The field of software vulnerability detection is rapidly advancing, with a focus on improving the efficiency and effectiveness of fuzzing techniques. Recent developments have seen the integration of large language models and machine learning algorithms to enhance the discovery of software vulnerabilities. The use of probabilistic models and static analysis has also shown promise in predicting energy consumption and identifying object-oriented access patterns. Noteworthy papers include GPTrace, which leverages LLM embeddings for effective crash deduplication, and HarnessAgent, which introduces a tool-augmented agentic framework for scalable harness construction. Other notable papers include OOPredictor, which predicts object-oriented accesses using static analysis, and ReFuzz, which reuses tests for processor fuzzing with contextual bandits. These advancements have the potential to significantly improve the detection and mitigation of software vulnerabilities, and are expected to continue shaping the field in the coming years.