arXiv:2510.25799v3 Announce Type: replace Abstract: Human experts often struggle to select the best option from a large set of items with multiple competing objectives, a process bottlenecked by the difficulty of formalizing complex, implicit preferences. To address this, we introduce LISTEN (LLM-based Iterative Selection with Trade-off Evaluation from Natural-language), an agentic LLM-based framework that treats the LLM as a decision-making agent capable of iteratively refining its internal preference model and taking actions (e.g., proposing utilities or selecting candidates) to maximize ali
The rapid advancement of large language models (LLMs) has enabled their application to complex decision-making processes, where they can emulate and refine human-like preference models.
This development allows for LLMs to overcome bottlenecks in multi-objective selection by formalizing implicit preferences, enabling more efficient and optimized decision-making across various domains.
LLMs shift from purely generative or analytical tools to active, iterative decision-making agents capable of refining their internal models and proposing actions to achieve maximal utility in complex scenarios.
- · AI-driven decision-making platforms
- · Consulting services (augmented by AI)
- · Companies with complex resource allocation problems
- · Software as a Service (SaaS)
- · Traditional human-only decision-making processes
- · Manual data analytics services
- · Businesses relying on inefficient, subjective selection
Increased efficiency and optimization in enterprise-level selection processes for complex items.
Automation of highly skilled decision-making tasks, potentially displacing human experts in narrow domains.
The development of more sophisticated, self-improving AI agents that can operate with minimal human oversight in strategic planning.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL