When the Transcript Stops Being Evidence: Re-Sonance and the Limit of LLM Speech Repair
Re-Sonance shows where LLM correction can rescue dysarthric speech recognition—and where upstream signal loss makes downstream intelligence insufficient.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Fix the Worst Frame First: Adaptive Anchoring for Synthetic Video Supervision
Adaptive Identity Anchoring reframes synthetic face-swap supervision as a measured repair process, but its quality and cost advantages remain hypotheses awaiting validation.
When Conservatism Wins: Rare-Event Estimation Depends on What You Fear Missing
Rare-event estimators can reverse rank when the cost of underestimating a failure changes, making the evaluation loss part of the operational decision.
When Saying Less Scores More: The Win-by-Silence Failure in AI Plan Evaluation
A plan scorer can reward omitted work, and optimization can discover the exploit without being told where it is.
English Looks Ready. Amharic Says Otherwise: What ADAGE Exposes in Multilingual Evaluation
ADAGE shows why strong English reasoning scores can give multilingual product teams an incomplete picture of capability in native-language markets.
Safe at the Finish, Unsafe on the Way: What SafeRelBench Exposes in Embodied AI
SafeRelBench shows why task completion can hide unsafe action ordering in embodied agents, and what deployment teams should measure instead.
Same Algorithm, Different Outcome: What 33,000 Actor-Critic Runs Reveal
A large controlled study shows why reinforcement-learning reliability depends less on algorithm labels than on critic quality, policy representation, gradient estimation, and update schedules.
Black Box Is Too Blunt: What AI Interfaces Reveal to Attackers
A signal-based view of model access turns API outputs, embeddings, and deployment choices into concrete adversarial-security decisions.
One Explanation Is a Single Point of Failure: RashomonLLM Turns Explanations Into Predictive Feedback
A new explainability framework argues that validated explanations can improve tabular prediction when they are diversified, tested for faithfulness, and revised from prediction errors.
The Checker Gets the Final Say: What P-99 Reveals About Verified AI Coding
A Prolog case study shows how AI coding agents can generate code and proofs while deterministic verification remains the acceptance gate—and specification remains the human bottleneck.