SOTAVerified

Multimodal Reasoning

Reasoning over multimodal inputs.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 226–250 of 302 papers

TitleStatusHype
Shakti-VLMs: Scalable Vision-Language Models for Enterprise AI—0
VisualSphinx: Large-Scale Synthetic Vision Logic Puzzles for RL—0
Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learning—0
Advancing Conversational Diagnostic AI with Multimodal Reasoning—0
Skywork-VL Reward: An Effective Reward Model for Multimodal Understanding and Reasoning—0
SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and RAG-Enhanced Vision-Language Model—0
Sound2Sight: Generating Visual Dynamics from Sound and Context—0
SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware Reinforcement Learning—0
Stacked Latent Attention for Multimodal Reasoning—0
SToLa: Self-Adaptive Touch-Language Framework with Tactile Commonsense Reasoning in Open-Ended Scenarios—0
VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training—0
Text Is MASS: Modeling as Stochastic Embedding for Text-Video Retrieval—0
AdaToken-3D: Dynamic Spatial Gating for Efficient 3D Large Multimodal-Models Reasoning—0
The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs—0
VLMGuard-R1: Proactive Safety Alignment for VLMs via Reasoning-Driven Prompt Optimization—0
Diving into Self-Evolving Training for Multimodal Reasoning—0
DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents—0
DisinfoMeme: A Multimodal Dataset for Detecting Meme Intentionally Spreading Out Disinformation—0
Adapting Vision-Language Models for Evaluating World Models—0
Don't Look Only Once: Towards Multimodal Interactive Reasoning with Selective Visual Revisitation—0
Draw with Thought: Unleashing Multimodal Reasoning for Scientific Diagram Generation—0
DreamPRM: Domain-Reweighted Process Reward Model for Multimodal Reasoning—0
Deep Neural Networks for Visual Reasoning—0
DRIVINGVQA: Analyzing Visual Chain-of-Thought Reasoning of Vision Language Models in Real-World Scenarios with Driving Theory Tests—0
EfficientLLaVA:Generalizable Auto-Pruning for Large Vision-language Models—0
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All datasetsREBUSMATH-VAlgoPuzzleVQA

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GPT-4VAccuracy24—Unverified
2Gemini ProAccuracy13.2—Unverified
3LLaVa-1.5-13BAccuracy1.8—Unverified
4LLaVa-1.5-7BAccuracy1.5—Unverified
5BLIP2-FLAN-T5-XXLAccuracy0.9—Unverified
6QWENAccuracy0.9—Unverified
7CogVLMAccuracy0.9—Unverified
8InstructBLIPAccuracy0.6—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT4VAccuracy22.76—Unverified
2Gemini ProAccuracy17.66—Unverified
3Qwen-VL-MaxAccuracy15.59—Unverified
4InternLM-XComposer2-VLAccuracy14.54—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-4Acc30.3—Unverified