Optimizing LLMs for parametric CoT faithfulness improves both paradigms consistently while contextual optimization yields more variable gains, and different contextual metrics do not transfer reliably to each other.
Model merging in the era of large language models: Methods, applications, and future directions
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
HeteroFusion fuses heterogeneous LLMs via topology-based alignment and conflict-aware denoising, outperforming merging and ensemble baselines in cross-family and multi-source settings.
citing papers explorer
-
Investigating the Interplay between Contextual and Parametric Chain-of-Thought Faithfulness under Optimization
Optimizing LLMs for parametric CoT faithfulness improves both paradigms consistently while contextual optimization yields more variable gains, and different contextual metrics do not transfer reliably to each other.
-
Can Heterogeneous Language Models Be Fused?
HeteroFusion fuses heterogeneous LLMs via topology-based alignment and conflict-aware denoising, outperforming merging and ensemble baselines in cross-family and multi-source settings.