Temporal Uncertainty
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Related Articles from SNS
Beyond Static Uncertainty: Modeling Temporal Uncertainty Dynamics for Probabilistic Time Series Forecasting
arXiv:2603.24254v3 Announce Type: replace Abstract: Real-world time series exhibit temporally structured uncertainty: volatility clusters in turbulent regimes, dissipates in stable periods, and shifts abruptly around structural breaks. Yet many probabilistic forecasting methods estimate predictive uncertainty as an independent per-step quantity, leaving the evolution and persistence of volatility regimes under-modeled. We formalize this missing dimension as temporal uncertainty dynamics and...
Can I Take Another Dose? Evaluating LLM Decision-Making Under Temporal Uncertainty in OTC Dosing QA
arXiv:2606.04262v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for everyday health questions, including whether a user can safely take another dose of an over-the-counter (OTC) medication. Yet this common safety-relevant setting remains underexplored in existing medical QA evaluations, where correct answers require tracking dose timing, computing rolling 24-hour intake, following product-label constraints, and handling incomplete medication histories.
Uncertainty-Aware and Temporally Regulated Expert Advice in Reinforcement Learning for Autonomous Driving
arXiv:2605.30576v1 Announce Type: new Abstract: Exploration in reinforcement learning for autonomous driving is inherently unsafe: agents must experience novel behaviors to learn, yet exploration can lead to collisions or off-road driving. We propose an uncertainty-aware framework that leverages expert advice to guide exploration while avoiding long-term dependence. Advice is triggered when epistemic or aleatoric uncertainty exceeds adaptive thresholds derived from rolling buffers, ensuring...
Remember with Confidence: Uncertainty Quantification for Spatio-temporal Memory with Probabilistic Guarantees
Announce Type: new Abstract: Long-horizon robot operation requires spatio-temporal memory to record the environment state and recall it for downstream reasoning. Scene graphs and retrieval-augmented systems ground VLM descriptions to persistent 3D entities with rich semantic descriptions. However, VLM captions are noisy and viewpoint-inconsistent, and existing systems treat them as an oracle with no mechanism to detect unreliable stored descriptions.
RadOT-Eval: Auditable Structured-Evidence Transport for Radiology Report Evaluation
arXiv:2606.08769v1 Announce Type: new Abstract: Automatic evaluation is critical for high-stakes text generation, where errors often involve omitted findings, hallucinated content, polarity reversals, location changes, uncertainty mismatches, and temporal-comparison errors rather than low surface similarity alone. Radiology report generation provides a challenging test case because generated reports must preserve structured clinical evidence across sources. We present RadOT-Eval, an...
Representation over Routing: Diagnosing Temporal Routing Pathologies in Multi-Timescale PPO
Announce Type: replace Abstract: Temporal credit assignment in reinforcement learning is often approached by introducing value estimates at multiple discount factors. A natural next step is to let the actor dynamically route among these temporal heads, using either differentiable attention or heuristic uncertainty weights. This paper argues that such routing can create a numerical shortcut rather than a reliable temporal abstraction.
Probabilistic Precipitation Nowcasting with Rectified Flow Transformers
arXiv:2605.31204v1 Announce Type: new Abstract: Accurate weather forecasts are essential across various domains and are safety-critical in extreme weather conditions. Compared to simulation-based forecasting, data-driven approaches show greater efficiency, enabling short-term, high-resolution nowcasting. In particular, diffusion models proved effective in weather nowcasting due to their strong probabilistic foundation.
Personalized and Robust Proactive Robot Assistance with Uncertainty-Guided LLM Reasoning
arXiv:2606.08458v1 Announce Type: new Abstract: Proactive robot assistance in household environments requires accurate prediction of human activities and object usage under dynamic and noisy conditions. Existing approaches often rely on complex spatio-temporal models, which can be computationally expensive and sensitive to environmental variability. In this paper, we propose GLOBE, a lightweight framework that combines n-gram Markov models for capturing temporal behavioral patterns with...
Bayesian learning for the stochastic shortest path problem
Announce Type: cross Abstract: Sequential decision-making problems are often modelled as a Markov decision process (MDP). We focus on the stochastic shortest path (SSP) problem, which is an infinite-horizon undiscounted MDP with absorbing terminal states. We develop a Bayesian framework to learn the optimal decision strategy through interactions with the decision-making task.
Spline Policy: A Structured Representation for Robot Policies
arXiv:2606.07386v1 Announce Type: new Abstract: Modern imitation-learning policies for robot manipulation often represent actions as fixed-resolution action chunks, which are simple and effective but expose limited geometric and temporal structure before execution. This paper studies Spline Policy (SP), a structured representation that replaces action chunks with spline parameters while keeping the policy backbone unchanged. The predicted spline can be decoded as a compact continuous...