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Models that adapt to a stream of tasks without forgetting prior capabilities still struggle to isolate updates between different LoRA experts. PASs-MoE creates separate pathway activation subspaces for each expert, which helps mitigate misaligned co-drift. If your MLLM pipeline relies on continual instruction tuning, this could be a game-changer.
“arXiv:2601.13020v2 Announce Type: replace-cross Abstract: Continual instruction tuning (CIT) requires multimodal large language models (MLLMs) to adapt to a stream of tasks without forgetting prior capabilities. A common strategy is to isol…”
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ACTIVE
CATEGORY
Models
EVIDENCE
Not yet assessed
ENTITY
PASs-MoE, LoRA, MLLMs
DECISION
Automated · no editorial override
LAST OBSERVED
Aug 3, 2026