Evaluation Benchmarks for High-Context Conversational Intelligence

Abstract

High-context conversational intelligence requires an AI system to not only interpret direct linguistic content but
also maintain continuity of meaning across time, infer implicit intent, and adapt to subtle shifts in tone, social
expectations, and situational framing. Existing evaluation methods often measure surface-level correctness and
fluency while overlooking deeper discourse reasoning processes that enable effective, human-aligned dialogue.
This study proposes a structured benchmarking framework that evaluates conversational models across multi-turn
context retention, semantic continuity, adaptive inference, pacing modulation, and robustness under context
distortion. Experimental analysis shows that conversational performance is strongly influenced by how
representation memory and contextual inference are encoded rather than by model scale alone. Further, enterprise
workflow integration tests highlight that sustained conversational coherence depends on coordination between
inference layers and application-level session persistence mechanisms. The findings establish a foundation for
benchmarking and developing conversational systems capable of reliably supporting high-context human–AI
communication.