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Listen-to-Reason (L2R), an interpretable-by-design pipeline that passes audio to the LLM through an explicit, human-readable tree that outperforms all LALMs the authors compare against on SAKURA and trails them by 6-12 points on MMAU and MMAR, is proposed.
Large audio-language models (LALMs) are sensitive to input perturbations, such as noise, waveform corruption, and adversarial injections. We propose AnchorPrompt, an efficient adaptation method that keeps the model frozen and learns a single block of prompt vectors inserted at the decoder input, between the audio and q...
A compositional alignment method is introduced that closes much of the modality gap between symbolic and numerical embeddings, reliably distinguish the effects of local edits that the original alignment cannot, and transfer to external SR corpora.
This work evaluates continued pretraining on FineWeb-Edu dumps drawn strictly from after each model's knowledge cutoff, evaluated across six open-weight models spanning three families and four parameter scales, painting a more optimistic picture of time-incremental CPT than the prior continual learning literature sugge...
SPARE (Semantic Prediction for Audio REasoning), which introduces a register token aligned with the final conclusion using a cosine similarity loss with a Sentence-BERT embedding, conditions the model's latent space with the target semantic goal before reasoning begins.
Experiments show that ZipCodec substantially outperforms existing streaming codecs at comparable bitrates in both reconstruction and downstream tasks, while operating at a significantly lower frame rate.
PiSAs (Privacy in Shared Agentic systems), a benchmark for assessing unintentional leaks with dual CI annotations, enables direct measurement of cross-user spillage across agentic system components and interfaces, such as outputs, inter-agent communication, and memory.
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