Shuai Su

PlanTwin introduces a privacy-preserving architecture that allows cloud-hosted LLMs to plan over sensitive local environments by projecting the raw state into a sanitized, abstract digital twin.

The paper introduces Plan, a structured agentic behavior that decomposes multi-hop questions into ordered sub-questions before retrieval, and proposes a self-bootstrapping paradigm to train it without relying on model distillation.

The paper proposes CSMR, a cognitive scheduling framework that allows a language model to dynamically decide when to acquire task-relevant visual evidence, significantly improving multimodal reasoning accuracy.