arXiv subjects

Anh N. Nhu

Publications and source records attributed to Anh N. Nhu.

4 recordsLinked to original sources

arXiv2026

6.5% of the Neuro-Symbolic Literature Can Be Reproduced from Its Published Artifacts, a Six-Stage Audit Framework and First Instantiation

We present a six-stage framework for auditing the reproducibility of scientific claims across a research literature within the computer science domain, and instantiate our framework for the neuro-symbolic AI (NSAI) subdomain. Instantiating the framework on the NSAI subdomain produced a multi-year audit. Stage one retrieved 5,497 records and removed 3,018 duplicates. Stage two screened the 2,479 unique records at title and abstract, identifying 1,365 self-identified NSAI records, then removed a further 61 at full text for off-topic, non-research, no-quantitative-evaluation, or inaccessible-full-text reasons. Stage three sought a verifiable public code artifact for each of the 1,304 eligible records and found none for 849, leaving 455 to enter the artifact inventory and bounded rerun of stages four and five. We fully or partially reproduced 85 studies, 6.52% of the eligible corpus and 18.68% of attempted reruns. We found that 321 attempted reruns were blocked by missing non- code artifacts and 42 by missing or unusable code repositories. These figures quantify a persistent reproducibility deficit that survives even nominal "code available" declarations, and signal the need for enforced, versioned, and permanently archived artifact bundles in future NSAI publications. We argue that empirical NSAI papers should be required at submission time to provide complete, versioned, and permanently archived artifact bundles.

cs.AI↗

arXiv2026

OpenVO: Open-World Visual Odometry with Temporal Dynamics Awareness

We introduce OpenVO, a novel framework for Open-world Visual Odometry (VO) with temporal awareness under limited input conditions. OpenVO effectively estimates real-world-scale ego-motion from monocular dashcam footage with varying observation rates and uncalibrated cameras, enabling robust trajectory dataset construction from rare driving events recorded in dashcam. Existing VO methods are trained on fixed observation frequency (e.g., 10Hz or 12Hz), completely overlooking temporal dynamics information. Many prior methods also require calibrated cameras with known intrinsic parameters. Consequently, their performance degrades when (1) deployed under unseen observation frequencies or (2) applied to uncalibrated cameras. These significantly limit their generalizability to many downstream tasks, such as extracting trajectories from dashcam footage. To address these challenges, OpenVO (1) explicitly encodes temporal dynamics information within a two-frame pose regression framework and (2) leverages 3D geometric priors derived from foundation models. We validate our method on three major autonomous-driving benchmarks - KITTI, nuScenes, and Argoverse 2 - achieving more than 20 performance improvement over state-of-the-art approaches. Under varying observation rate settings, our method is significantly more robust, achieving 46%-92% lower errors across all metrics. These results demonstrate the versatility of OpenVO for real-world 3D reconstruction and diverse downstream applications.

cs.CV↗

arXiv2025

Time-Aware World Model for Adaptive Prediction and Control

In this work, we introduce the Time-Aware World Model (TAWM), a model-based approach that explicitly incorporates temporal dynamics. By conditioning on the time-step size, Δt, and training over a diverse range of Δt values -- rather than sampling at a fixed time-step -- TAWM learns both high- and low-frequency task dynamics across diverse control problems. Grounded in the information-theoretic insight that the optimal sampling rate depends on a system's underlying dynamics, this time-aware formulation improves both performance and data efficiency. Empirical evaluations show that TAWM consistently outperforms conventional models across varying observation rates in a variety of control tasks, using the same number of training samples and iterations. Our code can be found online at: github.com/anh-nn01/Time-Aware-World-Model.

cs.LG↗

arXiv2024

Physics-Guided Reinforcement Learning System for Realistic Vehicle Active Suspension Control

The suspension system is a crucial part of the automotive chassis, improving vehicle ride comfort and isolating passengers from rough road excitation. Unlike passive suspension, which has constant spring and damping coefficients, active suspension incorporates electronic actuators into the system to dynamically control stiffness and damping variables. However, effectively controlling the suspension system poses a challenging task that necessitates real-time adaptability to various road conditions. This paper presents the Physics-Guided Deep Reinforcement Learning (DRL) for adjusting an active suspension system's variable kinematics and compliance properties for a quarter-car model in real time. Specifically, the outputs of the model are defined as actuator stiffness and damping control, which are bound within physically realistic ranges to maintain the system's physical compliance. The proposed model was trained on stochastic road profiles according to ISO 8608 standards to optimize the actuator's control policy. According to qualitative results on simulations, the vehicle body reacts smoothly to various novel real-world road conditions, having a much lower degree of oscillation. These observations mean a higher level of passenger comfort and better vehicle stability. Quantitatively, DRL outperforms passive systems in reducing the average vehicle body velocity and acceleration by 43.58% and 17.22%, respectively, minimizing the vertical movement impacts on the passengers. The code is publicly available at github.com/anh-nn01/RL4Suspension-ICMLA23.

cs.RO↗