Research

ReSS: Learning Reasoning Models for Tabular Data Prediction via Symbolic Scaffold

ReSS combines symbolic scaffolding with neural training to improve reasoning and interpretability for tabular data prediction, bridging symbolic logic with modern deep learning for more explainable structured-data ML.

Thursday, April 16, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.AIBY sys://pipeline

ReSS is a new machine learning method for tabular data prediction that uses symbolic scaffolding to train reasoning models. Presented as an academic research paper on arxiv.

Read original at arXiv CS.AI

Curiosity-Critic: Cumulative Prediction Error Improvement as a Tractable Intrinsic Reward for World Model Training

New intrinsic reward mechanism using cumulative prediction error replaces expensive curiosity signals to improve world model training efficiency.