VIVAS: Vitalizing Visual Perception in VLM Pre-training via Vision-language Unified Autoregressive Supervision

arXiv:2609.27948 · cs.CV · Submitted 2026-08-25 · Read on arXiv

cs.CV

Submitted: 2026-08-25

Updated: 2026-08-25

Terminology

• Alignment — 21× in this paper · explained in 1 episode
• Benchmark — 20× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Grounding — 7× in this paper · explained in 3 episodes
• Large Language Model — 7× in this paper
• Ablation study — 4× in this paper · explained in 1 episode
• Benchmarking — 4× in this paper · explained in 3 episodes
• Contrastive Learning — 4× in this paper · explained in 5 episodes
• Instruction Tuning — 4× in this paper · explained in 2 episodes
• Quantization — 4× in this paper · explained in 12 episodes
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Visual Grounding — 4× in this paper · explained in 2 episodes
• Abstraction — 3× in this paper · explained in 1 episode
• Hallucination — 3× in this paper · explained in 3 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Vision-language models — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Distillation — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Multimodal Reasoning — 2× in this paper · explained in 5 episodes
• Reinforcement Learning (RL) — 2× in this paper · explained in 25 episodes
• Scaling Laws — 2× in this paper · explained in 4 episodes
• Supervised Fine-Tuning — 2× in this paper
• Supervised Fine-Tuning (SFT) — 2× in this paper · explained in 8 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Chain-of-Thought (CoT) — 1× in this paper · explained in 4 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Principal Component Analysis (PCA) — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Self-Distillation — 1× in this paper · explained in 7 episodes
• Semantic Gap — 1× in this paper · explained in 2 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Verifiable Rewards — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• Qwen3-VL Technical Report
• Qwen2.5-VL Technical Report
• Are We on the Right Way for Evaluating Large Vision-Language Models?
• Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
• GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial Tasks
• DeepSeek-V3 Technical Report
• OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning
• BLINK: Multimodal Large Language Models Can See but Not Perceive
• HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models
• Seed1.5-VL Technical Report
• TACO: Think-Answer Consistency for Optimized Long-Chain Reasoning and Efficient Data Learning via Reinforcement Learning in LVLMs
• CATCH: Complementary Adaptive Token-level Contrastive Decoding to Mitigate Hallucinations in LVLMs
• A Diagram Is Worth A Dozen Images
• SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
• Spatial Forcing: Implicit Spatial Representation Alignment for Vision-language-action Model
• Unleashing the Intrinsic Visual Representation Capability of Multimodal Large Language Models
• ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use
• VISTA: Enhancing Vision-Text Alignment in MLLMs via Cross-Modal Mutual Information Maximization
• Visual Instruction Tuning
• MMBench: Is Your Multi-modal Model an All-around Player?

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