arXiv (All)
open access
Knowledge Interaction in Hypernetwork-based Continual Learning: Conflict and Composition
Continual learning aims to adapt pretrained large language models (LLMs) to evolving data while preserving their pretrained capabilities. A central challenge is catastrophic forgetting: adaptation to new data can impair performance on previously learned knowledge. Since continual learning commonly c
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Functional Subnetwork Localization for Efficient Continual Learning in Large Language Models#1356873
- CLARE: Scalable Class-Incremental Continual Learning via a Sparsity-Based Framework#1123430
- CLARE: Scalable Class-Incremental Continual Learning via a Sparsity-Based Framework#919413
- CRAFT: Forgetting-Aware Intervention-Based Adaptation for Continual Learning#1355573
- SPARS: A Stability-Plasticity-Aware Region Selection Framework for Continual Learning#1354558
Record · ID 1326927
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive