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Updated through the day. Every headline links straight to the source. The two lines underneath are ours.
Model merging at the representation level is an underexplored angle compared to parameter-space methods, and Wasserstein alignment is mathematically grounded. If you're building systems that need to blend expertise across domains without interference, this is worth reading. Otherwise it's solid research on an emerging problem.
Model merging is a real cost driver for multi-task systems, and this fixes a concrete problem: merged decoders suffer cumulative representation drift. The position-aware correction is practical. If you're merging specialized LLMs into one model, this approach will improve quality. Builders shipping multi-task systems should test it.