Software-Defined Stochastic Inference Engine (SDSIE) Initial public release and technical research specification for the Software-Defined Stochastic Inference Engine (SDSIE). Overview This specification outlines an adaptive virtual runtime architecture designed to execute generative AI workloads with high energy efficiency on conventional silicon by dynamically modulating precision, temporal execution, and memory states. Included Assets Full Research Whitepaper (PDF)
The exponential growth of heterogeneous digital information across structured and unstructured repositories presents a critical challenge for large language models (LLMs): the inability to access and reason over dynamically evolving knowledge without costly model retraining. This…
Final reproducible workflow for the manuscript "Prediction of Microemulsion Phase Classes from Formulation Variables Using Machine Learning and Feature Importance". This release contains the raw dataset, cleaned dataset, data dictionary, Python workflow, package requirements, gen…
Large language models frequently possess the knowledge needed to answer a question correctly yet commit to the wrong response. This paper presents friction-guided inference, a calibrated inference-time pipeline that uses the model's own logprob distribution — available at zero co…
Deep neural networks have achieved substantial success in image, text, and signal analysis, but their advantage is less consistent for heterogeneous tabular data, where tree-based ensemble methods often remain strong baselines. This study proposes CANON (Cross-Attention Neuro-sym…
🔗 Reproducible code: github.com/jpbronsard/syntonic-portfolio v 3.0 V2.0 measured one channel of financial adaptation: the return channel, where \(\tau^\star=1/\sqrt{2}\) is the structural signature of the random-walk limit. Markets have a second channel with the same structure…