When Circuits Go Atomic: Pruning Transformers One Neuron at a Time
A mechanism-first reading of multi-granular node pruning, and why the practical value is cheaper model diagnosis rather than magical model compression.
Cognaptus Insights
Industry thoughts, trends, and commentary.
You Know It When You See It—But Can the Model?
A business-focused reading of Agile Deliberation, a framework for turning vague subjective visual concepts into working VLM classifiers through structured human reflection.
Crowds, Codes, and Consensus: When AI Learns the Language of Science
A mechanism-first reading of MatSci-YAMZ, showing why AI-assisted vocabulary work is less about automated definitions and more about governed semantic negotiation.
Fault, Interrupted: How RIFT Reinvents Reliability for the LLM Hardware Era
RIFT shows how LLM accelerator reliability can move from broad random fault campaigns to targeted, workflow-ready diagnosis of the few faults that actually matter.
Graph Theory in Stereo: When Causality Meets Correlation in Categorical Space
A mechanism-first reading of how categorical semantics separates graph syntax from probabilistic semantics in Bayesian and Markov networks.
Path of Least Resistance: Why Realistic Constraints Break MAPF Optimism
A robotics planning paper shows why warehouse fleet performance depends less on abstract path optimality and more on realistic execution constraints, model fidelity, and planner scalability.
Teach Me Once: How One‑Shot LLM Guidance Reshapes Hierarchical Planning
A mechanism-first reading of SCOPE, a paper showing how LLM guidance can be moved from runtime planning into one-time subgoal initialization for cheaper hierarchical agents.
Vectors of Influence: When Beliefs Survive the Geometry of Minds
A cognitive-geometric paper reframes persuasion, leadership, marketing, and AI alignment as problems of whether meaning survives translation across different value spaces.
When the Machines Come Knocking: AI Agents vs Human Hackers in Live Penetration Tests
A live-enterprise penetration-testing study shows that AI security agents are becoming useful not because they are magically smarter than humans, but because scaffolding lets them work longer, wider, and cheaper under controlled conditions.
Agents on the Assembly Line: How Production-Grade AI Workflows Actually Get Built
A mechanism-first reading of why production-grade agentic AI is less about giving agents more freedom and more about engineering away the places where they should not guess.