artificialintelbulletin.com

Tag: cs.SD

  • CircuitTree: Composing Circuits into A Syllable Transcriber

    CircuitTree: Composing Circuits into A Syllable Transcriber

    [Submitted on 7 Oct 2026]

    View PDF HTML (experimental)

    Abstract:The goal of circuit discovery is to decompose a monolithic neural network into tiny modular subnetworks which are interpretable in the sense that each does an identifiable subtask. We develop a practical method for composing arbitrarily many circuits into a single model. We propose a greedy, density-scaled composition algorithm that overcomes limitations of previous attempts to compose circuits by naive union, then generalize it to $N$ circuits via CircuitTree, a new data structure built on a $Theta(Nlog N)$ tree-compose schedule. We compose circuits pruned from HuBERT for classifying individual phonemes into a syllable classifier, a capability none of the 39 phoneme circuits has individually. Chaining CircuitTrees into a hierarchy-of-circuits pipeline, we transcribe open syllables at 64.8% accuracy, within 1.2 points of a 66.0% ceiling that shares no circuitry at all, with 99.5% of circuit components shared.

    Submission history

    From: Jiankun Wei [view email]
    [v1] Wed, 7 Oct 2026 21:29:44 UTC (148 KB)

  • What Do Audio-Language Judges of Generated Music Listen For? Four Probes with Controls from Expert Ratings

    What Do Audio-Language Judges of Generated Music Listen For? Four Probes with Controls from Expert Ratings

    [Submitted on 9 Oct 2026]

    View PDF HTML (experimental)

    Abstract:Open audio-language models are used as judges of generated music: given a clip and its text prompt, the model rates the clip, and the judge is validated by how well its ratings correlate with expert ratings. The correlation does not show what the judge responds to: the music, the text prompt, or the way the question is asked. We propose four probes that test each of these, three of them read against a control computed from the human ratings: removing the audio, replacing the clip with another model’s clip for the same prompt, swapping the order of two clips in pairwise judging, and rewording the instruction. We apply them to five open judges on all 2,748 clips of MusicEval, and the last two to the SongEval songs. Without audio, no judge exceeds what the prompt alone predicts from the human ratings, and for prompt alignment every judge falls below it. With audio, every judge follows the clip it hears when rating prompt alignment, two do when rating musical quality, and the strongest judge’s quality score tracks the experts’ alignment rating more than their quality rating. The pairwise verdicts of the four smaller judges follow the position of a clip in the pair (its slot) rather than its quality on both rating sets. For musical quality, rewording the instruction moves the correlation of most judges as much as changing the judge. MusicEval’s prompt assignment bounds any prompt-only judge at the model level. We recommend reporting the four probes with every music judge.

    Submission history

    From: Mateusz Modrzejewski [view email]
    [v1] Fri, 9 Oct 2026 08:56:54 UTC (50 KB)