AIvaluateXR benchmarks 17 LLMs across four XR platforms on performance, speed, memory and battery metrics and proposes a 3D Pareto optimality method to identify optimal on-device model-device pairs.
Truthfulqa: Measuring how models mimic human falsehoods
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2025 2verdicts
UNVERDICTED 2representative citing papers
A framework detects LLM anomalies including hallucinations, jailbreaks, and backdoors by forensic inspection of layer-wise hidden state patterns, reporting over 95% accuracy with minimal computational overhead.
citing papers explorer
-
AIvaluateXR: An Evaluation Framework for on-Device AI in XR with Benchmarking Results
AIvaluateXR benchmarks 17 LLMs across four XR platforms on performance, speed, memory and battery metrics and proposes a 3D Pareto optimality method to identify optimal on-device model-device pairs.
-
Exposing the Ghost in the Transformer: Abnormal Detection for Large Language Models via Hidden State Forensics
A framework detects LLM anomalies including hallucinations, jailbreaks, and backdoors by forensic inspection of layer-wise hidden state patterns, reporting over 95% accuracy with minimal computational overhead.