LoMo is a lightweight data curation technique that locally substitutes text with images in prompts to enforce cross-modal invariance, yielding 2.67-2.82 point gains over standard SFT on two VLMs across 13 benchmarks.
Deciphering cross-modal alignment in large vision-language models with modality integration rate
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2026 2verdicts
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SEDualVLN introduces a spatially-enhanced dual-system VLN architecture that achieves state-of-the-art results on VLN-CE benchmarks through coordinated VLM action generation and MLLM waypoint planning.
citing papers explorer
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LoMo: Local Modality Substitution for Deeper Vision-Language Fusion
LoMo is a lightweight data curation technique that locally substitutes text with images in prompts to enforce cross-modal invariance, yielding 2.67-2.82 point gains over standard SFT on two VLMs across 13 benchmarks.
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SEDualVLN: A Spatially-Enhanced Dual-System for Vision-Language Navigation
SEDualVLN introduces a spatially-enhanced dual-system VLN architecture that achieves state-of-the-art results on VLN-CE benchmarks through coordinated VLM action generation and MLLM waypoint planning.