1 citations · 2 across the 7 of their papers we have counts for
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cs.AI2026
When2Think: Learning When and How Much to Reason
Jaejun Shim, HyunJin Kim, Young Jin Kim +1
Large Reasoning Models (LRMs) often overthink easy problems and underthink hard ones, leading to inefficient computation allocation. Existing methods regulate generated computation…
cs.AI2025
Research Superalignment Should Advance Now with Alternating Competence and Conformity Optimization
HyunJin Kim, Xiaoyuan Yi, Jing Yao +4
The recent leap in AI capabilities, driven by big generative models, has sparked the possibility of achieving Artificial General Intelligence (AGI) and further triggered discussion…
cs.AI2024★ 1 cited
Talking with Tables for Better LLM Factual Data Interactions
Jio Oh, Geon Heo, Seungjun Oh +5
Large Language Models (LLMs) often struggle with requests related to information retrieval and data manipulation that frequently arise in real-world scenarios under multiple condit…