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Modality-Decoupled Online Recursive Editing
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Proposes M-ORE, a modality-decoupled online recursive editor for MLLMs that keeps separate locality statistics for the text stack and visual projector and performs Sherman-Morrison updates in a frozen orthogonal low-rank write space, achieving constant per-edit overhead.
Problem
Online model editing of multimodal LLMs under a stream of corrections suffers from cross-modal conflict (visual activations dominate update statistics) and inter-edit interference (sequential edits entangle in a shared subspace), while existing editors either drift or incur edit-growing cost.
Approach
Derives a proximal-projection update with a locality quadratic form, restricts writes to a frozen orthonormal low-rank basis, keeps separate per-module (text vs. visual projector) steady-space preconditioners updated by Sherman-Morrison recursion, giving O(1) per-edit cost.
Key results
- On E-IC with LLaVA-v1.5 at 100 edits, M-ORE reaches 90.73 average vs 83.66 for LiveEdit.
- On BLIP2-OPT E-IC at 100 edits, reliability improves from 74.63 (LiveEdit) to 83.14 for M-ORE.
- Removing the frozen orthogonal basis drops LLaVA E-IC average by 18.10 points at 100 edits.
Surprising findings
- Removing masked pooling slightly improves the average score (+1.70 at 100 edits on E-IC), suggesting the rank-one sketch is not harmful.
- Editing the vision encoder and projector alone preserves text locality but lowers reliability and generality.
Limitations
- Cross-modal covariance is approximated with diagonal variance and layer-wise energy proxies.
- Some locality flips occur near fragile decoding boundaries and with visually similar multimodal inputs.
Experimental setup
Datasets: E-VQA E-IC NQ OK-VQA MME
Benchmarks: E-VQA E-IC MME
Metrics: reliability generality locality average score edit latency peak memory
Baselines: FT-L FT-M MEND AlphaEdit SERAC IKE LiveEdit
Builds on: ROME MEMIT AlphaEdit LiveEdit MEND
Closest papers by meaning
- Rethinking Residual Distribution in Locate-then-Edit Model Editing (NeurIPS 2025 · similarity 0.88)
- Towards Lifelong Model Editing via Simulating Ideal Editor (ICML 2025 · similarity 0.88)
- MEMOIR: Lifelong Model Editing with Minimal Overwrite and Informed Retention for LLMs (NeurIPS 2025 · similarity 0.88)
- Reinforced Lifelong Editing for Language Models (ICML 2025 · similarity 0.87)
- Lifelong Knowledge Editing for Vision Language Models with Low-Rank Mixture-of-Experts (CVPR 2025 · similarity 0.87)
- BalancEdit: Dynamically Balancing the Generality-Locality Trade-off in Multi-modal Model Editing (ICML 2025 · similarity 0.87)
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