A Context Alignment Pre-processor for Enhancing the Coherence of Human-LLM Dialog
arXiv:2603.16052v1 Announce Type: new Abstract: 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. To address this problem, this paper proposes a computational framework called the Context Alignment Pre-processor (C.A.P.). Rather than operating during generation, C.A.P. functions as a pre-processing module between user input and response generation. The framework includes three core processes: (1) semantic expansion, which extends a user instruction to a broader semantic span including its premises, literal meaning, and implications; (2) time-weighted context retrieval, which prioritizes rec
arXiv:2603.16052v1 Announce Type: new Abstract: 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. To address this problem, this paper proposes a computational framework called the Context Alignment Pre-processor (C.A.P.). Rather than operating during generation, C.A.P. functions as a pre-processing module between user input and response generation. The framework includes three core processes: (1) semantic expansion, which extends a user instruction to a broader semantic span including its premises, literal meaning, and implications; (2) time-weighted context retrieval, which prioritizes recent dialogue history through a temporal decay function approximating human conversational focus; and (3) alignment verification and decision branching, which evaluates whether the dialogue remains on track by measuring the semantic similarity between the current prompt and the weighted historical context. When a significant deviation is detected, C.A.P. initiates a structured clarification protocol to help users and the system recalibrate the conversation. This study presents the architecture and theoretical basis of C.A.P., drawing on cognitive science and Common Ground theory in human-computer interaction. We argue that C.A.P. is not only a technical refinement but also a step toward shifting human-computer dialogue from one-way command-execution patterns to two-way, self-correcting, partnership-based collaboration. Finally, we discuss implementation paths, evaluation methods, and implications for the future design of interactive intelligent systems.
Executive Summary
This article proposes a Context Alignment Pre-processor (C.A.P.) to enhance the coherence of human-LLM dialog by mitigating contextual misalignment. C.A.P. operates as a pre-processing module, including three core processes: semantic expansion, time-weighted context retrieval, and alignment verification and decision branching. The framework draws on cognitive science and Common Ground theory, aiming to shift dialogue from one-way command-execution to two-way, self-correcting partnership-based collaboration. The authors provide a theoretical basis for C.A.P. and outline implementation paths, evaluation methods, and implications for future design. This work has significant implications for the development of interactive intelligent systems, enabling more effective and natural human-computer dialogue.
Key Points
- ▸ C.A.P. addresses the critical challenge of contextual misalignment in human-LLM dialog
- ▸ The framework includes three core processes: semantic expansion, time-weighted context retrieval, and alignment verification and decision branching
- ▸ C.A.P. draws on cognitive science and Common Ground theory to inform its design
Merits
Strength
The proposed framework is well-grounded in cognitive science and Common Ground theory, providing a theoretically sound approach to addressing contextual misalignment in human-LLM dialog
Technical Innovation
The use of a pre-processing module to align context and facilitate more effective dialogue is a significant technical innovation in the field of human-computer interaction
Demerits
Limitation
The framework's effectiveness may be limited by its reliance on user input and the ability of users to provide clear and concise instructions
Scalability
The scalability of C.A.P. for large-scale, complex dialogue systems is unclear and requires further investigation
Expert Commentary
The proposed Context Alignment Pre-processor (C.A.P.) is a significant contribution to the field of human-computer interaction, offering a theoretically sound and technically innovative approach to addressing contextual misalignment in human-LLM dialog. The framework's reliance on cognitive science and Common Ground theory provides a solid foundation for its design, and its potential to facilitate more effective and natural dialogue is substantial. However, the scalability and effectiveness of C.A.P. in large-scale, complex dialogue systems require further investigation. Additionally, the development of C.A.P. has significant implications for the design of policies and regulations governing the use of AI systems in human-computer interaction, emphasizing the need for more nuanced and context-sensitive approaches.
Recommendations
- ✓ Further research is needed to investigate the scalability and effectiveness of C.A.P. in large-scale, complex dialogue systems
- ✓ The development of C.A.P. should be accompanied by a careful consideration of the policy and regulatory implications of its use in human-computer interaction