Teaching Reinforcement Learning to Think Before It Acts
A mechanism-first reading of H2RL, a neuro-symbolic reinforcement learning framework that uses logic as training scaffolding rather than inference-time baggage.
Cognaptus Insights
Industry thoughts, trends, and commentary.
When the Streets Flood, Let the AI Drive: Reinforcement Learning for Climate‑Resilient Cities
A case-first reading of how reinforcement learning can turn long-term flood adaptation from a fixed infrastructure plan into a staged, testable capital-allocation strategy.
Your AI’s Memory Palace: Why Personal Assistants Need a Knowledge Graph
EpisTwin shows why serious personal AI may need explicit knowledge graphs, not just longer context windows or better vector search.
Caught on Skeleton: How Pose-Based AI Is Teaching Retail Cameras to Adapt
A mechanism-first look at how pose-based shoplifting detection moves from static video anomaly benchmarks toward periodically adapting retail IoT systems.
Don’t Just Answer — Ask: Why Interactive Benchmarks May Redefine AI Intelligence
A mechanism-first reading of Interactive Benchmarks, showing why the next useful AI evaluation may measure how models acquire information, not just how confidently they answer.
Mind the Units: Why LLMs Still Can't Count (And How CONE Fixes It)
CONE shows why numerical AI failures are often embedding failures: numbers need magnitude, units, and attribute context before retrieval or reasoning can become reliable.
Strings Attached: When AI Starts Solving Physics
A mechanism-first reading of how Gemini Deep Think, Tree Search, executable verification, and human review turned a difficult cosmic-string integral into a case study for credible AI-assisted discovery.
The AI That Remembers Itself: Why Memory May Be the Real Operating System of Agents
A mechanism-first reading of why persistent AI agents may need governed memory infrastructure, not just better retrieval.
When Models Get Sick: The Rise of AI Medicine
A case-first reading of Model Medicine, a proposed clinical framework for diagnosing AI systems whose failures emerge from weights, prompts, memory, tools, and time.
When Your AI Teammate Starts Freelancing: Rethinking Human–Agent Alignment
A mechanism-first reading of why agentic AI makes human–AI alignment a moving governance problem, not a one-time agreement on goals or outputs.