When Prompts Hire Specialists: Why pMoE Changes Visual Adaptation Economics
A mechanism-first reading of pMoE, a visual prompt-tuning framework that lets frozen vision experts collaborate through dynamic prompt routing instead of full retraining.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Agents That Remember: When Context Stops Being a Liability
A practical reading of intrinsic memory agents, showing when structured memory improves multi-agent work and when it merely adds expensive notes.
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.
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.