A mechanism-first reading of STELLA, a time-series forecasting framework that gives LLMs structured semantic guidance instead of asking them to hallucinate order from raw numbers.
ACE shows why consumer AI reliability depends less on fluent answers and more on hurdle checks, grounding discipline, and workflow-level evaluation.
A mechanism-first analysis of how adaptive visual prompt injection turns ordinary image resizing into a security boundary for multimodal AI systems.
A mechanism-first reading of TDKPS, a statistical framework for detecting behavioral drift in black-box multi-agent systems without pretending it can explain every cause.
A mechanism-first reading of Algorithmic Thinking Theory and what it implies for designing enterprise AI workflows beyond best-of-k prompting.
A case-first reading of how penalty-aware policy reasoning lets autonomous agents distinguish acceptable emergency exceptions from dangerous rule-breaking.
RoCo shows how role-specialized LLM agents can improve automatic heuristic design—but its business value lies in disciplined solver augmentation, not magic optimization.
MemVerse shows why persistent AI agents need structured multimodal memory, fast distilled recall, and evidence-grounded retrieval—not just longer context windows.
DeepRule shows how LLMs can turn messy retail knowledge into auditable assortment and pricing rules, but the real lesson is the pipeline, not the model.
A practical reading of an MCP-integrated Blocksworld benchmark showing why planning, verification, execution, and replanning must be tested together before LLM agents touch real operations.