RAGA’s real lesson is not that knowledge graphs magically beat vector RAG, but that enterprise retrieval needs provenance, lifecycle control, and repairable consistency.
A practical reading of evidence tracing and execution provenance as the infrastructure layer that turns opaque AI-agent activity into auditable, controllable business systems.
FLUXtrapolation shows why AI models for sparse environmental systems need deployment-shaped stress tests, not comforting average-error leaderboards.
A mechanism-first reading of how quantum rare-event sampling changes the cost of finding tail scenarios, and why the advantage depends on aggregate rare-tail mass rather than black-swan theatre.
A practical reading of three arXiv papers showing why AI value depends on evaluation, routing, and context-aware interpretation rather than blind faith in a single large model.
A practical reading of two new agent papers showing why enterprise AI should be judged by observable behaviour and runtime contracts, not human-like performance theatre.
A mechanism-first reading of a factorial molecular GNN benchmark showing why message construction deserves more attention than architectural nameplates.
A small trajectory encoder nearly matches a frontier LLM judge on reward-hack detection, but only when it can read the reasoning-rich trace.
A practical reading of two arXiv papers showing why AI works best in high-stakes evaluation when it is anchored to human evidence and audited for real engagement.
A mechanism-first reading of AI-Paper-Review shows why AI review is useful as a pre-submission quality gate, not as a substitute for human peer review.