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Model Merging on Loss Landscape: A Geometry Perspective

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arXiv:2605. 26693v1 Announce Type: cross Abstract: Model merging offers a promising avenue for knowledge integration and parallel development without retraining.

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Authors: Juanwu Lu, Anand Bhaskar, Brian Axelrod, Ekaterina Tolstaya, Tristan Emrich
Released: 2026-05-27 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Model Merging on Loss Landscape: A Geometry Perspective overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)". Matching terms: class, line, without, full, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.26693

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