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When the Transcript Stops Being Evidence: Re-Sonance and the Limit of LLM Speech Repair

TL;DR for operators A downstream AI model can rescue imperfect upstream output only while enough evidence survives to reconstruct what was lost. Re-Sonance1 makes that boundary unusually clear: after speech recognition, LLM correction lowers Word Error Rate from 21.58 to 13.74 for mild dysarthria and from 23.70 to 17.88 for moderate dysarthria, but severe-case WER rises from 83.77 to 84.40 and Match Error Rate rises from 87.50 to 90.96. Lower error rates mean the reconstructed wording is closer to the intended transcript. ...

The Model Saw Every Scene. The System Had to Remember the Story.

TL;DR for operators A scene-by-scene assistant can describe every clip fluently while quietly forgetting who the characters are, how they relate, or why an earlier event matters now. StoryTeller addresses that continuity problem without task-specific training by keeping a persistent record of recurring characters and carrying forward only narrative facts that have been checked against the video.1 ...

Dexterity Over Data: Why Sign Language Broke Generic 3D Pose Models

Hands are small, fast, and inconvenient. That is a problem for AI systems that prefer the world to be large, slow, and conveniently labeled. A walking person can be reconstructed with some tolerance for imprecision. A signer cannot. In sign language, a curled finger, wrist angle, palm orientation, or moment of hand-body contact may carry meaning. When the model gets that wrong, it is not merely producing an awkward avatar. It is quietly changing the message. ...