Multi-agent debate has proven effective in improving large language models quality for
reasoning and factuality tasks. While various role-playing strategies in multi-agent debates …

Existing Large Multimodal Models (LMMs) struggle with mathematical geometric reasoning
due to a lack of high-quality image-text paired data. Current geometric data generation …

Sketching serves as a versatile tool for externalizing ideas, enabling rapid exploration and
visual communication that spans various disciplines. While artificial systems have driven …

As the field of Multimodal Large Language Models (MLLMs) continues to evolve, their
potential to revolutionize artificial intelligence is particularly promising, especially in …

While vision-language models (VLMs) have demonstrated remarkable performance across
various tasks combining textual and visual information, they continue to struggle with fine …

Compositional reasoning in Vision-Language Models (VLMs) remains challenging as these
models often struggle to relate objects, attributes, and spatial relationships. Recent methods …

Modern vision models have achieved remarkable success in benchmarks where local
features provide critical information about the target. There is now a growing interest in …

Mathematical reasoning, a core aspect of human cognition, is vital across many domains,
from educational problem-solving to scientific advancements. As artificial general …

With advances in foundational and vision-language models, and effective fine-tuning
techniques, a large number of both general and special-purpose models have been …

Chain-of-Thought (CoT) reasoning is widely used to enhance the mathematical reasoning
capabilities of large language models (LLMs). The introduction of process supervision for …