Beam Me Less, Scotty: MoE Models Learn When Not to Call Every Expert
BEAM shows how separating expert selection from expert activation can turn MoE inference from a fixed Top-K habit into an adaptive compute-control layer.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Entropy, My Dear Watson: Finding Hallucinations in the Shape of Uncertainty
A mechanism-first reading of CES, a lightweight hallucination detector that treats token entropy distributions as operational risk fingerprints rather than mere confidence scores.
Expert Witness: How MoE Translation Models Can Lose Weight Without Losing the Plot
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.
Filter Bubble Bursts: When Common Crawl Beats Clean Data
A business-focused reading of why data filtering may be a compute-dependent strategy rather than a universal pretraining rule.
Memory Lane Has Potholes: MemFail and the Business of Testing Agent Recall
MemFail shows why persistent AI-agent memory should be evaluated by failure mode, not by vague recall accuracy or larger context windows.
Rank and File: AI Leaderboards Are Measurement Instruments, Not Scoreboards
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.
Uncertain Terms: Hallucination Scores Are Triage Signals, Not Lie Detectors
A business-focused reading of why uncertainty estimators can help detect LLM hallucinations only after task-specific validation.
Cache Me If You Can: Why LLM Benchmarks Need Contamination-Resistant Data
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.
Clue by Clue: ProjectionBench and the Business of Testing AI Discovery
ProjectionBench turns AI scientific discovery from a vague ambition into a measurable context-sensitivity test.
Compile Once, Train Later: Offline RL Moves Code-Model Verification Upstream
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.