New Research Addresses LLM Efficiency and Safety Concerns
Three recent papers on arXiv highlight critical challenges facing large language models:
The Curse of Depth in LLMs
According to arXiv paper 2502.05795v3, researchers have identified what they call the “Curse of Depth” in modern Large Language Models. The paper “highlights, explains, and addresses the recent observation” that nearly half of the layers in LLMs “are less effective than expected,” representing a significant efficiency concern in current model architectures.
Compressed Guardrail Training
A separate study (arXiv:2601.00454v1) introduces “Defensive M2S,” a new training approach for guardrail models. According to the abstract, these guardrail models are “essential for ensuring the safety of Large Language Model deployments,” but processing complete multi-turn conversation histories creates “significant computational cost.” The proposed method uses a “training paradigm that fine-tun[es]” models on compressed conversations to address this challenge.
Safety Risks in Reasoning Models
Research paper arXiv:2512.00412v2 examines Large Reasoning Models (LRMs), which the authors describe as “a powerful advancement in multi-step reasoning tasks.” While these models offer “enhanced transparency and logical consistency through explicit chains of thought,” the paper warns that “these models introduce novel safety” challenges requiring further investigation.