arXiv AI
Toward a Decision-Assurance Layer for AI-Assisted Flight Planning in Air Traffic Management
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FLY-EVAL++: An Evidence-Driven Evaluation Protocol for Safety-Constrained Flight Prediction with Large Language Models
FLY-EVAL++ is an evidence-driven evaluation protocol designed for safety-constrained flight prediction with large language models. It combines deterministic verification of protocol compliance, physical feasibility, and safety constraints with rubric-guided aggregation into interpretable multi-dimensional scores. Applied to Flight Trajectory and Attitude Prediction, the protocol revealed that safety compliance is the most discriminative dimension among 66 LLMs, with models showing up to 28-point differences in safety scores and recurrent failures such as safety violations under physically plausible predictions and instability in multi-step rollouts.
Hugging Face Trending Papers
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FLY-EVAL++: An Evidence-Driven Evaluation Protocol for Safety-Constrained Flight Prediction with Large Language Models
FLY-EVAL++ is an evidence-driven evaluation protocol designed for safety-constrained flight prediction with large language models. It combines deterministic verification of protocol compliance, physical feasibility, and safety constraints, then aggregates results into interpretable multi-dimensional scores. Applied to Flight Trajectory and Attitude Prediction, the protocol reveals that safety compliance is the most discriminative metric, with models of similar predictive accuracy differing by over 28 points in safety score and exhibiting recurrent safety violations and instability in multi-step rollouts.
Hugging Face Trending Papers
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AeroCopilotBench: A Two-Tier Benchmark for Evaluating LLM Agents as Aviation Copilots in an Interactive Virtual Cockpit Environment
Large language model (LLM) agents may assist flight crews with complex decisions and task execution, but existing aviation evaluations centered on static knowledge do not support systematic testing of...
arXiv AI
Aug 18
AeroCopilotBench: A Two-Tier Benchmark for Evaluating LLM Agents as Aviation Copilots in an Interactive Virtual Cockpit Environment
arXiv:2608. 16349v1 Announce Type: new Abstract: Large language model (LLM) agents may assist flight crews with complex decisions and task execution, but existing aviation evaluations centered on static knowledge do not support systematic testing of procedural execution and safety compliance in interactive environments.
arXiv AI
Aug 21
On the Applicability of Safety Nets: A Safety-By-Design Solution for Certifying Neural Networks
arXiv:2608. 20053v1 Announce Type: new Abstract: The integration of Artificial Intelligence (AI) in safety-critical aviation systems presents significant challenges for certification and deployment.
arXiv Computation and Language
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Beyond Semantic Accuracy: Consequence-Aware Evaluation for Safety-Critical Language Understanding
arXiv:2608.24621v2 Announce Type: replace Abstract: Can language models be trusted in safety- critical operations? In such settings, strong per- formance on semantic metrics does not guaran- tee oper...