A product-key parametric memory head with selective sparse updates mitigates catastrophic forgetting in generative retrieval models during sequential addition of new documents.
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7 Pith papers cite this work. Polarity classification is still indexing.
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A replay method for continual face forgery detection condenses real-fake distribution discrepancies into compact maps and synthesizes compatible samples from current real faces to reduce forgetting under tight memory budgets without storing historical images.
MRCKG combines a multimodal-structural curriculum, cross-modal preservation, and contrastive replay to let multimodal knowledge graphs learn new entities and relations over time without catastrophic forgetting.
Mujica-MyGo decomposes multi-turn RAG interactions via multi-agent workflows and applies minimalist policy gradient optimization to improve performance on QA benchmarks while avoiding long-context problems.
Trains a 42M-parameter Spanish cybersecurity LLM from scratch with curriculum phases and achieves 0.23 tool-selection accuracy after SFT mixture rebalancing to 1:21 tool-use ratio.
MF-CKGE separates temporal old and new knowledge into distinct embedding spaces with semantic decoupling and adaptive importance scoring to improve continual link prediction.
Face-D²CL fuses spatial and frequency features and uses dual continual learning to reduce forgetting while adapting to new DeepFakes, cutting average error rates by 60.7% and raising unseen-domain AUC by 7.9% over prior SOTA.
citing papers explorer
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A Parametric Memory Head for Continual Generative Retrieval
A product-key parametric memory head with selective sparse updates mitigates catastrophic forgetting in generative retrieval models during sequential addition of new documents.
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Direct Discrepancy Replay: Distribution-Discrepancy Condensation and Manifold-Consistent Replay for Continual Face Forgery Detection
A replay method for continual face forgery detection condenses real-fake distribution discrepancies into compact maps and synthesizes compatible samples from current real faces to reduce forgetting under tight memory budgets without storing historical images.
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When Modalities Remember: Continual Learning for Multimodal Knowledge Graphs
MRCKG combines a multimodal-structural curriculum, cross-modal preservation, and contrastive replay to let multimodal knowledge graphs learn new entities and relations over time without catastrophic forgetting.
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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning
Mujica-MyGo decomposes multi-turn RAG interactions via multi-agent workflows and applies minimalist policy gradient optimization to improve performance on QA benchmarks while avoiding long-context problems.
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VectraYX-Nano: A 42M-Parameter Spanish Cybersecurity Language Model with Curriculum Learning and Native Tool Use
Trains a 42M-parameter Spanish cybersecurity LLM from scratch with curriculum phases and achieves 0.23 tool-selection accuracy after SFT mixture rebalancing to 1:21 tool-use ratio.
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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.
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Face-D(^2)CL: Multi-Domain Synergistic Representation with Dual Continual Learning for Facial DeepFake Detection
Face-D²CL fuses spatial and frequency features and uses dual continual learning to reduce forgetting while adapting to new DeepFakes, cutting average error rates by 60.7% and raising unseen-domain AUC by 7.9% over prior SOTA.