KAIST group pushes knowledge-intelligence separation to fix LLM trustworthiness
coallaoh · x · 2026-08-18
The author argues that the innate capabilities and fundamental limitations of parametric models need far more research, along with better human-in-the-loop interfaces — noting that the human/AI division of labor should shift by domain, since a coding agent and a clinical assistant have very different costs of a wrong answer.
Their Scalable Trustworthy AI group (moved to KAIST AI in Feb 2026) proposes "Knowledge-Intelligence Separation": analogous to the code-data separation of the 1960s, store knowledge in interpretable, editable external modules while keeping reasoning in the model. This enables faster customization, training data attribution by design, and knowledge editing/unlearning — addressing LLM hallucination, continual-learning failures, impractical GDPR compliance, and parameter-level privacy leaks. Research spans RAG and vector databases, memory-augmented architectures, multimodality, human-AI interaction, expert-in-the-loop systems, agentic AI, and privacy/security.
Related event: Human-AI division of labor should vary by domain, researchers argue(2 posts)→
More from Research
- NeurIPS Competition SimulacraBench: AI Survey Prediction — soumitrashukla9 · 2026-08-19
- AI for Science: Estimating Bacteria Size with Code — johnowhitaker · 2026-08-19
- UAI 2026 paper: identifiability metrics show systematic false positives in interpretability evals — RexDouglass · 2026-08-19
- GenBio AI releases AIDO Cell preview: one world model simulating the whole human cell — AllThingsApx · 2026-08-19
- Google Launches 'Blue Skies' to Use AI for Contrail Avoidance — ymatias · 2026-08-19
- Stanford study: X's For You algorithm mistakes outrage for interest, drifting from what users value — StanfordHAI · 2026-08-19