A mechanism-first reading of how routing statistics can turn a general-purpose MoE LLM into a smaller translation specialist, and where the compression claim stops short of cheaper inference.
A business-focused reading of why data filtering may be a compute-dependent strategy rather than a universal pretraining rule.
MemFail shows why persistent AI-agent memory should be evaluated by failure mode, not by vague recall accuracy or larger context windows.
A mechanism-first reading of AI Cartography, showing why raw LLM leaderboard ranks need latent-structure, ecosystem-noise, and scaling-law diagnostics before they become business evidence.
A business-focused reading of why uncertainty estimators can help detect LLM hallucinations only after task-specific validation.
A mechanism-first reading of contamination-resistant benchmark datasets: why protected latent inputs could make LLM evaluation harder to memorize, easier to govern, and still difficult to operationalize.
ProjectionBench turns AI scientific discovery from a vague ambition into a measurable context-sensitivity test.
A mechanism-first reading of how offline reinforcement learning can post-train code models by turning pre-verified code datasets into cheaper, harder-task learning signals.
A business-oriented reading of why AI peer reviewers look strongest when judged item by item, but weakest when treated as a replacement panel.
A practical reading of two arXiv papers on why preference alignment depends less on having more behavior data and more on whether the supervision signal actually reveals what people prefer.