arXiv:2605.23668v1 Announce Type: cross Abstract: Although large language model (LLM) conversational systems process millions of multi-turn dialogues daily, they remain fundamentally reactive: they respond only after the user types a query. A key step toward proactive interaction is next-query prediction, which anticipates the user's subsequent query based solely on the preceding dialogue. Progress on this task is hindered by the lack of dedicated benchmarks and a fundamental efficiency--quality trade-off: naively concatenating full dialogue history incurs linearly growing token consumption, w
LLM conversational systems are mature enough to reveal their fundamental reactive limitations, prompting research into more proactive interaction paradigms.
Proactive AI interaction represents a significant step towards more integrated and efficient human-computer interfaces, expanding the utility and agency of AI systems beyond simple query-response.
The focus in conversational AI shifts from mere response generation to anticipatory interaction, demanding new architectures for memory and intent prediction.
- · AI-powered customer service companies
- · Conversational AI developers
- · Productivity software providers
- · Reactive chat systems
- · Basic conversational AI frameworks
Next-query prediction improves user experience and efficiency in AI interactions.
More proactive AI could lead to a reduction in certain white-collar tasks by anticipating user needs.
The development of truly proactive AI agents could fundamentally alter human-computer interaction paradigms, making computers more like active collaborators.
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Read at arXiv cs.AI