The Test Suite Passed. The Physics Did Not.
A case study in AI-assisted scientific software shows why enterprise reliability depends on supervision protocols, not just better coding agents.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Trace Evidence: The AI Learned Something. Can You Inspect What?
A practical synthesis of three arXiv papers on why AI learning from human traces, reasoning signals, rewards, and personalization must become inspectable before it becomes operationally trustworthy.
Uncertainty Without the Sampling Tax
A mechanism-first reading of Calibrated Variance Propagation, a method for getting useful Bayesian uncertainty from modern vision and multimodal models without paying for many test-time samples.
Feedback Is the New Attack Surface
Why automated prompt-injection risk is not just about malicious prompts, but about the feedback loops that let attackers optimize against agentic systems.
The Chain of Thought Needs a Chain of Custody
Why long-horizon AI systems need explicit intermediate controls, not just bigger models or longer context windows.
The Jailbreak Wasn’t Written. It Was Bred.
A mechanism-first reading of GAS-Leak-LLM and what black-box suffix optimization means for enterprise AI security testing.
The Model Spoke Your Language. Its Reasoning Did Not.
AdaMame shows why multilingual reasoning needs trained language fidelity, not polite prompts.
The Reasoning Trace Needs a Work Order
A practical reading of TGEO: why interpretable reasoning needs executable states, contracts, and audit trails rather than prettier chain-of-thought.
The Retriever Found Similar Things. The Evidence Was Elsewhere.
Why enterprise RAG should be treated as controlled evidence assembly, not a semantic-similarity contest.
The Solver Was Fine. The Premises Got Lost.
SciR shows why scientific AI evaluation must separate evidence extraction from formal reasoning before enterprises trust model answers in technical workflows.