observation 2cec8827-945a-465a-b57d-ed01b74ed74b · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Aligning Modalities in Vision Large Language Models via Preference Fine-tuning
Reference 66
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Observation 14067b64-e3d4-4320-8f78-261170d301f2 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Calibrated Self-Rewarding Vision Language Models
Reference 67
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Observation 221d1c84-03bf-489e-a144-4c5434667161 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
Reference 68
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Observation 6a56b08c-c0b5-4c9d-9001-1f9b8121916e · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning VGA: Vision GUI assis- tant - minimizing hallucinations through image-centric fine- tuning
Reference 69
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Reference 72
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Reference 73
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Observation 48cc2e8b-a86b-46e1-a840-d6ab3448261e · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning For preference-aligned fine-tuning, we utilize Direct Preference Optimization (DPO) on 29,012 samples from the critique- VQA dataset
Reference 74
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Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning In this section, we will list out the hyperparameters we choose for evaluation
Reference 75
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Observation 470ff0d4-673e-42dc-94da-807a7963785c · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning Unresolved cited work
Reference 76
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Observation 1e61a02c-5122-4f37-a31c-9e03ba72aab7 · outbound
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning You can find them in Figure 6, Figure 7 and Figure 8
Reference 77
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