·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
The AI layoff wave is becoming a powder keg1h◆GAGPO: Generalized Advantage Grouped Policy Optimization4h◆When and How Severely: Scenario-Specific Safety Envelopes for Driving VLAs4h◆ChronoID: Infusing Explicit Temporal Signals into Semantic IDs for Generative Recommendation4h◆Robust Fall Recovery for Armless Bipedal-Wheeled Robots Via Force-Guided Learning4h◆DIFF-ERO: A Conformance-Aware Loss for Deep Learning in Process Mining4h◆AgentCyberRange: Benchmarking Frontier AI Systems in Realistic Cyber Ranges4h◆Pix2Pix-Hybrid: Structure-Guided Conditional Synthesis of Hajj Crowd Images with Multi-Channel Conditioning and Weak Attribute Supervision4h◆Thinking Outside the [Chat]Box: Bridging Computer Science and Industrial Design for Cognitive-Inclusive Generative AI4h◆Achieving Precise Text-To-Cypher Via Grounded Knowledge Graph Data Generation4h◆I'm Sorry Driver, I'm Afraid I Can't Do That: Appraising the Safety of LLMs within Automotive Contexts4h◆Discovery under Hypothesis Redundancy: A Geometric Theory of Discovery Bottlenecks4h◆The Perceived Fragility of Explanations in Audio Models: Manipulation of Attribution with Unchanged Predictions4h◆SIMMER: Benchmarking Latent Failures in LLM Executable Planning with a World Model4h◆CARE: Controlling LLM-Generated Policies through Auditable Review of Evidence in Scientific Experimentation4h◆A Comparative Study of Deep Learning Architectures for Multi-Horizon Behavioural Forecasting for Mobile Health4h◆Expert-Driven Survival Machines: Improving Stratification and Interpretability in Multiple Clinical Cohorts4h◆Moonlight in Latent Space: Chirality and Structural Correspondence Between Beethoven's Op. 27 No. 2 and Machine Learning Mechanisms4h◆When Good Verifiers Go Bad: Self-Improving VLMs Can Regress on New Tasks4h◆From Self-Supervised Speech Models to Mixture-of-Experts for Robust Anti-Spoofing4h◆The AI layoff wave is becoming a powder keg1h◆GAGPO: Generalized Advantage Grouped Policy Optimization4h◆When and How Severely: Scenario-Specific Safety Envelopes for Driving VLAs4h◆ChronoID: Infusing Explicit Temporal Signals into Semantic IDs for Generative Recommendation4h◆Robust Fall Recovery for Armless Bipedal-Wheeled Robots Via Force-Guided Learning4h◆DIFF-ERO: A Conformance-Aware Loss for Deep Learning in Process Mining4h◆AgentCyberRange: Benchmarking Frontier AI Systems in Realistic Cyber Ranges4h◆Pix2Pix-Hybrid: Structure-Guided Conditional Synthesis of Hajj Crowd Images with Multi-Channel Conditioning and Weak Attribute Supervision4h◆Thinking Outside the [Chat]Box: Bridging Computer Science and Industrial Design for Cognitive-Inclusive Generative AI4h◆Achieving Precise Text-To-Cypher Via Grounded Knowledge Graph Data Generation4h◆I'm Sorry Driver, I'm Afraid I Can't Do That: Appraising the Safety of LLMs within Automotive Contexts4h◆Discovery under Hypothesis Redundancy: A Geometric Theory of Discovery Bottlenecks4h◆The Perceived Fragility of Explanations in Audio Models: Manipulation of Attribution with Unchanged Predictions4h◆SIMMER: Benchmarking Latent Failures in LLM Executable Planning with a World Model4h◆CARE: Controlling LLM-Generated Policies through Auditable Review of Evidence in Scientific Experimentation4h◆A Comparative Study of Deep Learning Architectures for Multi-Horizon Behavioural Forecasting for Mobile Health4h◆Expert-Driven Survival Machines: Improving Stratification and Interpretability in Multiple Clinical Cohorts4h◆Moonlight in Latent Space: Chirality and Structural Correspondence Between Beethoven's Op. 27 No. 2 and Machine Learning Mechanisms4h◆When Good Verifiers Go Bad: Self-Improving VLMs Can Regress on New Tasks4h◆From Self-Supervised Speech Models to Mixture-of-Experts for Robust Anti-Spoofing4h◆
News/I'm Sorry Driver, I'm Afraid I Can't Do That: Appraising the Safety of LLMs within Automotive Contexts
arxiv
PublishedJune 15, 2026 at 4:00 AM

I'm Sorry Driver, I'm Afraid I Can't Do That: Appraising the Safety of LLMs within Automotive Contexts

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2606.14327v1 Announce Type: cross Abstract: This paper appraises recent frameworks within AI development to integrate LLMs into control tasks in automotive contexts from the perspective of safety assurance. This work has built upon the rapid integration of LLMs across automotive settings. Howe

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivGAGPO: Generalized Advantage Grouped Policy Optimization4harxivWhen and How Severely: Scenario-Specific Safety Envelopes for Driving VLAs4harxivChronoID: Infusing Explicit Temporal Signals into Semantic IDs for Generative Recommendation4harxivRobust Fall Recovery for Armless Bipedal-Wheeled Robots Via Force-Guided Learning4h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews