When 100% Sensitivity Isn’t Safety: How LLMs Fail in Real Clinical Work
A real-world NHS medication-safety evaluation shows why detecting risk is not the same as knowing what safe action requires.
Cognaptus Insights
Industry thoughts, trends, and commentary.
When More Explanation Hurts: The Early‑Stopping Paradox of Agentic XAI
A rice-yield case study shows why agentic explanations improve early, peak quickly, and then decay into verbose, weakly grounded advice.
Agents All the Way Down: When Science Becomes Executable
Why Bohrium+SciMaster argues that agentic science scales through infrastructure, execution traces, validation gates, and reusable workflows—not one heroic AI Scientist.
Teaching Has a Poker Face: Why Teacher Emotion Needs Its Own AI
A mechanism-first reading of T-MED and AAM-TSA, showing why teacher emotion recognition needs domain-specific multimodal design rather than generic sentiment analysis.
Think Before You Beam: When AI Learns to Plan Like a Physicist
A comparison-based look at why reasoning agents may matter less as replacements for radiotherapy planners than as auditable planning partners.
When 1B Beats 200B: DeepSeek’s Quiet Coup in Clinical AI
A clinical-AI paper shows why workflow evidence, local deployment, and domain tuning matter more than raw model size in chest X-ray reporting.
When Bigger Isn’t Smarter: Stress‑Testing LLMs in the ICU
A clinical-AI benchmark shows why hospitals should compare large language models against smaller baselines before assuming that scale buys better prediction.
When One Clip Isn’t Enough: Teaching LLMs to Watch Long Videos Like Adults
LongVideoAgent shows why long-video AI needs selective grounding and targeted perception, not just bigger context windows.
When Sketches Start Running: Generative Digital Twins Come Alive
A mechanism-first reading of how vision-language models can turn factory sketches and prompts into executable FlexSim digital twins, and where the promise still stops.
Don’t Forget How to Feel: Teaching Motion Models Empathy Without Amnesia
A mechanism-first reading of L2-EMG and ES-MoE, showing why emotional motion generation needs continual adaptation rather than just better emotion labels.