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By establishing a human perceptual alignment metric and conducting systematic analysis, it is demonstrated that perceptual alignment is governed far more by how a model is trained (its learning objective) than by how well it performs (EER).
Results show that reliable verification improves accept-or-rethink decisions and reduces unnecessary reasoning turns, enabling more reliable and efficient self-verification without an external judge.
This work training a LALM with reinforcement learning to reason over its own speech output generates a draft speech as a form of audio-token reasoning, critiques its own generation by reflecting on the acoustic realization in text, and produces a refined version conditioned on both the first-pass speech and the critiqu...
AudioICL-Bench is introduced, a diagnostic benchmark whose per-episode rules are resampled so that no correct answer is recoverable from prior knowledge, and its nine tasks are organized along two axes that separate what must be learned from demonstrations from what must be perceived in the signal, enabling failures to...
The Teaching Monster Challenge is introduced, the first instructional video generation benchmark to treat the learner persona as an explicit evaluation criterion and release the benchmark, rubric, and human judgments as a testbed for both teaching systems and automatic judges.
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