arXiv subjects

Jinxin Li

Publications and source records attributed to Jinxin Li.

4 recordsLinked to original sources

arXiv2026

Simultaneous estimation of relative phase and coherence in astronomical interferometry

Astronomical interferometry is a cornerstone technique for high-resolution stellar imaging and observational astrophysics, extracting spatial information from the coherence of light collected by separated telescopes. Since the degree of coherence is complex, a genuine imaging task requires the joint recovery of the modulus and the relative phase, instead of independent singleparameter estimations. We investigate the simultaneous estimation of both parameters based on direct interferometry scheme and continuou-svariable quantum teleportation scheme. We find that in simultaneous estimation the direct interferometry scheme consistently yields a lower quantum Cram\'er-Rao bound, demonstrating its superiority over the continuous-variable quantum teleportation scheme. Furthermore, we establish the conditions under which the classical Cram\'er-Rao bound for Gaussian measurements saturates the quantum Cram\'er-Rao bound, identifying heterodyne detection as a near-optimal measurement scheme in the large mean photon number regime. An analysis of transmission loss reveals that the direct interferometry scheme yields superior precision in the short-baseline regime, whereas the continuous-variable quantum teleportation scheme outperforms it at longer baselines.

quant-ph↗

arXiv2025

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.

cs.CL↗

arXiv2025

HARP: Hallucination Detection via Reasoning Subspace Projection

Hallucinations in Large Language Models (LLMs) pose a major barrier to their reliable use in critical decision-making. Although existing hallucination detection methods have improved accuracy, they still struggle with disentangling semantic and reasoning information and maintaining robustness. To address these challenges, we propose HARP (Hallucination detection via reasoning subspace projection), a novel hallucination detection framework. HARP establishes that the hidden state space of LLMs can be decomposed into a direct sum of a semantic subspace and a reasoning subspace, where the former encodes linguistic expression and the latter captures internal reasoning processes. Moreover, we demonstrate that the Unembedding layer can disentangle these subspaces, and by applying Singular Value Decomposition (SVD) to its parameters, the basis vectors spanning the semantic and reasoning subspaces are obtained. Finally, HARP projects hidden states onto the basis vectors of the reasoning subspace, and the resulting projections are then used as input features for hallucination detection in LLMs. By using these projections, HARP reduces the dimension of the feature to approximately 5% of the original, filters out most noise, and achieves enhanced robustness. Experiments across multiple datasets show that HARP achieves state-of-the-art hallucination detection performance; in particular, it achieves an AUROC of 92.8% on TriviaQA, outperforming the previous best method by 7.5%.

cs.CL↗

arXiv2025

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.

cs.RO↗