Tootfinder

TRUST: Test-time Resource Utilization for Superior Trustworthiness
Haripriya Harikumar, Santu Rana
https://arxiv.org/abs/2506.06048 https://

TRUST: Test-time Resource Utilization for Superior Trustworthiness
Standard uncertainty estimation techniques, such as dropout, often struggle to clearly distinguish reliable predictions from unreliable ones. We attribute this limitation to noisy classifier weights, which, while not impairing overall class-level predictions, render finer-level statistics less informative. To address this, we propose a novel test-time optimization method that accounts for the impact of such noise to produce more reliable confidence estimates. This score defines a monotonic subs…

Benchmarking Time-localized Explanations for Audio Classification Models
Cecilia Bola\~nos, Leonardo Pepino, Martin Meza, Luciana Ferrer
https://arxiv.org/abs/2506.04391

Benchmarking Time-localized Explanations for Audio Classification Models
Most modern approaches for audio processing are opaque, in the sense that they do not provide an explanation for their decisions. For this reason, various methods have been proposed to explain the outputs generated by these models. Good explanations can result in interesting insights about the data or the model, as well as increase trust in the system. Unfortunately, evaluating the quality of explanations is far from trivial since, for most tasks, there is no clear ground truth explanation to u…

I understand why sensible people say not to trust "answers" from AI/LLM but I don't understand why they then act like that invalidates the usefulness of LLMs.
abbv dividends 2021 through 2025?
Answer for some reason lists a chart for Agilent Technologies, Inc.
Then lists a chart showing dividends for ABBV by year (including quarterly payment amounts).
I can see those amounts are close (probably exactly right...) and for my quick estimate good enough.