1 citations · 1 across the 4 of their papers we have counts for
5 papers
Seq@K: Evaluating Language Models Under Sequential Feedback
Thomson Yen, Gilbert Yang, Naimeng Ye +8
Language-model agents differ fundamentally from prior intelligent systems in their ability to learn from natural language in context. This capability enables agents to iteratively…
LatentGym: A Testbed For Cross-Task Experiential Learning With Controllable Latent Structure
Daksh Mittal, Tommaso Castellani, Thomson Yen +7
We envision continually learning agentic systems that become more useful over time: as they encounter sequences of related tasks, they should infer the hidden structure shared acro…
SynthTools: A Framework for Scaling Synthetic Tools for Agent Development
Tommaso Castellani, Naimeng Ye, Daksh Mittal +4
For agentic systems to use external tools to solve complex, long-horizon tasks, we need a large set of diverse and controllable tool-use environments. We introduce SynthTools, a fu…
PersonalLLM: Tailoring LLMs to Individual Preferences
Thomas P. Zollo, Andrew Wei Tung Siah, Naimeng Ye +2
As LLMs become capable of complex tasks, there is growing potential for personalized interactions tailored to the subtle and idiosyncratic preferences of the user. We present a pub…
Exchangeable Sequence Models Quantify Uncertainty Over Latent Concepts
Naimeng Ye, Hongseok Namkoong
Intelligent agents must be able to articulate its own uncertainty. In this work, we show that pre-trained sequence models are naturally capable of probabilistic reasoning over exch…