BBS:      TELESC.NET.BR
Assunto:  Researchers teach AI to correct own mistakes
De:       Mike Powell
Data:     Sat, 14 Feb 2026 12:35:00 -0500
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Swiss scientists want to make long AI-generated videos even better by
preventing them from 'degrading into randomness' - is that a good idea? I am
not so sure

By Efosa Udinmwen published 21 hours ago

EPFL researchers teach AI to correct its own video mistakes

    AI-generated videos often lose coherence over time due to a problem called
drift
    Models trained on perfect data struggle when handling imperfect real-world
input
    EPFL researchers developed retraining by error recycling to limit
progressive degradation

AI-generated videos often lose coherence as sequences grow longer, a problem
known as drift.  This issue occurs because each new frame is generated based on
the previous one, so any small error, such as a distorted object or slightly
blurred face, is amplified over time.

Large language models trained exclusively on ideal datasets struggle to handle
imperfect input, which is why videos usually become unrealistic after a few
seconds.

Recycling errors to improve AI performance

Generating videos that maintain logical continuity for extended periods remains
a major challenge in the field.  Now, researchers at EPFL's Visual
Intelligence for Transportation (VITA) laboratory have introduced a method
called retraining by error recycling.

Unlike conventional approaches that try to avoid errors, this method
deliberately feeds the AI's own mistakes back into the training process.  By
doing so, the model learns to correct errors in future frames, limiting the
progressive degradation of images.

The process involves generating a video, identifying discrepancies between
produced frames and intended frames, and retraining the AI on these
discrepancies to refine future output.

Current AI video systems typically produce sequences that remain realistic for
less than 30 seconds before shapes, colors, and motion logic deteriorate.

By integrating error recycling, the EPFL team has produced videos that resist
drift over longer durations, potentially removing strict time constraints on
generative video.

This advancement allows AI systems to create more stable sequences in
applications such as simulations, animation, or automated visual storytelling.

Although this approach addresses drift, it does not eliminate all technical
limitations.  Retraining by recycling errors increases computational demand and
may require continuous monitoring to prevent overfitting to specific mistakes.
Large-scale deployment may face resource and efficiency constraints, as well as
the need to maintain consistency across diverse video content.

Whether feeding AI its own errors is truly a good idea remains uncertain, as
the method could introduce unforeseen biases or reduce generalization in
complex scenarios.

The development at VITA Lab shows that AI can learn from its own errors,
potentially extending the time limits of video generation.

However, how this method will perform outside controlled testing or in creative
applications remains unclear, which suggests caution before assuming it can
fully solve the drift problem.

Via TechXplore


https://www.techradar.com/pro/swiss-scientists-want-to-make-long-ai-generated-v
ideos-even-better-by-preventing-them-from-degrading-into-randomness-is-that-a-g
ood-idea-i-am-not-so-sure

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