DYNAMIC CONTEXT-AWARE MULTI-AGENT COLLABORATIVE REASONING FRAMEWORK V2.0 FOR PHYSICS EXPERIMENTS

While AI shows great potential in empowering physics experiments, the highly innovative and rapidly iterating nature of these experiments poses challenges for traditional Large Language Models (LLMs). Reasoning on specific methods typically requires full-context injection of local knowledge bases, resulting in excessive context consumption and high inference costs. This study proposes an intelligent agent framework for physics experiments integrating dynamic context-aware management and collaborative reasoning. Adaptable to existing LLMs, the framework dynamically extracts and injects high-value information into the context, significantly reducing inference costs and improving efficiency without compromising reasoning quality compared to full-context methods. By supporting rapid secondary development and diverse scenario adaptation, the framework effectively overcomes computational and cost bottlenecks, offering an efficient and flexible paradigm for the automation and intelligent transformation of physics experiments.