Cognaptus Insights

A new embodied-agent study shows why collaborative AI fails when the informed agent gives more instructions instead of helping the limited agent verify what it can actually perceive.

Aṇubuddhi shows how conversational agents can speed up quantum optics experiment design—but also why simulation alignment is not the same thing as numerical truth.

A mechanism-first reading of MobiMem, a memory-centric agent system that improves personalization, capability, and latency without continually retraining the model.

A mechanism-first reading of Prompt-to-Parts, where language models become useful for physical design not by imagining perfect 3D objects, but by compiling intent into constrained, inspectable part assemblies.

A mechanism-first reading of state-augmented disassembly graphs and why circular-economy triage is a sequential decision problem, not a green ranking exercise.

A mechanism-first reading of proportional duty: why uncertainty should shift responsibility toward verification instead of becoming an excuse for inaction.

A mechanism-first reading of GAR, an adversarial reinforcement learning framework that teaches LLMs through slice-level criticism rather than final-answer applause.

A mechanism-first reading of patchwork AGI: why collective agent systems may become the real control surface for safety, governance, and enterprise deployment.

CitySeeker shows why urban AI agents fail not because they cannot see streets, but because they cannot reliably translate vague human needs into grounded city actions.

A case-first reading of a hybrid ML and RAG-LLM framework for forecasting lung-cancer pain episodes before the ward has to react.