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This paper proposes an uncertainty-aware reinforcement learning framework for autonomous driving that uses expert advice guided by adaptive uncertainty thresholds and a commitment-cooldown strategy to improve safety and efficiency. Experiments in the CARLA simulator show a 5-7% success improvement over the IQN baseline.
BELIEF is a structured evidence modeling and uncertainty-aware fusion framework for biomedical question answering that converts retrieved documents into evidence objects and combines symbolic Dempster-Shafer reasoning with LLM-based inference. Experiments on PubMedQA, MedQA, and MedMCQA show BELIEF achieves state-of-the-art results in the majority of settings.
ScreenSearch introduces a system for ambiguity-aware desktop exploration, combining structural screen retrieval and deduplication with a PUCT graph-bandit to handle partial observability in GUI agents. It collects over 1M screenshots across 11 applications and demonstrates a novelty–ambiguity trade-off in exploration policies.
PRISM is a diffusion-based framework for text image super-resolution that uses flow-matching prior rectification and uncertainty-aware residual encoding to improve accuracy under severe degradation, achieving state-of-the-art performance with millisecond-level inference.