5 papers
Federation over Harness
Dixi Yao, Tahseen Rabbani, Tian Li
Modern LLM-powered agents increasingly improve through experience sharing, where distributed agents distill local reasoning experience into reusable knowledge, such as a shared ins…
Learning Scientific Exploration from Human Research Decision Trajectories
Xuchen Gong, Shane Gu, Haokun Liu +3
A key challenge in building AI systems for scientific research is enabling : the systematic process of investigating unknown phenomena or ideas to…
Federation over Text: Insight Sharing for Multi-Agent Reasoning
Dixi Yao, Tahseen Rabbani, Manzil Zaheer +1
Modern agents specialize in varying domains while there is no clear approach combining different domain skills. We propose a federated learning-like framework, Federation over Text…
Hold Onto That Thought: Assessing KV Cache Compression On Reasoning
Minghui Liu, Aadi Palnitkar, Tahseen Rabbani +9
Large language models (LLMs) have demonstrated remarkable performance on long-context tasks, but are often bottlenecked by memory constraints. Namely, the KV cache, which is used t…
Exploring System-Heterogeneous Federated Learning with Dynamic Model Selection
Dixi Yao
Federated learning is a distributed learning paradigm in which multiple mobile clients train a global model while keeping data local. These mobile clients can have various availabl…