aiBACKGROUND
Backdoor Containment via Expert Quarantine and Shutdown in LLMs
BACKGROUND
1 SIGNALFIRST DETECTED 2 October 2026
Narrative synthesis pending — 1 signal collected.
How the story developed
Other feeds and industry voices
5 May
BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs
arXiv:2605.00422v1 Announce Type: cross Abstract: Large language models (LLMs) have driven major progress in NLP, yet their substantial memory and compute demands still hinder
REPORT · WHAT · ArXiv CS.AIOPEN STORY
2 Apr
Self-Routing: Parameter-Free Expert Routing from Hidden States
arXiv:2604.00421v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) layers increase model capacity by activating only a small subset of experts per token, and typically rely
REPORT · HOW · ArXiv CS.AIOPEN STORY
3 Jul
DRL-CLBA: A Clean Label Backdoor Attack for Speech Classification via DDPG Reinforcement Learning
arXiv:2607.01729v1 Announce Type: new Abstract: Deep learning models for speech classification are vulnerable to backdoor attacks, where malicious triggers cause misclassification
REPORT · WHERE · ArXiv CS.AIOPEN STORY
Sources
ArXiv CS.AI
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