8 papers
Muse Spark Safety & Preparedness Report
Cristina Menghini, Peter Ney, Hamza Kwisaba +117
Muse Spark is the latest large language model developed by Meta. In this report, we first present evaluations for catastrophic risk domains under Meta's Advanced AI Scaling Framewo…
Positional Encoding via Token-Aware Phase Attention
Yu Wang, Sheng Shen, Rémi Munos +2
We prove under practical assumptions that Rotary Positional Embedding (RoPE) introduces an intrinsic distance-dependent bias in attention scores that limits RoPE's ability to model…
LoReC: Rethinking Large Language Models for Graph Data Analysis
Hongyu Zhan, Qixin Wang, Yusen Tan +6
The advent of Large Language Models (LLMs) has fundamentally reshaped the way we interact with graphs, giving rise to a new paradigm called GraphLLM. As revealed in recent studies,…
The Alignment Waltz: Jointly Training Agents to Collaborate for Safety
Jingyu Zhang, Haozhu Wang, Eric Michael Smith +7
Harnessing the power of LLMs requires a delicate dance between being helpful and harmless. This creates a fundamental tension between two competing challenges: vulnerability to adv…
Many-Tier Instruction Hierarchy in LLM Agents
Jingyu Zhang, Tianjian Li, William Jurayj +3
Large language model agents receive instructions from many sources-system messages, user prompts, tool outputs, other agents, and more-each carrying different levels of trust and a…
Large Reasoning Models Learn Better Alignment from Flawed Thinking
ShengYun Peng, Eric Smith, Ivan Evtimov +7
Large reasoning models (LRMs) "think" by generating structured chain-of-thought (CoT) before producing a final answer, yet they still lack the ability to reason critically about sa…