probabilistic

Obie Fernandez announces full documentation for the 'feelings' Ruby gem, which provides probabilistic conditionals for adding decision models to code, inspired by LLM workflows.

TypeSafe has launched Jev, the first System One Model, a frontier AI model that outputs typed probabilistic decisions instead of prose.

The author revised the success criterion for coding-agent evaluations by moving from requiring three perfect 88/88 runs to a criterion where each case must pass at least 2 of 3 runs, reducing randomness impact and improving stability.

The paper presents a biologically grounded framework where noisy neural and synaptic dynamics enable probabilistic inference and learning via stochastic sampling, using analogue in-memory computing hardware for scalable and energy-efficient computation.

This paper proposes a probabilistic approach to training-data extraction from black-box language models, showing that aggregate membership-inference metrics hide per-document leakage and introducing the 'leakit' audit tool.

Presents MBDiff, a multi-view behavior-aware diffusion model for probabilistic utility data imputation that learns user behavior from global, local, and instance-level views and uses a conditional attentional denoising network. Evaluated on real utility data from Florida, it outperforms state-of-the-art baselines.

This paper presents a hybrid probabilistic forecasting system that integrates time series decomposition (Prophet) with NLP techniques applied to Bolivian news coverage to predict roadblocks, achieving improved AUC-ROC and Brier Score over purely statistical models.

This paper introduces the Probabilistic Concept-Aware Steering (PCS) framework for LLM inference, which uses concept-driven steering vector retrieval and probabilistic strength calibration to improve interpretability, optimality, and generalizability, achieving over 30% higher direction accuracy and over 89% steering accuracy on multiple datasets.

This paper proposes a probabilistic extension to neuro-symbolic AGI robots using Belnap's typed intensional first-order logic. It introduces global and local symmetry transformations to preserve knowledge and enable real-time decisions, with neural networks computing probability density based on maximum information entropy.

This paper introduces BlendIn, an inference-time alignment framework that uses probabilistic model blending to assess guidance reliability and proportionally weight model contributions, achieving up to 50% performance improvement by avoiding harmful interventions.

The IMF published a formal note introducing the concept of agentic payments, framing the tension between probabilistic AI systems and deterministic payment infrastructure, and defining the shift from 'click to pay' to 'decide to pay'.

Njord is a probabilistic graph neural network for ensemble ocean forecasting that provides uncertainty estimates and achieves state-of-the-art performance on global and regional benchmarks, improving surface temperature prediction.