We present SDialog, an MIT-licensed open-source Python toolkit for end-to-end development, simulation, evaluation, and analysis of LLM-based conversational agents. Built around a standardized Dialog representation, SDialog unifies persona-driven multi-agent simulation with composable orchestration for controlled synthetic dialog generation; multi-layer evaluation combining linguistic metrics, LLM-as-a-judge assessments, and functional correctness validators; mechanistic interpretability tools for activation inspection and causal behavior steering via feature ablation and induction; and audio rendering with full acoustic simulation, including 3D room modeling and microphone effects. The toolkit integrates with major LLM backends under a consistent API, enabling mixed-backend and reproducible experiments. By bridging agent construction, user simulation, dialog generation, evaluation, and interpretability within a single coherent workflow, SDialog enables more controlled, transparent, and systematic research on conversational systems.

Speech Language Models (SLMs) aim to learn language from raw audio, without textual resources. Despite significant advances, our current models exhibit weak syntax and semantic abilities. However, if the scaling properties of neural language models hold for the speech modality, these abilities will improve as the amount of compute used for training increases. In this paper, we use models of this scaling behavior to estimate the scale at which our current methods will yield a SLM with the English proficiency of text-based Large Language Models (LLMs). We establish a strong correlation between pre-training loss and downstream syntactic and semantic performance in SLMs and LLMs, which results in predictable scaling of linguistic performance. We show that the linguistic performance of SLMs scales up to three orders of magnitude more slowly than that of text-based LLMs. Additionally, we study the benefits of synthetic data designed to boost semantic understanding and the effects of coarser speech tokenization.