ActivityEditor introduces a dual-LLM-agent system with reinforcement learning that produces statistically faithful and physically valid human mobility trajectories in zero-shot cross-regional settings.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
ActivityEditor: Learning to Synthesize Physically Valid Human Mobility
ActivityEditor introduces a dual-LLM-agent system with reinforcement learning that produces statistically faithful and physically valid human mobility trajectories in zero-shot cross-regional settings.