Formalizes Reasoning Portability (RP) and proposes RDB-CL to modulate per-sample KL regularization in RLVR for MLLM continual learning, achieving +12.0% Last accuracy over vanilla RLVR baseline by preserving reusable reasoning on high-RP samples.
Continual multi- modal knowledge graph construction
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A taxonomy survey of continual learning for vision-language models, grouping methods into multi-modal replay, cross-modal regularization, and parameter-efficient adaptation, with a review of benchmarks and metrics.
MF-CKGE separates temporal old and new knowledge into distinct embedding spaces with semantic decoupling and adaptive importance scoring to improve continual link prediction.
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Reasoning Portability: Guiding Continual Learning for MLLMs in the RLVR Era
Formalizes Reasoning Portability (RP) and proposes RDB-CL to modulate per-sample KL regularization in RLVR for MLLM continual learning, achieving +12.0% Last accuracy over vanilla RLVR baseline by preserving reusable reasoning on high-RP samples.
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Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting
A taxonomy survey of continual learning for vision-language models, grouping methods into multi-modal replay, cross-modal regularization, and parameter-efficient adaptation, with a review of benchmarks and metrics.
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Multi-Faceted Continual Knowledge Graph Embedding for Semantic-Aware Link Prediction
MF-CKGE separates temporal old and new knowledge into distinct embedding spaces with semantic decoupling and adaptive importance scoring to improve continual link prediction.