Darwin, But Make It Neural: When Networks Learn to Mutate Themselves
A mechanism-first reading of Self-Referential Graph HyperNetworks, and why their real business lesson is adaptive exploration rather than magical self-improving AI.
A mechanism-first reading of Self-Referential Graph HyperNetworks, and why their real business lesson is adaptive exploration rather than magical self-improving AI.
A financial AI fairness study shows why testing individual LLM agents is not enough when their collaboration can create new system-level bias.
A mechanism-first reading of E-SDS, a framework that makes automated reward generation environment-aware for humanoid locomotion.
A CNN–HOSVD leukemia classifier shows why the practical value of medical AI depends less on headline accuracy than on where automation enters the diagnostic workflow.
A practical reading of AI epidemiology: governing deployed AI by measuring expert-AI interactions instead of pretending every black box can be opened on schedule.
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.