SDialog: A Python Toolkit for End-to-End Agent Building, User Simulation, Dialog Generation, and Evaluation
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911315461668864 |
|---|---|
| author | Burdisso, Sergio Baroudi, Séverin Labrak, Yanis Grunert, David Cyrta, Pawel Chen, Yiyang Madikeri, Srikanth Villatoro-Tello, Esaú Schaaf, Thomas Marxer, Ricard Motlicek, Petr |
| author_facet | Burdisso, Sergio Baroudi, Séverin Labrak, Yanis Grunert, David Cyrta, Pawel Chen, Yiyang Madikeri, Srikanth Villatoro-Tello, Esaú Schaaf, Thomas Marxer, Ricard Motlicek, Petr |
| contents | We present SDialog, an MIT-licensed open-source Python toolkit that unifies dialog generation, evaluation and mechanistic interpretability into a single end-to-end framework for building and analyzing LLM-based conversational agents. Built around a standardized \texttt{Dialog} representation, SDialog provides: (1) persona-driven multi-agent simulation with composable orchestration for controlled, synthetic dialog generation, (2) comprehensive evaluation combining linguistic metrics, LLM-as-a-judge and functional correctness validators, (3) mechanistic interpretability tools for activation inspection and steering via feature ablation and induction, and (4) audio generation with full acoustic simulation including 3D room modeling and microphone effects. The toolkit integrates with all major LLM backends, enabling mixed-backend experiments under a unified API. By coupling generation, evaluation, and interpretability in a dialog-centric architecture, SDialog enables researchers to build, benchmark and understand conversational systems more systematically. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_09142 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SDialog: A Python Toolkit for End-to-End Agent Building, User Simulation, Dialog Generation, and Evaluation Burdisso, Sergio Baroudi, Séverin Labrak, Yanis Grunert, David Cyrta, Pawel Chen, Yiyang Madikeri, Srikanth Villatoro-Tello, Esaú Schaaf, Thomas Marxer, Ricard Motlicek, Petr Artificial Intelligence We present SDialog, an MIT-licensed open-source Python toolkit that unifies dialog generation, evaluation and mechanistic interpretability into a single end-to-end framework for building and analyzing LLM-based conversational agents. Built around a standardized \texttt{Dialog} representation, SDialog provides: (1) persona-driven multi-agent simulation with composable orchestration for controlled, synthetic dialog generation, (2) comprehensive evaluation combining linguistic metrics, LLM-as-a-judge and functional correctness validators, (3) mechanistic interpretability tools for activation inspection and steering via feature ablation and induction, and (4) audio generation with full acoustic simulation including 3D room modeling and microphone effects. The toolkit integrates with all major LLM backends, enabling mixed-backend experiments under a unified API. By coupling generation, evaluation, and interpretability in a dialog-centric architecture, SDialog enables researchers to build, benchmark and understand conversational systems more systematically. |
| title | SDialog: A Python Toolkit for End-to-End Agent Building, User Simulation, Dialog Generation, and Evaluation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2512.09142 |