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NeurIPS 2026Mercuria Energy Trading SAU Modena and Reggio EmiliaU Modena and Reggio Emilia, ItalModel merging

Rethinking Layer-wise Model Merging through Chain of Merges

Chain of Merges sequentially merges layer weights while updating activation statistics to reduce internal covariate shift and surpass existing model merging benchmarks.

Pietro Buzzega, Riccardo Salami, Angelo Porrello, SIMONE CALDERARA

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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lenient 5/5
medium 8/10
strict 0/5
83%Must read
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NeurIPS 2026SpotlightHong Kong PolytechnicDepartment of Computer Science aZhejiangU Technology SydneyThe Hong Kong PolytechnicModel merging

FeatCal: Feature Calibration for Post-Merging Models

FeatCal reduces post-merging feature drift via layer-wise closed-form weight calibration without gradients, outperforming Surgery and ProbSurgery on CLIP and GLUE benchmarks.

Yanggan Gu, Shuo CAI, Zihao Wang, Wenjun Wang and 6 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 13 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 2/5