How Can We Make LLM Agents More Reliable in Memory and Tool Use?
How Can We Make LLM Agents More Reliable in Memory and Tool Use? Three recent papers look at a shared problem in tool-using agents: an LLM may know how to call a tool, but still struggle with choosing the right tool at the right time, reusing past experience, or adapting a learned skill when the environment changes. "Contract2Tool: Learning Preconditions and Effects for Reliable Tool-Augmented LLM Agents" focuses on tool appropriateness through lightweight contracts about preconditions, effects, risk, and cost. "MemToolAgent" examines how long-term memory, retrieval of similar past cases, and reflection can improve tool-using behavior. "Efficient Skill Grounding via Code Refactoring with Small Language Models" addresses a related reliability issue in embodied agents, where a reusable skill can fail when embodiment or environment details differ. All three were announced on arXiv in June 2026 and, taken together, point to three practical axes for more stabl...