Trevor Bihl

Alessandro Pagani, Marco Cominelli, Liying Han et al.

This paper presents a preliminary analysis of the ability of Chronos foundation model to process and internally represent frequency domain information. Foundation models that process time-series data offer practitioners a unified architecture capable of learning generic temporal representations across diverse tasks and domains, reducing the need for task-specific feature engineering and enabling transfer across signal modalities. Despite their growing adoption, the extent to which such models encode fundamental signal properties remains insufficiently characterised. We address this gap by analysing Chronos under controlled conditions, starting from the simplest class of signals: discrete sinusoids generated at fixed frequencies. Using lightweight online minimum description length probes applied to the decoder architecture, we test for the presence and separability of frequency information in the model's internal representations. The results provide insight into how frequential content is captured across the frequency spectrum and highlight regimes in which representation quality may degrade or require particular care. These findings offer practical guidance for users of Chronos in signal processing and information fusion contexts, and contribute to ongoing efforts to improve the interpretability and evaluation of foundation models for temporal data.

Luca Cotti, Luca Lavazza, Marco Cominelli et al.

We address Human Activity Recognition (HAR) utilizing Wi-Fi Channel State Information (CSI) under the joint requirements of causal interpretability, symbolic controllability, and direct operation on high-dimensional raw signals. Deep neural models achieve strong predictive performance on CSI-based HAR (CHAR), yet rely on continuous latent representations that are opaque and difficult to modify; purely symbolic approaches, in contrast, cannot process raw CSI streams. We propose a fully automatic and strictly decoupled pipeline in which CSI magnitude windows are compressed by a categorical variational autoencoder with Gumbel-Softmax latent variables under a capacity-controlled objective, yielding a compact discrete representation. The encoder is then frozen and used as a deterministic mapping to one-hot latent trajectories. Causal discovery is performed on these trajectories to estimate class-conditional temporal dependency graphs. Statistically supported lagged dependencies are translated into Linear Temporal Logic (LTL) rules, producing a fully symbolic and deterministic classifier based solely on rule evaluation and aggregation, without any learned discriminative head. Because rules are defined over discrete latent variables, antenna-specific rule sets can in principle be combined at the symbolic level, enabling structured multi-antenna fusion without retraining the encoder. Results from CHAR Latent Temporal Rule Extraction (CHARL-TRE) indicate competitive performance while preserving explicit temporal and causal structure, showing that deterministic symbolic classification grounded in unsupervised discrete latent representations constitutes a viable alternative to end-to-end black-box models for wireless HAR.

Keegan Kimbrell, Alexis R. Tudor, Peter Van et al.

Autonomous unmanned aerial vehicles (UAVs) must operate safely in dynamic environments and adapt to changing mission conditions. Although deep learning approaches have shown strong performance for navigation and perception, they are often difficult to explain, verify, and modify for safety-critical tasks. We propose a symbolic state-centered UAV agent using the s(CASP) answer set programming system, enabling autonomous task execution with constraint-based commonsense reasoning in a high-fidelity Unreal Engine 5 environment. We fully implement prior work on the VECSR-A system to support multi-step autonomous behaviors including navigation, search, debris detection, precision spraying, object transport, and inspection. The UAV reasons over environmental and spatial constraints, dynamically revising plans when tasks fail or data is insufficient. Because decisions are based on commonsense reasoning, they are guaranteed to be correct and explainable. We evaluate the feasibility of s(CASP) for UAV control in realistic simulated missions. Results show that our framework enables explainable, adaptive autonomy without retraining, handling complex constraint-aware decisions and dynamic task reevaluation.