Tag
The paper proposes FLARE, a framework combining fuzzy logic, time-driven activity-based costing, and ROI analysis to assess the economic and operational implications of AI adoption in healthcare under uncertainty.
The paper proposes intuitionistic fuzzy deep RVFL (IF-dRVFL) and ensemble deep RVFL (IF-edRVFL) frameworks that use sample neighborhood information to improve robustness against noise and outliers in classification tasks, outperforming existing SOTA fuzzy and non-fuzzy approaches on benchmark datasets.
This paper proposes a training-free, uncertainty-aware inference framework for using large language models in operations research. The method uses short lookahead simulations and importance resampling to improve the coherence of mathematical formulations, outperforming standard baselines on OR benchmarks.
This paper presents FST.ai 2.5, an explainable and uncertainty-aware AI framework for Olympic and Para-Taekwondo that integrates athlete digital twins, competition analytics, and federation-scale decision support.
This paper formalizes the problem of when to invoke LLMs in streaming inference systems as a risk-based sequential stopping problem. It proves theoretical guarantees and empirically validates the framework on turbofan degradation data.
This paper presents AffectFlow-DINO, a multi-task learning system for the 11th ABAW challenge that uses a conditional rectified-flow head to model uncertainty in in-the-wild facial behavior estimation, achieving substantial improvements over the baseline.
This paper introduces xAARA, an uncertainty-aware multi-expert fusion engine that augments clinical assessment of stroke rehabilitation by providing calibrated uncertainty and interpretable explanations, achieving high accuracy and reducing predictive uncertainty in movement quality evaluation.
Proposes ULPS, a framework integrating a calibrated LLM into RL training with uncertainty-modulated guidance and A*-based symbolic trajectories, achieving improved success rate and sample efficiency on MiniGrid-UnlockPickup.
Neetyabhas is a framework for uncertainty-aware public policy optimization using hierarchical reinforcement learning agents in agent-based epidemic simulations. It models individual behaviors (mask-wearing, vaccination, shopping) and policymaker interventions under uncertainty, demonstrating effective COVID-19 outbreak management.
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.