3 citations · 7 across the 16 of their papers we have counts for
5 papers · 1 filter
Can Test-Time Memory Evolution Outperform Test-Time Training for Scientific Discovery?
Yepeng Liu, Shengmai Chen, Xuandong Zhao +4
Recent \emph{test-time training} (TTT) frameworks have enabled open-weight large language models (LLMs) to outperform inference-only frameworks powered by frontier models on scient…
Learning to Follow In-Context Watermark Instructions via Self-Distillation
Yepeng Liu, Tianyi Chen, Xuandong Zhao +2
In-context watermarking (ICW) prepends an instruction to a query asking the model to embed a statistically detectable signal in its response. It thus equips LLMs with a watermarkin…
MemFail: Stress-Testing Failure Modes of LLM Memory Systems
Ishir Garg, Neel Kolhe, Dawn Song +1
Large language model (LLM) agents increasingly rely on external memory systems to remain consistent across long-horizon interactions, but little empirical work has been done to und…
SafeKey: Amplifying Aha-Moment Insights for Safety Reasoning
Kaiwen Zhou, Xuandong Zhao, Gaowen Liu +4
Large Reasoning Models (LRMs) introduce a new generation paradigm of explicitly reasoning before answering, leading to remarkable improvements in complex tasks. However, they pose…
Multimodal Situational Safety
Kaiwen Zhou, Chengzhi Liu, Xuandong Zhao +3
Multimodal Large Language Models (MLLMs) are rapidly evolving, demonstrating impressive capabilities as multimodal assistants that interact with both humans and their environments.…