LLM Hallucination Detection: A Fast Fourier Transform Method Based on Hidden Layer Temporal Signals

Hallucination remains a critical barrier for deploying large language models (LLMs) in reliability-sensitive applications. Existing detection methods largely fall into two categories: factuality checking, which is fundamentally constrained by external knowledge coverage, and static hidden-state analysis, that fails to capture deviations in reasoning dynamics. As a result, their effectiveness and robustness remain limited. We propose HSAD (Hidden Signal Analysis-based Detection), a novel hallucination detection framework that models the temporal dynamics of hidden representations during autoregressive generation. HSAD constructs hidden-layer signals by sampling activations across layers, applies Fast Fourier Transform (FFT) to obtain frequency-domain representations, and extracts the strongest non-DC frequency component as spectral features. Furthermore, by leveraging the autoregressive nature of LLMs, HSAD identifies optimal observation points for effective and reliable detection. Across multiple benchmarks, including TruthfulQA, HSAD achieves over 10 percentage points improvement compared to prior state-of-the-art methods. By integrating reasoning-process modeling with frequency-domain analysis, HSAD establishes a new paradigm for robust hallucination detection in LLMs.

Flying Vines: Design, Modeling, and Control of a Soft Aerial Robotic Arm

Aerial robotic arms aim to enable inspection and environment interaction in otherwise hard-to-reach areas from the air. However, many aerial manipulators feature bulky or heavy robot manipulators mounted to large, high-payload aerial vehicles. Instead, we propose an aerial robotic arm with low mass and a small stowed configuration called a "flying vine". The flying vine consists of a small, maneuverable quadrotor equipped with a soft, growing, inflated beam as the arm. This soft robot arm is underactuated, and positioning of the end effector is achieved by controlling the coupled quadrotor-vine dynamics. In this work, we present the flying vine design and a modeling and control framework for tracking desired end effector trajectories. The dynamic model leverages data-driven modeling methods and introduces bilinear interpolation to account for time-varying dynamic parameters. We use trajectory optimization to plan quadrotor controls that produce desired end effector motions. Experimental results on a physical prototype demonstrate that our framework enables the flying vine to perform high-speed end effector tracking, laying a foundation for performing dynamic maneuvers with soft aerial manipulators.