Artism, or How AI Learned to Critique Itself
A mechanism-first reading of Artism, a dual-engine AI framework that turns generative art into a self-critical loop rather than another novelty machine.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Delegating to the Almost-Aligned: When Misaligned AI Is Still the Rational Choice
A decision-theoretic guide to deciding when imperfectly aligned AI systems are still worth delegating to.
From Benchmarks to Beakers: Stress‑Testing LLMs as Scientific Co‑Scientists
A comparison-based reading of SDE, a benchmark that tests whether frontier LLMs can move from science quiz performance to iterative scientific discovery.
Long Thoughts, Short Bills: Distilling Mathematical Reasoning at Scale
Nemotron-Math shows that better mathematical reasoning supervision is not just more data, but a carefully engineered mix of reasoning depth, tool use, source diversity, filtering, and long-context training economics.
Mind-Reading Without Telepathy: Predictive Concept Decoders
A mechanism-first reading of Predictive Concept Decoders and why activation-based audit layers may matter more than model self-explanations.
Stepwise Think-Critique: Teaching LLMs to Doubt Themselves (Productively)
A close reading of Stepwise Think-Critique, a single-model approach that interleaves reasoning and self-critique to make mathematical reasoning more inspectable without pretending self-audit is already trust.
When Tokens Remember: Graphing the Ghosts in LLM Reasoning
A practical reading of CAGE, an attribution-graph method that audits not only which prompt evidence influenced an LLM answer, but how intermediate generations carried that influence forward.
Greedy Enough to Win: When Loss Starts Driving the Learning Rate
A close reading of GreedyLR shows why loss-driven learning-rate scheduling is less a clever trick than a practical way to reduce wasted training motion.
Model First, Think Later: Why LLMs Fail Before They Reason
A practical reading of Model-First Reasoning: why agent failures often begin with unstable problem representation, not weak reasoning.
Picking Less to Know More: When RAG Stops Ranking and Starts Thinking
A mechanism-first reading of Context-Picker, a RAG framework that treats evidence selection as minimal sufficient subset choice rather than fixed Top-K retrieval.