We introduce Enhancing Graph of Thoughts (EGoT), a method designed to enhance the performance of large language models (LLMs) on complex reasoning tasks. EGoT automates the process of generating accurate responses using given data and a base prompt. The process consists of several steps: It obtains an initial response from the answering node using the base prompt. Evaluation node evaluates the response and generates reasoning for it, utilizing the score's probabilities to enhance evaluation accuracy. The reasoning from both the answering node and the evaluation node is aggregated to identify the problem in the response. This aggregated reasoning is incorporated into the base prompt to obtain an enhanced response. These steps are organized in a graph architecture, where the final leaf nodes are merged to produce a final response. As the graph descends, the temperature is lowered using Cosine Annealing and scoring, to explore diverse responses with earlier nodes and to focus on precise responses with later nodes. The minimum temperature in Cosine Annealing is adjusted based on scoring, ensuring that nodes with low scores continue to explore diverse responses, while those with high scores confirm accurate responses. In sorting 256 elements using GPT-4o mini, EGoT performs 88.31\% accuracy, while GoT (Graph of Thoughts) achieves 84.37\% accuracy. In the frozen lake problem using GPT-4o, EGoT averages 0.55 jumps or falls into the hole, while ToT (Tree of Thoughts) averages 0.89.

There are many various data-oriented attacks on robotic vehicles (RVs) that change the inputs of an RV control program. While much research has been dedicated to detecting the attacks, the recovery mechanism has received relatively less attention. Without recovery after detection, an RV cannot continue with its assigned missions. Unfortunately, the existing recovery mechanisms have limitations that make it difficult to deploy these in real RVs, such that they require additional hardware/software or can only recover from the limited types of data-oriented attacks. To overcome these limitations, we propose a framework called SCVMON that detects and helps RVs recover from various data-oriented attacks that generate inappropriate control commands. Based on the observation that data-oriented attacks inevitably change the values of some variables in RV control programs, SCVMON systematically identifies the safety-critical variables (SCVs) that can affect the safety of RVs. For efficient recovery, we extract from SCVs a set of monitored safety-critical variables (mSCVs) that can reflect all input changes, and monitor them to detect and recover from various data-oriented attacks. SCVMON does not depend on the physical nature of a specific sensor or hardware, which is a significant benefit, and it can be applied through a simple software update. Our evaluation shows that SCVMON can quickly detect and recover from 20 types of data-oriented attacks. Also, SCVMON incurs only 0.3% storage overhead and up to 5.1% runtime overhead, proving that it is suitable for RVs.