Advances in Multimodal Large Language Models

The field of multimodal large language models (MLLMs) is rapidly advancing, with a focus on improving reasoning and planning capabilities. Recent research has highlighted the limitations of current MLLM benchmarks, which often rely on heuristic-based task groupings and lack clear cognitive targets. To address this, new frameworks and benchmarks have been proposed, such as those using structural equation modeling and cognitive science-inspired approaches. These developments aim to provide more interpretable and theoretically grounded evaluations of MLLM abilities.

Noteworthy papers include:

  • MARBLE, a challenging multimodal reasoning benchmark that scrutinizes MLLMs' ability to reason step-by-step through complex multimodal problems, and
  • MMReason, a new benchmark designed to precisely and comprehensively evaluate MLLM long-chain reasoning capability with diverse, open-ended, challenging questions.

Sources

Aligning MLLM Benchmark With Human Preferences via Structural Equation Modeling

FinEval-KR: A Financial Domain Evaluation Framework for Large Language Models' Knowledge and Reasoning

MARBLE: A Hard Benchmark for Multimodal Spatial Reasoning and Planning

Are Large Language Models Capable of Deep Relational Reasoning? Insights from DeepSeek-R1 and Benchmark Comparisons

MMReason: An Open-Ended Multi-Modal Multi-Step Reasoning Benchmark for MLLMs Toward AGI

A Practical Guide to Interpretable Role-Based Clustering in Multi-Layer Financial Networks

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation

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