Who Was Where When? AI Tries to Remember History
HIPE-2026 turns person–place extraction from historical text into a test of temporal reasoning, evidential discipline, and deployable efficiency.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Causal Brews: Why Your Feature Engineering Needs a Graph Before a Grid Search
A mechanism-first reading of CAFE, a causally guided automated feature engineering framework that uses causal graphs as soft search priors rather than magical truth machines.
Certified to Speak: When AI Agents Need a Shared Dictionary
A mechanism-first reading of stimulus-meaning certification: how AI agents can test shared vocabulary before using it in consequential workflows.
From Causal Parrots to Causal Counsel: When LLMs Argue with Data
A mechanism-first reading of how LLMs can become auditable causal-prior generators when their claims are filtered by consensus, checked against data, and adjudicated by argumentation.
Small Models, Big Skills: When Agent Frameworks Meet Industrial Reality
A comparison-based reading of when Agent Skills make small language models useful in regulated industrial environments—and when they merely expose the model’s limits.
The Reliability Gap: Why Smarter AI Agents Still Fail When It Matters
A mechanism-first reading of why agent accuracy is not the same as production reliability, and how firms should evaluate consistency, robustness, predictability, and safety before deployment.
Thoughts in Motion: From Static Prompts to Self-Optimizing Reasoning Graphs
A mechanism-first reading of Framework of Thoughts, showing why reasoning performance depends on orchestration architecture as much as prompting cleverness.
When the Muse Has a GPU: Teaching a Machine to Write Poetry
A mechanism-first reading of a seven-month GPT-4 poetry workshop—and why the real business lesson is workflow design, not instant synthetic genius.
Do They Mean It? Testing Whether AI Actually ‘Reasons’ Behind the Wheel
CARE-Drive turns AI driving explanations into a testable question: do model decisions actually respond to human-relevant reasons, or merely sound as if they do?
From Guesswork to Generative Foresight: Why Diffusion Models May Fix Multi-Agent Blind Spots
GlobeDiff shows why partial observability in multi-agent systems is less a memory problem than a generative state-inference problem.