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.

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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
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article

Autex: A Reliability-First In-Car Voice Assistant Agent for the CAR-bench IJCAI-ECAI 2026 Challenge

Eslam Abdelmogood Abdelkader
Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
article

Autex: A Reliability-First In-Car Voice Assistant Agent for the CAR-bench IJCAI-ECAI 2026 Challenge

Eslam Abdelmogood Abdelkader
article en

Abstract

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.

Zenodo (CERN European Organization for Nuclear Research)
Oldham Council (GB)
Openalex Percentile: Top 8%
Adversarial Robustness in Machine Learning
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