DRAPE generates query-image conditioned prompts on the fly for multimodal continual instruction tuning and reports SOTA results on MCIT benchmarks.
Dynamic mixture of curriculum lora experts for continual multimodal instruction tuning
7 Pith papers cite this work. Polarity classification is still indexing.
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ProtoAda uses format-aware prototypes for better task routing and geometry-aware consolidation to reduce interference in multimodal continual instruction tuning.
PEAM is a parametric memory framework for Minecraft agents that internalizes experiences into a multimodal MoE-LoRA module using contrastive objectives on failures and a scale-free self-triggered consolidation mechanism.
Hystar adapts CLIP-like models to unseen query styles by generating per-input singular-value perturbations with a hypernetwork for attention layers and a new StyleNCE contrastive loss.
CRAM uses adaptive MoE with centroid routing and orthogonality constraints to enable parameter-efficient multimodal continual instruction tuning while mitigating forgetting.
CRAFT is a continual learning method for LLMs that learns low-rank interventions on hidden representations, using a unified KL-divergence objective to handle task routing by output divergence, forgetting control via prior-state regularization, and intervention merging.
Proposes LoRA-based mixture-of-experts with autoencoder routing for continual bidirectional motion-language learning, reporting near-zero forgetting on a 5-task HumanML3D benchmark derived via semantic clustering.
citing papers explorer
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Dynamic Cross-Modal Prompt Generation for Multimodal Continual Instruction Tuning
DRAPE generates query-image conditioned prompts on the fly for multimodal continual instruction tuning and reports SOTA results on MCIT benchmarks.
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ProtoAda: Prototype-Guided Adaptive Adapter Expansion and Geometric Consolidation for Multimodal Continual Instruction Tuning
ProtoAda uses format-aware prototypes for better task routing and geometry-aware consolidation to reduce interference in multimodal continual instruction tuning.
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PEAM: Parametric Embodied Agent Memory through Contrastive Internalization of Experience in Minecraft
PEAM is a parametric memory framework for Minecraft agents that internalizes experiences into a multimodal MoE-LoRA module using contrastive objectives on failures and a scale-free self-triggered consolidation mechanism.
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Hystar: Hypernetwork-driven Style-adaptive Retrieval via Dynamic SVD Modulation
Hystar adapts CLIP-like models to unseen query styles by generating per-input singular-value perturbations with a hypernetwork for attention layers and a new StyleNCE contrastive loss.
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CRAM: Centroid-Routing and Adaptive MoE for Multimodal Continual Instruction Tuning
CRAM uses adaptive MoE with centroid routing and orthogonality constraints to enable parameter-efficient multimodal continual instruction tuning while mitigating forgetting.
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CRAFT: Forgetting-Aware Intervention-Based Adaptation for Continual Learning
CRAFT is a continual learning method for LLMs that learns low-rank interventions on hidden representations, using a unified KL-divergence objective to handle task routing by output divergence, forgetting control via prior-state regularization, and intervention merging.
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Towards Continual Motion-Language Agents: LoRA Variants for Incremental Motion Understanding and Generation
Proposes LoRA-based mixture-of-experts with autoencoder routing for continual bidirectional motion-language learning, reporting near-zero forgetting on a 5-task HumanML3D benchmark derived via semantic clustering.