The field of large language models (LLMs) is moving towards addressing critical challenges in privacy and reasoning. Researchers are exploring innovative approaches to mitigate privacy leakage in LLMs, such as test-time interventions that balance privacy and utility. Additionally, there is a growing interest in enhancing LLM code debugging capabilities through psychologically backed scaffold reasoning frameworks. These frameworks aim to optimize cognitive pathways and improve reasoning accuracy and efficiency. Furthermore, investigations into chain-of-thought (CoT) monitorability are underway, focusing on the challenges and potential of monitoring potential model misbehavior through CoT analysis. Noteworthy papers include:

• SALT, which introduces a lightweight test-time intervention to mitigate privacy leakage in LLMs, achieving significant reductions in contextual privacy leakage.
• Dual-Process Scaffold Reasoning, which proposes a novel framework for code debugging that outperforms other reasoning approaches in accuracy and efficiency.
• MoME, a new paradigm that enables LLMs to monitor other models' misbehavior through their CoT and provide structured judgments along with supporting evidence.