3 citations · 7 across the 16 of their papers we have counts for
4 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…
InfoSynth: Information-Guided Benchmark Synthesis for LLMs
Ishir Garg, Neel Kolhe, Xuandong Zhao +1
Large language models (LLMs) have demonstrated significant advancements in reasoning and code generation, but efficiently creating new benchmarks to evaluate these capabilities rem…