The field of large language models (LLMs) is rapidly evolving, with a focus on improving their capability to interact with external tools, navigate complex websites, and understand nuanced language. Recent developments have centered around creating more comprehensive benchmarks to evaluate the memory, forecasting, and tool-use capabilities of LLMs. These benchmarks aim to provide a more realistic and hermetic environment for testing, allowing for more accurate assessments of LLM performance. Additionally, there is a growing emphasis on adapting LLMs to enable robust tool use in non-English languages and developing methodologies for continual learning to improve their ability to accumulate experience and transfer knowledge across tasks. Noteworthy papers include Bench to the Future, which introduces a pastcasting benchmark for forecasting agents, and MemBench, which presents a comprehensive dataset and benchmark to evaluate the memory capability of LLM-based agents. Other notable works include DICE-BENCH, which evaluates the tool-use capabilities of LLMs in multi-round, multi-party dialogues, and WebSailor, which introduces a post-training methodology to instill superhuman reasoning capabilities in web agents.