Artificial intelligence has advanced through scale, data, and computation, yet remains bounded by its representational framework. We ask what makes intelligence open-ended. Every inferential system selects meaningful distinctions, limiting what it can learn. Within a fixed purpose, performance reaches a ceiling set by...
Simon Hegelich, Bardh Prenkaj, Habiba Sarhan et al.· 0 citations
Language models (LMs) often hallucinate by committing to confident answers rather than abstaining, even when they do not have enough information to answer reliably. A large body of existing work mitigates hallucination through detection or abstention mechanisms, but leaves open how models internally arrive at the decis...
Vy Nguyen, Zi-Qi Xu, Jeffrey Chan et al.· 0 citations
The work positions the work as a controlled reading of which parts of NCA-style computation carry the weight in language modelling, and examines a hierarchical variant of the Neural Cellular Automaton that cascades three stages with windows.
Avni Mittal, Avinash Anand, Ashutosh Kumar et al.· 0 citations
Twin Worlds (TW), a framework for improving reliability in knowledge-intensive reasoning through equivariance-based abstention, is proposed, which identifies when answers are not reliably grounded in the provided evidence and outperforms uncertainty- and sufficiency-based baselines.
Vy Nguyen, Zi-Qi Xu, Jeffrey Chan et al.· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
