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AI & Enterprise

KAIST develops tech to boost safety of custom AI while preserving performance

KAIST said a research team developed a learning framework designed to improve the safety of customised AI trained on personal or corporate documents while keeping task performance. The Buffer-and-Reinforce approach applies a temporary buffering module during fine-tuning to block malicious data from affecting the core model, then removes it and adds a safety reinforcement module using QR decomposition. In tests, the share of dangerous answers fell to about 8 percent from about 18 percent.

AI & Enterprise

Team Naver showcases AI full-stack technology at ICML 2026

Team Naver said on Sunday it took part in the International Conference on Machine Learning (ICML) 2026, presenting research results ranging from advanced AI models to physical AI. The event was held at Seoul\'s COEX from July 6 to 11. It showcased work across AI safety, model and agent operations, and 3D spatial understanding. Highlights included red-teaming using Stable-GFlowNet, a model-merging method called SyMerge, and a Seoul World Model developed jointly with partners.

AI & Enterprise

Dinotisia unveils KV cache compression technology to ease AI computing bottlenecks

AI infrastructure company Dinotisia said on Wednesday it has released a paper and source code for KV cache compression technology that stores previously read context on GPUs to avoid recalculation by large language models. The company said its STAR-KV technology, a joint effort involving UC San Diego’s VVIP Lab and Dinotisia researchers, was selected as an ICML 2026 Spotlight paper. It said tests showed up to 75 percent reduction and up to 20-fold compression.