Tag
This paper presents a multi-agent reasoning framework where multiple foundation models collaborate through structured critique and aggregation, demonstrating that model heterogeneity significantly improves step-wise reasoning accuracy and reduces variance across domains.
This paper investigates how domain adaptation reshapes explanatory behavior in language models by training on a pre-Copernican corpus, finding that fine-tuning shifts explanatory framing more than cosmological stance.