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cs.CL2026
Ask Less, Ask Better: Allocating User Attention in Proactive LLM Agents with Interruption Budgets
Xuanming Zhang, Amir H. Rezaeian, Miguel Ballesteros +6
LLM agents can ask clarifying questions to complete tasks, but answering each question demands users' time and effort. Existing evaluations typically measure this burden by countin…
cs.CL2026
Budget-Aware Anytime Reasoning with LLM-Synthesized Preference Data
Xuanming Zhang, Shwan Ashrafi, Aziza Mirsaidova +5
We study the reasoning behavior of large language models (LLMs) under limited computation budgets. In such settings, producing useful partial solutions quickly is often more practi…