A business reading of Governed Memory, showing why multi-agent AI needs shared memory, policy routing, schema feedback, and entity isolation—not just another RAG store.
MALLES shows why useful AI economic agents need transaction alignment, numerical sensitivity, and population calibration—not just better role-play prompts.
A mechanism-first reading of RPMS, showing why reliable LLM agents need executable rules, state-aware memory, and conflict arbitration—not larger memory alone.
Why demand forecasts should be evaluated by the inventory decisions they trigger, not only by the errors they minimize.
A business-focused reading of why cultural alignment in LLM systems should be measured, compared, and optimized rather than handled as a one-line localization prompt.
A close reading of LEAFE, a reflective-experience training framework that shifts AI agents from blind retry loops toward internalized recovery behavior.
How SurgΣ turns fragmented surgical videos, labels, and reasoning traces into a reusable data infrastructure for surgical foundation models.
SocialOmni shows why audio-visual AI needs to be tested not only for what it understands, but for who it tracks, when it enters, and how it responds.
A mechanism-first reading of how inverse specification rewards train slide-generation agents to preserve intent, not merely produce prettier decks.
A close look at why conformal factuality can make RAG systems statistically safer while making their answers less useful, less robust, and more expensive unless teams measure the right things.