Cognaptus Insights

How CASE uses context-aware semantic embeddings to add LLM-derived meaning to tabular prediction pipelines without replacing traditional tabular models.

Chameleon tests whether threat estimates can control how a honeypot allocates engagement effort, rather than merely improve the text it generates.

CAP shows why browser-agent readiness depends less on visible progress than on reliably completing every required action and perception step across real websites.

HLSR shows why selective rerouting can benefit more from matching live and predicted traffic to route timing than from simply replanning more vehicles.

DataSpace shows that enterprise data-agent reliability depends not only on finding and analyzing evidence, but also on the harness and the exact materialization of the requested result.

A new verification layer suggests that many safety-classifier failures can be corrected and monitored before operators resort to retraining the underlying model.

AtmosERC shows that extracting a persistent affective signal from conversation history can outperform simply pooling the same global context.

A NAVSIM audit shows why model rankings should pass behavioral sanity and numerical-stability checks before they drive selection, release, or safety decisions.

Near-perfect separation of jailbreak prompts in representation space does not mean a safety monitor can reliably distinguish refusal from unsafe compliance.

A formal framework shows how autonomous-agent audits can localize newly introduced workflow errors, control false flags, and reveal where high-dimensional attribution stops being statistically reliable.