A mechanism-first reading of SMB-Structure, a clinical EHR world-modeling approach that shows why predicting patient trajectories is not the same as reconstructing medical records.
Agent-RRM shows why the next useful reward model for agents may need to diagnose bad reasoning, not merely score final answers.
A mechanism-first reading of WoW-bench, showing why enterprise agents fail when they cannot model hidden workflow dynamics.
Attention-MoA shows why multi-agent LLM systems need structured critique, residual memory, and adaptive depth—not just more model calls.
A mechanism-first reading of BPIM, a YOLOv5-based framework that improves aerial small-object detection by preserving boundary, position, and cross-scale cues.
A mechanism-first reading of PRISM, a lightweight generative recommender that treats semantic IDs as fragile business infrastructure rather than decorative tokens.
A practical reading of modern LLM safety research, showing why alignment should be treated as an operational control system rather than a one-time model property.
CORD shows that audio-language models may fail not because they cannot hear, but because their audio-conditioned reasoning drifts away from their own text pathway.
A mechanism-first reading of how process-tensor diagnostics turn SGD memory from training folklore into something measurable, testable, and operationally useful.
A new Finnish railway-delay dataset shows that predictive rail AI begins with spatial-temporal data engineering, not with a glamorous model leaderboard.