The field of multimodal learning and reasoning is rapidly evolving, with a focus on developing models that can effectively integrate and process multiple forms of data, such as text, images, and audio. Recent research has emphasized the importance of improving the visual grounding and reasoning capabilities of multimodal large language models (MLLMs), enabling them to better understand and interpret visual information. Noteworthy papers in this area include CausalVLBench, which introduces a comprehensive benchmark for evaluating the visual causal reasoning abilities of MLLMs, and VGR, which proposes a novel reasoning framework that enhances the fine-grained visual perception capabilities of MLLMs. Additionally, papers like MANBench and Argus Inspection have highlighted the limitations of current MLLMs in terms of human-like reasoning and visual fine-grained perception, emphasizing the need for further research and development in these areas.
Advancements in Multimodal Learning and Reasoning
Sources
CausalVLBench: Benchmarking Visual Causal Reasoning in Large Vision-Language Models
Human-centered Interactive Learning via MLLMs for Text-to-Image Person Re-identification
MANBench: Is Your Multimodal Model Smarter than Human?
Stronger Language Models Produce More Human-Like Errors
VIBE: Can a VLM Read the Room?
Dynamic Double Space Tower
Bhatt Conjectures: On Necessary-But-Not-Sufficient Benchmark Tautology for Human Like Reasoning
VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories?
SceneGram: Conceptualizing and Describing Tangrams in Scene Context
Quizzard@INOVA Challenge 2025 -- Track A: Plug-and-Play Technique in Interleaved Multi-Image Model
Are Multimodal Large Language Models Pragmatically Competent Listeners in Simple Reference Resolution Tasks?
VGR: Visual Grounded Reasoning
Affogato: Learning Open-Vocabulary Affordance Grounding with Automated Data Generation at Scale
Balancing Preservation and Modification: A Region and Semantic Aware Metric for Instruction-Based Image Editing
Machine Mirages: Defining the Undefined
Unified Representation Space for 3D Visual Grounding
VisText-Mosquito: A Multimodal Dataset and Benchmark for AI-Based Mosquito Breeding Site Detection and Reasoning
SemIRNet: A Semantic Irony Recognition Network for Multimodal Sarcasm Detection
Argus Inspection: Do Multimodal Large Language Models Possess the Eye of Panoptes?
ViLLa: A Neuro-Symbolic approach for Animal Monitoring
ReSeDis: A Dataset for Referring-based Object Search across Large-Scale Image Collections
RE-IMAGINE: Symbolic Benchmark Synthesis for Reasoning Evaluation
Enhancing Hyperbole and Metaphor Detection with Their Bidirectional Dynamic Interaction and Emotion Knowledge
Minding the Politeness Gap in Cross-cultural Communication