LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios

Recent advances in the intrinsic reasoning capabilities of large language models (LLMs) have given rise to LLM-based agent systems that exhibit near-human performance on a variety of automated tasks. However, although these systems share similarities in terms of their use of LLMs, different reasoning frameworks of the agent system steer and organize the reasoning process in different ways. In this survey, we propose a systematic taxonomy that decomposes agentic reasoning frameworks and analyze how these frameworks dominate framework-level reasoning by comparing their applications across different scenarios. Specifically, we propose an unified formal language to further classify agentic reasoning systems into single-agent methods, tool-based methods, and multi-agent methods. After that, we provide a comprehensive review of their key application scenarios in scientific discovery, healthcare, software engineering, social simulation, and economics. We also analyze the characteristic features of each framework and summarize different evaluation strategies. Our survey aims to provide the research community with a panoramic view to facilitate understanding of the strengths, suitable scenarios, and evaluation practices of different agentic reasoning frameworks.

CaSTFormer: Causal Spatio-Temporal Transformer for Driving Intention Prediction

Accurate prediction of driving intention is key to enhancing the safety and interactive efficiency of human-machine co-driving systems. It serves as a cornerstone for achieving high-level autonomous driving. However, current approaches remain inadequate for accurately modeling the complex spatio-temporal interdependencies and the unpredictable variability of human driving behavior. To address these challenges, we propose CaSTFormer, a Causal Spatio-Temporal Transformer to explicitly model causal interactions between driver behavior and environmental context for robust intention prediction. Specifically, CaSTFormer introduces a novel Reciprocal Shift Fusion (RSF) mechanism for precise temporal alignment of internal and external feature streams, a Causal Pattern Extraction (CPE) module that systematically eliminates spurious correlations to reveal authentic causal dependencies, and an innovative Feature Synthesis Network (FSN) that adaptively synthesizes these purified representations into coherent spatio-temporal inferences. We evaluate the proposed CaSTFormer on the public Brain4Cars dataset, and it achieves state-of-the-art performance. It effectively captures complex causal spatio-temporal dependencies and enhances both the accuracy and transparency of driving intention prediction.