3 papers
cs.LG2026
Co-Adaptive Multi-Task LoRA: Transfer-Aware, Label-Free Control of Domain Participation
Wei Zhang, Lin Tang, Ming Zhao +1
Fine-tuning a single low-rank adapter on many domains at once is multi-task learning: the domains must be co-learned, and how they share the adapter decides whether they help or hu…
cs.AI2026
Beyond Forecasting: The Belief-to-Trade Layer in Prediction-Market Agents
Yishu Wang, Yuxuan Wang, Jiaqi Deng +1
Forecasting future events has attracted growing attention as a testbed for general-purpose AI. A natural way to ground this evaluation is let the models trade in the prediction mar…
cs.LG2026
Predicting Mergeability of Parameter-Efficient Fine-Tuning Updates
Lin Tang, Wei Zhang, Jing Li +3
Low-rank adaptation (LoRA) makes it cheap to train many domain- and task-specific language model adapters, but whether two adapters can be merged is usually discovered only after b…