/ Paper arXiv 2611.15463
In short
In shortReport a mistake
Key numbers
From the paperCopied from the abstract, not written by AI.
Why this ranked
MeasuredPublic numbers, not AIHow ranking works
8 / 100#97 of 1200 papers that day, from GitHub, Hugging Face, Hacker News and Semantic Scholar, looked up the morning it was ranked.
Builder evidence8 / 40
- Code released
Community attention0 / 30
- ▲ 0 upvotes on Hugging Face
Author track record0 / 15
- Nothing found
What the paper shows0 / 15
- Nothing found
Abstract
From the paperThe authors' own words.
A large audio-language model (LALM) can answer "is a cat present?" about a mixture correctly, and its confidence can drop when the cat is removed, yet this does not show that it can tell the cat's presence from its absence. We introduce TRACE. It deletes the queried source and each rival in turn and asks whether every view still containing that sound outscores the view with it removed. Across 8 LALMs and 3,456 controlled mixtures, 6.3% of correct answers fail this ordering: the target-absent view scores at or above some target-present view, a conflict no shared threshold repairs. A target-only score check misses 42.5% of them. The share rises from 4.9% to 8.4% as target SNR falls from +5 to -5 dB, even on units correct at every level, and survives loudness and re-pairing controls. Accuracy certifies the answer; TRACE audits whether it orders presence above absence. Code and data: https://github.com/yikise/TRACE.
From the full paper
From the paperCopied from the paper itself by our code.
Reading the paper on arXiv…
Try it
Code, models and demos that use this paper.
Ask this paper
/ Ask a questionAnswers come from the full paper and name the section
/ Your notes
Select text in the abstract to highlight it or add a note. They stay in this browser.
