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.
Cognaptus Insights
Industry thoughts, trends, and commentary.
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.
Bench to the Future: Why E-commerce Is the Real Final Boss for Foundation Agents
A business-focused reading of EcomBench, showing why practical e-commerce tasks expose the gap between impressive agent demos and deployable operational reliability.
It Takes a Village (of Models): Why Multi-Agent Intelligence Won't Emerge by Accident
A close reading of why stronger single-agent foundation models do not automatically become reliable collaborators, coordinators, or multi-agent planners.
LoRA, But Make It Legible: How CARLoS Turns Chaos into Retrieval Signal
A mechanism-first reading of CARLoS, a framework that turns visual LoRA behavior into searchable, governable infrastructure.