A mechanism-first reading of Recursive Concept Evolution, a proposed way for frozen language models to add reusable concept subspaces instead of merely searching harder through tokens.
A mechanism-first reading of how primary causation can be formalized when discrete actions trigger continuous change.
A mechanism-first reading of WebClipper, showing how graph-based trajectory pruning can make deep research web agents cheaper, faster, and sometimes more accurate.
A mechanism-first reading of how implicit learning and lifted SOS inference can answer relational probabilistic queries from partial observations without constructing a full probabilistic model.
ReusStdFlow shows how enterprises can turn scattered agent workflows into reusable, retrieval-backed automation assets instead of asking LLMs to regenerate fragile workflow graphs from scratch.
A mechanism-first reading of X-SYS, showing why production explainability is less about choosing a saliency method and more about engineering responsive, traceable, adaptable, and scalable explanation systems.
A mechanism-first reading of MAC-AMP, a closed-loop multi-agent system that turns AI peer review into executable reward signals for antimicrobial peptide design.
A mechanism-first reading of a fuzzy optimal-control architecture for UAV take-off avoidance, and why its most important lesson may be a solver failure.
A mechanism-first reading of why completeness-first research agents need structured exploration, persistent candidate memory, validation, and multilingual search—not just longer browsing.
A mechanism-first reading of why high-quality social friction, not just bigger models or longer Chain-of-Thought, may become a core training lever for better AI agents.