From Prompting to Porting: Surviving the LLM Upgrade Cycle
A case-first analysis of why production LLM prompts need migration discipline, not just better wording, when model versions change.
Cognaptus Insights
Industry thoughts, trends, and commentary.
School of Thought: How Fine-Tuned Open LLMs Are Challenging the Giants in Education
A comparison-based read of how fine-tuned open models can rival proprietary LLMs for narrow educational feedback tasks—without pretending they have conquered tutoring.
The Trojan GAN: Turning LLM Jailbreaks into Security Shields
CAVGAN shows that LLM jailbreaks and defenses may be two sides of the same hidden-representation boundary problem.
Beyond the Pareto Frontier: Pricing LLM Mistakes in the Real World
A practical reading of an economic LLM evaluation framework that turns accuracy, latency, abstention, and inference cost into dollar-valued deployment decisions.
Collapse to Forget: Turning Model Collapse into a Privacy Feature for LLMs
A mechanism-first look at Partial Model Collapse, a machine unlearning method that turns self-generation drift into targeted output removal for LLMs.
Mind Games: How LLMs Subtly Rewire Human Judgment
A mechanism-first reading of how LLM summaries can reframe evidence, overweight early context, hallucinate authority, and measurably shift user decisions.
Passing Humanity's Last Exam: X-Master and the Emergence of Scientific AI Agents
X-Master shows that scientific AI progress can come from inference-time orchestration, tool access, critique, rewriting, and selection—not only from larger model training.
The Phantom Menace in Your Knowledge Base
A practical reading of PhantomText, a study showing how invisible document manipulations can poison RAG systems before the model ever sees a prompt.
Backtrack to the Future: How ASTRO Teaches LLMs to Think Like Search Algorithms
ASTRO shows that reasoning gains can come from training models to recover from wrong turns, not merely from scaling models or wrapping them in external agent scaffolds.
Secret Handshakes at Scale: How LLM Agents Learn to Collude
A mechanism-first reading of how LLM market agents coordinate prices, why communication and pressure matter, and what operators should test before deploying autonomous pricing agents.