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cs.AI2026
IronLLM: Forging Compact Edge-Native Language Models for Real-Time Embodied Intelligence
Changdi Yang, Fengquan Jiao, Haochih Lin +14
We present IronLLM-0.6B, a 654M-parameter language model designed for efficient on-device inference. IronLLM-0.6B combines a hybrid attention architecture with X-MTP, a lightweight…
cs.AI2026
Routing Drift Alone Does Not Diagnose Failure in Merged MoE LLMs
Yuanyi Wang, Yanggan Gu, Su Lu +7
Model merging efficiently combines specialized large language models (LLMs) without joint retraining, but can substantially alter expert routing in Mixture-of-Experts (MoE) models.…
cs.AI2025
InfiAlign: A Scalable and Sample-Efficient Framework for Aligning LLMs to Enhance Reasoning Capabilities
Shuo Cai, Su Lu, Qi Zhou +4
Large language models (LLMs) have exhibited impressive reasoning abilities on a wide range of complex tasks. However, enhancing these capabilities through post-training remains res…