Gestalt: Large Multimodal Interplay Model

arXiv:2610.00576 · cs.CV · Submitted 2026-09-30 · Read on arXiv

cs.CV

Submitted: 2026-09-30

Updated: 2026-10-08

Code: https://github.com/GeWu-Lab/Gestalt

Project page: https://gewu-lab.github.io/Gestalt

Terminology

• Benchmark — 24× in this paper · explained in 2 episodes
• Alignment — 13× in this paper · explained in 1 episode
• Fine-Tuning — 9× in this paper · explained in 5 episodes
• Adaptation — 6× in this paper · explained in 1 episode
• Multimodal Integration — 5× in this paper · explained in 2 episodes
• Instruction Tuning — 4× in this paper · explained in 2 episodes
• Redundancy — 4× in this paper · explained in 2 episodes
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Multimodal Learning — 3× in this paper · explained in 2 episodes
• Scaling Laws — 3× in this paper · explained in 4 episodes
• Tokenization — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes

Sources

• Qwen2.5-VL Technical Report
• Kimi K3: Open Frontier Intelligence
• LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training
• FineVision: Open Data Is All You Need
• InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
• ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image Generation
• BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset
• Emerging Properties in Unified Multimodal Pretraining
• Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling
• UniGenBench++: A Unified Semantic Evaluation Benchmark for Text-to-Image Generation
• TIIF-Bench: How Does Your T2I Model Follow Your Instructions?
• Lumina-DiMOO: An Omni Diffusion Large Language Model for Multi-Modal Generation and Understanding
• Omni-Diffusion: Unified Multimodal Understanding and Generation with Masked Discrete Diffusion
• MIBench: Evaluating LMMs on Multimodal Interaction
• Core Knowledge Deficits in Multi-Modal Language Models
• Gated Multimodal Units for Information Fusion
• Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models
• Unified Multimodal Understanding and Generation Models: Advances, Challenges, and Opportunities
• Chameleon: Mixed-Modal Early-Fusion Foundation Models
• Emu3: Next-Token Prediction is All You Need

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models