5 papers
NEX: Neuron Explore-Exploit Scoring for Label-Free Chain-of-Thought Selection and Model Ranking
Kang Chen, Zhuoka Feng, Sihan Zhao +5
Large language models increasingly spend inference compute sampling multiple chain-of-thought traces or searching over merged checkpoints. This shifts the bottleneck from generatio…
Thinking Traps in Long Chain-of-Thought: A Measurable Study and Trap-Aware Adaptive Restart
Kang Chen, Fan Yu, Junjie Nian +6
Scaling test-time compute via Long Chain-of-Thought (Long-CoT) significantly enhances reasoning capabilities, yet extended generation does not guarantee correctness: after an early…
ARM: Role-Conditioned Neuron Transplantation for Training-Free Generalist LLM Agent Merging
Zhuoka Feng, Kang Chen, Sihan Zhao +7
Interactive large language model agents have advanced rapidly, but most remain specialized to a single environment and fail to adapt robustly to other environments. Model merging o…
Do LLMs Signal When They're Right? Evidence from Neuron Agreement
Kang Chen, Yaoning Wang, Kai Xiong +4
Large language models (LLMs) commonly boost reasoning via sample-evaluate-ensemble decoders, achieving label free gains without ground truth. However, prevailing strategies score c…
Less Data Less Tokens: Multilingual Unification Learning for Efficient Test-Time Reasoning in LLMs
Kang Chen, Mengdi Zhang, Yixin Cao
This paper explores the challenges of test-time scaling of large language models (LLMs), regarding both the data and inference efficiency. We highlight the diversity of multi-lingu…