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.
A mechanism-first reading of how chain-of-thought traces change the probability of correct answers, and why longer reasoning is not the same thing as better reasoning.
A close reading of why reasoning models are more resistant to multi-turn pressure, why they still flip, and why confidence-based defenses may fail when models become too confident in their own reasoning.
BrowseComp-V3 shows that multimodal browsing agents do not mainly fail because they lack search tools; they fail because they cannot yet integrate visual and textual evidence reliably across long web trajectories.
A mechanism-first reading of how causation should be assigned when discrete actions trigger continuous change.
A paper on behavioral consistency shows why repeated agent trajectories can become an early warning signal for enterprise AI reliability.