A Context Alignment Pre-processor for Enhancing the Coherence of Human-LLM Dialog

ArXi:2603.16052v1 Announce Type: new Large language models (LLMs) have made remarkable progress in generating fluent text, but they still face a critical challenge of contextual misalignment in long-term and dynamic dialogue. When human users omit premises, simplify references, or shift context abruptly during interactions with LLMs, the models may fail to capture their actual intentions, producing mechanical or off-topic responses that weaken the collaborative potential of dialogue.