While Large Language Models (LLMs) have demonstrated their proficiency in
complex reasoning tasks, their performance in dynamic, interactive, and
competitive scenarios - such as business strategy and stock market analysis -
remains underexplored. To bridge this gap, we formally explore the dynamic
reasoning capabilities of LLMs for decision-making in rapidly evolving
environments. We introduce two game theory-based pilot challenges that mirror
the complexities of real-world dynamic decision-making. These challenges are
well-defined, enabling clear, controllable, and precise evaluation of LLMs'
dynamic reasoning abilities. Through extensive experiments, we find that
existing reasoning methods tend to falter in dynamic settings that require
k-level thinking - a key concept not tackled by previous works. To address
this, we propose a novel reasoning approach for LLMs, named "K-Level
Reasoning". This approach adopts the perspective of rivals to recursively
employ k-level thinking based on available historical information, which
significantly improves the prediction accuracy of rivals' subsequent moves and
informs more strategic decision-making. This research not only sets a robust
quantitative benchmark for the assessment of dynamic reasoning but also
markedly enhances the proficiency of LLMs in dynamic contexts.

通过提出一种名为 “K 级推理” 的新型推理方法，我们在大型语言模型中加强了动态情境中的决策制定能力，并为动态推理能力的评估设定了一个坚实的定量基准。

利用大型语言模型进行 K 级推理

K-Level Reasoning with Large Language Models

Event-Level Video Question Answering (EVQA) requires complex reasoning across
video events to obtain the visual information needed to provide optimal
answers. However, despite significant progress in model performance, few
studies have focused on using the explicit semantic connections between the
question and visual information especially at the event level. There is need
for using such semantic connections to facilitate complex reasoning across
video frames. Therefore, we propose a semantic-aware dynamic
retrospective-prospective reasoning approach for video-based question
answering. Specifically, we explicitly use the Semantic Role Labeling (SRL)
structure of the question in the dynamic reasoning process where we decide to
move to the next frame based on which part of the SRL structure (agent, verb,
patient, etc.) of the question is being focused on. We conduct experiments on a
benchmark EVQA dataset - TrafficQA. Results show that our proposed approach
achieves superior performance compared to previous state-of-the-art models. Our
code will be made publicly available for research use.

本文提出了一种语义感知的动态回顾 - 前瞻推理方法，用于视频问答，通过使用问题的语义角色标注（SRL）结构，实现了基于语义连接的复杂推理，在 TrafficQA 数据集上实验结果显示，相较于之前的最先进模型，该方法实现了卓越的性能提升。