Large Audio-Language Models (LALMs) as judges have emerged as a prominent approach for evaluating speech generation quality, yet their ability to assess speaker consistency across multi-turn dialogues remains unexplored.We present SpeakerSleuth, a benchmark evaluating whether LALMs can reliably judge speaker consistency across multi-turn dialogues through three tasks reflecting real-world requirements.We construct 1,818 humanverified evaluation instances across four diverse datasets spanning synthetic and real speech, with controlled acoustic difficulty.Evaluating twelve widely-used LALMs, we find that models struggle to reliably detect acoustic inconsistencies.For instance, given audio samples of the same speaker's turns, some models overpredict inconsistency, whereas others are overly lenient.Models further struggle to identify the exact turns that are problematic.When other interlocutors' turns are provided as textual context, performance degrades dramatically as models prioritize textual coherence over acoustic cues, failing to detect even obvious gender switches for a speaker.On the other hand, models perform substantially better in comparing and ranking acoustic variants, demonstrating inherent acoustic discrimination capabilities.These findings expose a significant bias in LALMs: they tend to prioritize text over acoustics, revealing fundamental modality imbalances that need to be addressed to build reliable audio-language judges.Our code and data are available at https: //github.com/holi-lab/SpeakerSleuth.

Small language models (SLMs) enable scalable tool-augmented multi-agent systems where multiple SLMs handle subtasks orchestrated by a powerful coordinator.However, they struggle with tool-use tasks, particularly in selecting appropriate tools and identifying correct parameters.A common failure mode is schema misalignment: models hallucinate plausible tool names that are absent from the provided tool schema, due to different naming conventions internalized during pretraining.Rather than training models to adapt to unfamiliar schemas, we propose adapting schemas to align with models' pretrained knowledge.We introduce PA-Tool (Pretraining-Aligned Tool Schema Generation), a training-free method that leverages peakedness, a signal used in contamination detection that indicates pretraining familiarity, to rename tool components.By generating multiple candidates and selecting the candidate with the highest peakedness, PA-Tool identifies pretrainingaligned naming patterns.Experiments on Meta-Tool and RoTBench show improvements of up to 17%, with schema misalignment errors reduced by 80%.PA-Tool enables small models to substantially improve tool-use accuracy without retraining, showing that schema-level interventions can unlock the tool-use potential of resource-efficient models.Our code is available at https://github.com/holi-lab/P A-Tool.