Solution Overview
Four breakthrough research areas transforming AI from static tools into dynamic, learning partners
LLM Post-Training
Continuous fine-tuning without catastrophic forgetting
Models stay updated at lower cost
Revolutionary approach to updating large language models without losing previously learned capabilities. Our method enables continuous adaptation to new information while preserving core knowledge.
Memory Augmentation
Go beyond short context windows
Long-term codebook memory recall for enterprises
Advanced memory systems that extend AI capabilities far beyond traditional context limitations. Enables persistent knowledge storage and intelligent retrieval for enterprise applications.
Hallucination Repair
Structured model editing for AI reliability
Essential in fast-evolving fields: healthcare, law, finance
Precise correction mechanisms that eliminate AI hallucinations through structured knowledge editing. Critical for high-stakes applications where accuracy is non-negotiable.
Personalized AI
Learn individual preferences in real time
Your AI grows unique to you
Adaptive personalization systems that learn and evolve with individual users. Creates truly personalized AI experiences that improve continuously through interaction.
Research Work
Cutting-edge projects pushing the boundaries of continual learning in AI systems
October 2025
CleanEdit
Retention-Aware Pruning & Bounded Replay for lifelong model editing
Haoyuan Song, Haihua Luo, Ming Wang
arXiv
Self-maintaining lifelong editing: prune harmful edits with statistical tests, recycle supervision via bounded replay, and keep models stable over time.
2 October 2025
REPAIR
Robust Editing via Progressive Adaptive Intervention and Reintergration
Yisu Wang, Ming Wang, Haoyuan Song
arXiv
Closed-loop editing with dynamic memory pruning, distribution-aware optimization, and guarded knowledge fusion for reliable, scalable updates.
