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New AIR system enhances MLLMs with adaptive code-based numerical reasoning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed AIR, an Adaptive Interleaved Reasoning system designed to enhance multimodal large language models (MLLMs). This system extends reinforcement learning to enable MLLMs to perform complex numerical computations by integrating code. AIR includes a novel data construction pipeline, data filtering strategies, and an adaptive tool-invocation mechanism with a group-constrained reward function. Experiments show a 6.1 percentage point improvement in performance on evaluation benchmarks after training, with a 9.9 pp increase in accuracy for interleaved reasoning tasks and over 95% success rate in tool usage. AI

IMPACT Enhances MLLMs' numerical reasoning capabilities, potentially improving their utility in complex computational tasks.

RANK_REASON The cluster describes a new research paper detailing a novel method for improving MLLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New AIR system enhances MLLMs with adaptive code-based numerical reasoning

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The cluster describes a new research paper detailing a novel method for improving MLLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yujie Zhong ·

    AIR: Adaptive Interleaved Reasoning with Code in MLLMs

    Following the paradigm shift initiated by OpenAI o3, interleaved reasoning with code to enhance multimodal large language models (MLLMs) has become a pivotal research frontier. The existing literature focuses primarily on tool-use within vision-perception tasks. However, such app…

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