arXiv (All)
open access
GONE: Structural Knowledge Unlearning via Neighborhood-Expanded Distribution Shaping
Unlearning knowledge is a pressing and challenging task in Large Language Models (LLMs) because of their unprecedented capability to memorize and digest training data at scale, raising more significant issues regarding safety, privacy, and intellectual property. However, existing works, including pa
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
- You Can't Have It Both Ways: Concept Entanglement Limits Diffusion Model Unlearning#1119210
- Compliance vs. Sensibility: On the Reasoning Controllability in Large Language Models#974398
- Relation-Aware Graph Foundation Model#1129079
- SAMBAR: Selective Anchoring via Method of Multipliers for Balanced Knowledge Acquisition and Retention in Vision-Language-Action Models#1116439
- Trajectory Unlearning on LLM-based Agents#1118712
Record · ID 821807
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