Carbon, Code & Clusters: When AI Audits the Life Cycle of Itself
A mechanism-first reading of how lightweight LLMs, embeddings, and clustering can map the AI–LCA research landscape without pretending that literature review has been fully automated.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Intent Is the New API: When Agentic AI Runs the RAN
A mechanism-first reading of how LLM agents could translate telecom intents into coordinated O-RAN control, and why the hard part is not language but coupled optimization.
Mind the Gap: Why Agency Isn’t Intelligence (Yet)
A new information-theoretic framework argues that today’s AI systems can act and learn, but still lack the self-monitoring architecture required for intelligence.
Mirror, Mirror on the LLM: Teaching Models to Think About Their Thinking
A mechanism-first reading of Metacognitive Behavioral Tuning and why enterprise AI reliability depends on reasoning control, not just longer chains of thought.
Template Thinking: Why Your Next AI Agent Should Steal from Cognitive Science
A practical reading of how cognitive models and classic AI algorithms can serve as reusable templates for designing interpretable, task-fit language agents.
When Agents Ask for Help: Teaching LLMs the Art of Expert Collaboration
A mechanism-first reading of AHCE, a framework that teaches LLM agents when to escalate to human experts and how to turn messy advice into executable action.
From Lone LLMs to Living Systems: The Multi-Agent Orchestration Shift
A clearer business reading of why multi-agent AI is less about adding more chatbots and more about building governed operating systems for work.
Resampling Reality: When Your AI Needs to See the Same Thing Twice
A mechanism-first reading of invariant-transformation resampling: how structured inference views can reduce epistemic uncertainty without retraining the model.
Update or Revise? Turns Out It’s the Same Argument in a Better Suit
A formal belief-change result shows why AGM revision is best read as a stricter version of KM update, with the real gap hiding in how systems handle unsurprising information.
When Analysts Become Agents: Fine-Grained AI Teams That Actually Trade
A research-backed look at why LLM trading agents may depend less on agent count and more on how expert workflows are decomposed, routed, and validated.