Large Audio Language Models (LALMs) employing the Chain-of-Thought paradigm have demonstrated remarkable reasoning capabilities. Though different problems naturally require varying depths of reasoning, existing methods often determine whether to perform reasoning, lacking fine-grained mechanisms to adapt reasoning length to problem complexity. As a result, LALMs often adopt a one-size-fits-all reasoning strategy, leading to redundant overthinking for simple tasks and insufficient reasoning for complex ones. In this paper, we conduct an in-depth analysis of LALM reasoning behavior and argue that effective and efficient reasoning should be adaptively aligned with task difficulty. To this end, we propose a difficulty-adaptive reasoning method for LALMs. Specifically, we introduce a reward function that dynamically links reasoning length to the model’s perceived problem difficulty, encouraging shorter reasoning for easy tasks and longer reasoning for more complex ones. Extensive experiments on three datasets demonstrate that our method consistently improves performance while reducing average reasoning length by at least 50%, achieving higher efficiency without sacrificing accuracy.

Despite the impressive chain-of-thought(CoT) reasoning ability of large language models (LLMs), its underlying mechanisms remains unclear. In this paper, we explore the inner workings of LLM’s CoT ability via the lens of neurons in the feed-forward layers. We propose an efficient method to identify reasoning-critical neurons by analyzing their activation patterns under reasoning chains of varying quality. Based on it, we devise a rather simple intervention method that directly stimulates these reasoning-critical neurons, to guide the generation of high-quality reasoning chains. Extended experiments validate the effectiveness of our method and demonstrate the critical role these identified neurons play in CoT reasoning.

Automated Essay Assessment (AEA) aims to judge students’ writing proficiency in an automatic way. This paper presents a Chinese AEA system IFlyEssayAssess (IFlyEA), targeting on evaluating essays written by native Chinese students from primary and junior schools. IFlyEA provides multi-level and multi-dimension analytical modules for essay assessment. It has state-of-the-art grammar level analysis techniques, and also integrates components for rhetoric and discourse level analysis, which are important for evaluating native speakers’ writing ability, but still challenging and less studied in previous work. Based on the comprehensive analysis, IFlyEA provides application services for essay scoring, review generation, recommendation, and explainable analytical visualization. These services can benefit both teachers and students during the process of writing teaching and learning.