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SeLMRoute introduces an LLM routing framework that separates candidate-independent semantic evidence extraction from performance learning, achieving 72.08% average accuracy on LLMRouterBench across 15 datasets and 20 candidate models, outperforming the strongest fixed candidate (69.23%) and enabling both performance-oriented and cost-aware routing decisions.
EnSol is an environment-aware graph neural network that predicts molecular solubility by representing solutes and solvents as graphs and using cross-attention to model interactions, with probabilistic outputs to capture temperature effects and experimental uncertainty. It achieves state-of-the-art performance on benchmark datasets, validated experimentally.
ThinkFlow is a novel end-to-end latent memory framework for lifelong conversational agents that uses probabilistic vectors to overcome textual memory bottlenecks. It enables autonomous personalization through self-evolution and test-time learning, outperforming existing memory systems.
The paper introduces PGP-Clinical-TimeKAN, a trajectory-first framework for joint probabilistic forecasting of clinical data, evaluated on ICU datasets with improved accuracy and probabilistic performance over existing models.
This paper introduces a computational model that uses probabilistic reasoning over language and code to simulate human inductive learning and active inquiry, outperforming pure LLMs and classic Bayesian models in behavioral studies.
This paper reformulates EEG-based reaction-time decoding as event-time posterior modeling, using behavioral latency as weak supervision to improve prediction accuracy.
This paper introduces a probabilistic approach to end-to-end AI weather forecasting by enhancing the Aardvark Weather model with learned observation noise and Monte Carlo dropout, improving mean forecasts by 4.2% on average and separating aleatoric and epistemic uncertainty.
The article proposes a framework for quantifying system-level harms from AI adoption in complex sociotechnical systems, using a case study on adversarial manipulation in financial infrastructure to demonstrate increased risks.
CLaST introduces a context-aware contrastive VAE framework for probabilistic multivariate time series forecasting, demonstrating significant performance improvements over baseline methods on multiple benchmarks.
This paper proposes modeling the CLIP latent space using Mixtures of von Mises–Fisher distributions on the unit hypersphere, capturing its directional and multimodal structure better than Gaussian assumptions. The model improves long-tailed and out-of-distribution detection and provides a semantic decomposition of CLIP embeddings.
This paper introduces PGRE, a probabilistic model for dynamic knowledge graphs that captures inter-relational dependencies using Poisson-Gamma and Markov processes, achieving competitive link prediction performance especially in sparse settings.
Proposes a hierarchical Bayesian framework for meta-learning in dynamical systems from multiple sparse, noisy datasets, using gradient-based MCMC with an embedded ODE solver for efficient posterior inference of shared and dataset-specific parameters.
Proposes NF-CoT, a latent reasoning framework using normalizing flows to model continuous thoughts in LLMs, preserving autoregressive advantages and achieving better code generation performance with lower cost.