Accuracy and Uncertainty in Machine Deception Detection (Research White Paper)
cs.LG, cs.AI, cs.CR, stat.ML
Submitted: 2026-10-11
Updated: 2026-10-11
Terminology
Sources
- Understanding intermediate layers using linear classifier probes
- OpenAI o1 System Card
- Diverse and Adaptable Arm Coordination for Octopus-Crawling via Diffusion-Based Uncertainty-Aware Optimization
- Liars' Bench: Evaluating Lie Detectors for Language Models
- One Probe Won't Catch Them All: Towards Targeted Deception Detection
- Linear Probe Accuracy Scales with Model Size and Benefits from Multi-Layer Ensembling
- The Linear Representation Hypothesis and the Geometry of Large Language Models
- The Obfuscation Atlas: Mapping Where Honesty Emerges in RLVR with Deception Probes
- Qwen3 Technical Report
- Representation Engineering: A Top-Down Approach to AI Transparency
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks