Personas, Panels, and the Illusion of Free A/B Tests
A practical reading of when LLM persona panels can replace field experiments for method benchmarking—and when they merely create cheaper noise.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Reading the Room? Apparently Not: When LLMs Miss Intent
A case-first reading of a paper showing why LLM safety fails when models respond to surface wording while missing the user's likely intent.
RoboSafe: When Robots Need a Conscience (That Actually Runs)
A mechanism-first reading of RoboSafe, a runtime safety guardrail that turns embodied-agent safety from vague refusals into executable checks over context and time.
Traffic, but Make It Agentic: When Simulators Learn to Think
A mechanism-first reading of TrafficSimAgent, showing why agentic traffic simulation is less about chatting with SUMO and more about turning simulation workflows into controllable, memory-aware optimization systems.
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.
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.