lvlm

This paper introduces SpatialBlock-15k, a synthetic dataset for block-stacking problems, to enhance 3D spatial reasoning in large vision-language models, demonstrating improved performance and generalization to real-world tasks.

This paper introduces a controlled evaluation framework for interactive visual grounding in large vision-language models (LVLMs), showing that current LVLMs perform below human baselines and struggle with proactive question-driven grounding.

This paper introduces UniME-R1, an embedder-adviser framework for unified multimodal retrieval that generates Retrieval-Centric Chain-of-Thought (RC-CoT) conditioned on retrieval feedback, improving retrieval performance by learning from hard negatives.

This arXiv paper introduces CausalVLBench, a benchmark for evaluating visual causal reasoning in large vision-language models across three tasks: causal structure inference, intervention target prediction, and counterfactual prediction. It evaluates open-source LVLMs on three causal representation learning datasets, revealing strengths and weaknesses.

This paper introduces IllusionReasoning, a benchmark using real-world visual illusions to jointly evaluate the perception and reasoning capabilities of Large Vision Language Models (LVLMs), finding that current models' reasoning abilities are not as advanced as claimed.

This paper investigates seemingly contradictory findings on whether large vision-language models (LVLMs) can coordinate efficient referring expressions. The authors show that models can achieve efficiency when explicitly prompted, but fail to infer the need for efficiency from implicit prompts, revealing key differences between human and AI communication.

UniDoc-RL presents a reinforcement learning framework for Large Vision-Language Models that optimizes retrieval, reranking, and visual reasoning through hierarchical decision-making and dense multi-reward supervision, achieving up to 17.7% improvements over prior RL-based methods on visual RAG tasks.