2 papers
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…
cs.CL2026
Barriers to Discrete Reasoning with Transformers: A Survey Across Depth, Exactness, and Bandwidth
Michelle Yuan, Weiyi Sun, Amir H. Rezaeian +5
Transformers have become the foundational architecture for a broad spectrum of sequence modeling applications, underpinning state-of-the-art systems in natural language processing,…