arXiv:2601.11957v4 Announce Type: replace Abstract: Overlapping calendar invitations force busy professionals to repeatedly decide which meetings to attend, reschedule, or decline. We refer to this preference-driven decision process as calendar conflict resolution. Automating this decision process is crucial yet challenging. Scheduling logistics can drain hours, and human delegation often fails at scale, which motivates us to ask: Can we trust large language models (LLMs) or language agents to manage time? To enable a systematic study of this question, we introduce CalConflictBench, a benchmar
The paper leverages recent advancements in large language models and reinforcement learning to address a long-standing challenge in personal and professional productivity.
This development indicates a tangible step towards autonomous AI agents capable of managing complex, preference-driven tasks, impacting how professionals interact with digital tools.
Personal time management, especially for busy professionals, could become increasingly delegated to AI, shifting demand for various productivity software and services.
- · AI software developers
- · Productivity software companies
- · Busy professionals
- · Traditional personal assistants
- · Basic calendar applications
- · Inefficient scheduling tools
AI models gain enhanced capabilities in autonomous decision-making for time management.
Increased reliance on AI agents for scheduling could lead to new forms of digital dependency and cybersecurity risks.
The technology could extend to broader domain-specific autonomous agents, collapsing other white-collar workflows.
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