Autex: A Reliability-First In-Car Voice Assistant Agent for the CAR-bench IJCAI-ECAI 2026 Challenge
We present Autex, a reliability-first in-car voice assistant agent submitted to the CAR-bench Challenge at IJCAI-ECAI 2026. Autex combines a deterministic multi-layer reliability kernel with a configurable LLM verifier to address three core CAR-bench failure modes: hallucinating unavailable capabilities, acting on ambiguous user intent, and violating domain policies. Epistemic guards fire before any LLM call, reducing the consistency gap between Pass@3 (latent capability) and Pass∧3 (deployment-readiness). We validate on 369 official CAR-bench trials across four frontier models, identify hallucination missing tool response as the dominant systemic weakness (0–14.3% Pass∧3 across all models tested), and describe the design choices made to address it. All model and provider configuration is environment-variable-driven, satisfying the Track 2 auditability and reconfigurability requirements.
Authors
- Eslam Abdelmogood Abdelkader (ORCID: https://orcid.org/0009-0009-3471-7388)
Institutions
- Oldham Council (GB)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-08-27
- DOI
- https://doi.org/10.5281/zenodo.22124500
- Primary Topic
- Adversarial Robustness in Machine Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.00