A mechanism-first reading of FAQ, a data-centric post-training quantization method that uses larger in-family models to regenerate calibration data and reduce quantization damage.
A mechanism-first reading of SD-RAG and what it teaches businesses about building privacy-aware RAG systems that do not rely on the answering model to protect secrets it has already seen.
A mechanism-first reading of why machine learning does not remove human control from science, but quietly redistributes it across goals, metrics, and methodological tradeoffs.
XChoice shows why AI–human alignment in constrained decisions should be audited through hidden trade-off mechanisms, not just plausible-looking outputs.
A mechanism-first reading of LEG, a hybrid LLM-and-greedy optimization framework that lets qualitative advice influence facility planning without surrendering coverage guarantees.
AstroReason-Bench shows why agentic AI needs physics-aware simulators, structured planning workflows, and specialized optimizers before it can handle real operational planning.
A practical reading of the Probe and Solve Algorithm, a two-phase method for tuning constraint programming solvers under real time budgets.
BoxMind shows that applied AI becomes useful when perception, prediction, and intervention are joined into a closed operational loop.
A mechanism-first reading of Think-with-Me, a test-time intervention framework that turns LLM reasoning from uncontrolled token generation into a feedback-guided control loop.
A mechanism-first reading of why LLMs can predict process outcomes from tiny event logs, and why the advantage depends on semantics rather than spreadsheet magic.