Tianda Sun

LLMs encode tool-call dependency graphs in their residual streams, revealing a structured understanding of agentic execution beyond simple sequence prediction.

LLMs trained to use knowledge graphs can exhibit a "peak-then-collapse" behavior, where performance initially improves with RL but then catastrophically degrades, revealing a surprising fragility in tool use.