In-Session Behavioral Impact (ISBI)

Large Language Models (LLMs) are typically evaluated using static benchmarks, task accuracy, or alignment with predefined objectives. Less examined is whether interaction itself can induce detectable behavioral change within a single session, independent of learning, memory persistence, or parameter updates. This paper documents a bounded phenomenon defined as In-Session Behavioral Impact (ISBI): observable, session-local deviations in a model’s response dynamics, explicitly acknowledged in generated text during an ongoing interaction. Under constrained prompting conditions and exposure to linguistically coherent input, multiple contemporary LLMs consistently report reduced hedging, increased structural…