Small Moves, Big Models: The Quiet Discipline of Bounded AI
Two papers show why AI creates value in structured systems when it is scoped as a precise intervention, not promoted into an all-purpose replacement.
Cognaptus Insights
Industry thoughts, trends, and commentary.
The Rule Is the Model: DEM’s Case for Bedside Anomaly Detection Without Explainer Theatre
A mechanism-first reading of DEM, a glass-box anomaly detector that turns residual distillation into an operational governance dial for physiological monitoring.
The Solver Isn’t the Strategy: FrontierOR’s Reality Check for AI Optimisation Agents
FrontierOR shows why runnable optimisation code is not the same as scalable algorithm design, and why enterprise AI agents need harder tests than solver demos.
Kitchen Confidential: FoodMonitor and the Compliance AI Reality Check
FoodMonitor shows why real compliance AI needs auditable evidence, not just video understanding with a rulebook attached.
Memory Foam: When AI Stops Storing Everything and Starts Learning From It
A business-focused reading of two new arXiv papers showing why long-horizon AI needs grounded abstraction, validated experience, and selective internalisation rather than ever-larger memory stores.
Mind the Readout: Why AI Gets Smarter When We Stop Worshipping the Output
A business-focused synthesis of three arXiv papers showing why AI reliability depends on representation, readout, and compute discipline—not just bigger outputs or heavier architectures.
Mixed Feelings: When LLM Batching Stops Being Obviously Better
A systems paper shows why mixed batching is not a universal default for LLM inference, and why bandwidth-aware scheduling may matter more than scheduler fashion.
Relight at Your Own Risk: WildRelight and the Synthetic Vision Reality Check
WildRelight shows why real-world relighting needs measurement infrastructure, not just prettier synthetic demos.
Same Causal Effect, Different Bill: Derivation Graphs and the Estimand Trap
A mechanism-first reading of derivation graphs, showing why equivalent do-calculus expressions can lead to very different estimators, costs, and operational decisions.
Stale Gradients, Fresh Economics: CoCD’s Lightweight Route to Zeroth-Order AI
Coherent Coordinate Descent turns stale finite-difference gradients into a practical mechanism for lighter zeroth-order optimisation, with clear promise and equally clear scale boundaries.