Stanford researchers introduced TRACE, an open-source system that diagnoses recurring agent failures and trains targeted capabilities using synthetic reinforcement learning environments. TRACE identif…
Researchers introduced TRACE, a new benchmark for evaluating conversational AI systems that use external tools, revealing that all current state-of-the-art evaluation frameworks fail to detect critica…
TRACE, an open-source Python library for self-organizing long-term memory in LLM agents, was released on PyPI. It organizes conversation history into a hierarchical B+Tree to enable efficient, multi-p…
Researchers introduced TRACE, a training-free framework that stabilizes reconstruction trajectories in imaging inverse problems by coupling adjacent intermediate states. The method, which approximates…
Researchers have developed TRACE, a trajectory-aware AI agent that uses large language model reasoning to optimize drug lead compounds by treating tool selection as a sequential decision-making proces…