2 papers
cs.LG2026
Learning to Orchestrate Agents in Natural Language with the Conductor
Stefan Nielsen, Edoardo Cetin, Peter Schwendeman +3
Powerful large language models (LLMs) from different providers have been expensively trained and finetuned to specialize across varying domains. In this work, we introduce a new ki…
cs.LG2026
TRINITY: An Evolved LLM Coordinator
Jinglue Xu, Qi Sun, Peter Schwendeman +3
Combining diverse foundation models is promising, but weight-merging is limited by mismatched architectures and closed APIs. Trinity addresses this with a lightweight coordinator t…