Uncontrolled intersections account for a significant fraction of roadway crashes due to ambiguous right-of-way rules, occlusions, and unpredictable driver behavior. While autonomous vehicle research has explored uncertainty-aware decision making, few systems exist to retrofit human-operated vehicles with assistive navigation support. We present a driver-assist framework for right-of-way reasoning at uncontrolled intersections, formulated as a Partially Observable Markov Decision Process (POMDP). Using a custom simulation testbed with stochastic traffic agents, pedestrians, occlusions, and adversarial scenarios, we evaluate four decision-making approaches: a deterministic finite state machine (FSM), and three probabilistic planners: QMDP, POMCP, and DESPOT. Results show that probabilistic planners outperform the rule-based baseline, achieving up to 97.5 percent collision-free navigation under partial observability, with POMCP prioritizing safety and DESPOT balancing efficiency and runtime feasibility. Our findings highlight the importance of uncertainty-aware planning for driver assistance and motivate future integration of sensor fusion and environment perception modules for real-time deployment in realistic traffic environments.

Trajectory anomaly detection is essential for identifying unusual and unexpected movement patterns in applications ranging from intelligent transportation systems to urban safety and fraud prevention. Existing methods only consider limited aspects of the trajectory nature and its movement space by treating trajectories as sequences of sampled locations, with sampling determined by positioning technology, e.g., GPS, or by high-level abstractions such as staypoints. Trajectories are analyzed in Euclidean space, neglecting the constraints and connectivity information of the underlying movement network, e.g., road or transit networks. The proposed Graph Enhanced Trajectory Anomaly Detection (GETAD) framework tightly integrates road network topology, segment semantics, and historical travel patterns to model trajectory data. GETAD uses a Graph Attention Network to learn road-aware embeddings that capture both physical attributes and transition behavior, and augments these with graph-based positional encodings that reflect the spatial layout of the road network. A Transformer-based decoder models sequential movement, while a multiobjective loss function combining autoregressive prediction and supervised link prediction ensures realistic and structurally coherent representations. To improve the robustness of anomaly detection, we introduce Confidence Weighted Negative Log Likelihood (CW NLL), an anomaly scoring function that emphasizes high-confidence deviations. Experiments on real-world and synthetic datasets demonstrate that GETAD achieves consistent improvements over existing methods, particularly in detecting subtle anomalies in road-constrained environments. These results highlight the benefits of incorporating graph structure and contextual semantics into trajectory modeling, enabling more precise and context-aware anomaly detection.

Generation of Artificial Intelligence (AI) texts in important works has become a common practice that can be used to misuse and abuse AI at various levels. Traditional AI detectors often rely on document-level classification, which struggles to identify AI content in hybrid or slightly edited texts designed to avoid detection, leading to concerns about the model's efficiency, which makes it hard to distinguish between human-written and AI-generated texts. A sentence-level sequence labeling model proposed to detect transitions between human- and AI-generated text, leveraging nuanced linguistic signals overlooked by document-level classifiers. By this method, detecting and segmenting AI and human-written text within a single document at the token-level granularity is achieved. Our model combines the state-of-the-art pre-trained Transformer models, incorporating Neural Networks (NN) and Conditional Random Fields (CRFs). This approach extends the power of transformers to extract semantic and syntactic patterns, and the neural network component to capture enhanced sequence-level representations, thereby improving the boundary predictions by the CRF layer, which enhances sequence recognition and further identification of the partition between Human- and AI-generated texts. The evaluation is performed on two publicly available benchmark datasets containing collaborative human and AI-generated texts. Our experimental comparisons are with zero-shot detectors and the existing state-of-the-art models, along with rigorous ablation studies to justify that this approach, in particular, can accurately detect the spans of AI texts in a completely collaborative text. All our source code and the processed datasets are available in our GitHub repository.

We present a formal and constructive theory showing that probabilistic finite automata can be exactly simulated using symbolic feedforward neural networks. Our architecture represents state distributions as vectors and transitions as stochastic matrices, enabling probabilistic state propagation via matrix-vector products. This yields a parallel, interpretable, and differentiable simulation of probabilistic finite automata dynamics using soft updates without recurrence. We formally characterize probabilistic subset construction, finite ε-closure, and exact simulation via layered symbolic computation, and prove equivalence between probabilistic finite automata and specific classes of neural networks. We further show that these symbolic simulators are not only expressive but learnable: trained with standard gradient descent-based optimization on labeled sequence data, they recover the exact behavior of ground-truth probabilistic finite automata. This learnability, formalized in Proposition 5.1, is the crux of this work. Our results unify probabilistic automata theory with classical neural architectures under a rigorous algebraic framework, bridging the gap between symbolic computation and deep learning.

Recent advances have significantly improved our understanding of the sample complexity of learning in average-reward Markov decision processes (AMDPs) under the generative model. However, much less is known about the constrained average-reward MDP (CAMDP), where policies must satisfy long-run average constraints. In this work, we address this gap by studying the sample complexity of learning an $ε$-optimal policy in CAMDPs under a generative model. We propose a model-based algorithm that operates under two settings: (i) relaxed feasibility, which allows small constraint violations, and (ii) strict feasibility, where the output policy satisfies the constraint. We show that our algorithm achieves sample complexities of $\tilde{O}\left(\frac{S A (B+H)}{ ε^2}\right)$ and $\tilde{O} \left(\frac{S A (B+H)}{ε^2 ζ^2} \right)$ under the relaxed and strict feasibility settings, respectively. Here, $ζ$ is the Slater constant indicating the size of the feasible region, $H$ is the span bound of the bias function, and $B$ is the transient time bound. Moreover, a matching lower bound of $\tildeΩ\left(\frac{S A (B+H)}{ ε^2ζ^2}\right)$ for the strict feasibility case is established, thus providing the first minimax-optimal bounds for CAMDPs. Our results close the theoretical gap in understanding the complexity of constrained average-reward MDPs.

Care coordination and population health management (PHM) are core functions of health systems and community partners, impacting large numbers of Americans enrolled in Medicaid and other safety-net programs. These efforts aim to proactively identify needs, prioritize outreach, and escalate appropriately, all within finite staffing and budget constraints. While outreach modalities (text, phone, video, in-person) carry low clinical risk, their time and opportunity costs vary significantly, making efficiency a primary design goal. In practice, the central operational question is when to deploy expensive in-person outreach versus efficient virtual modalities to maximize value and equity under capacity constraints. These decisions must be made in strictly offline settings, where policies are learned from logged data without exploration at deployment [1]. Classical approaches include constrained Markov decision processes [2], risk-sensitive objectives, and conservative offline RL (e.g., CQL/IQL) [3, 4]. Conformal prediction can provide calibrated error control [5, 6]; ensembles provide practical uncertainty quantification [7]; and decision-time computation is common in control [8]. In health services research and health economic evaluation, cost-effectiveness and cost-benefit analyses (CEA/CBA) guide program-level choices [9-12], but they are not designed for per-patient, per-decision recommendations that adapt to granular state features and logged behavior constraints. 1

Abstract-- The complex nonlinear dynamics of hydraulic excavators, such as time delays and control coupling, pose significant challenges to achieving high-precision trajectory tracking. Traditional control methods often fall short in such applications due to their inability to effectively handle these nonlinearities, while commonly used learning-based methods require extensive interactions with the environment, leading to inefficiency. T o address these issues, we introduce EfficientTrack, a trajectory tracking method that integrates model-based learning to manage nonlinear dynamics and leverages closed-loop dynamics to improve learning efficiency, ultimately minimizing tracking errors. Comparative experiments in simulation demonstrate that our method outperforms existing learning-based approaches, achieving the highest tracking precision and smoothness with the fewest interactions. Real-world experiments further show that our method remains effective under load conditions and possesses the ability for continual learning, highlighting its practical applicability. Excavators are primarily used in earthworks, mining, and construction projects, playing a vital role in tasks such as digging, loading, trenching, and leveling [1], [2], [3].

Language-conditioned robotic manipulation in open-world settings requires not only accurate task execution but also the ability to detect failures for robust deployment in real-world environments. Although recent advances in vision-language models (VLMs) have significantly improved the spatial reasoning and task-planning capabilities of robots, they remain limited in their ability to recognize their own failures. In particular, a critical yet underexplored challenge lies in detecting semantic misalignment errors, where the robot executes a task that is semantically meaningful but inconsistent with the given instruction. To address this, we propose a method for building datasets targeting Semantic Misalignment Failures detection, from existing language-conditioned manipulation datasets. We also present I-FailSense, an open-source VLM framework with grounded arbitration designed specifically for failure detection. Our approach relies on post-training a base VLM, followed by training lightweight classification heads, called FS blocks, attached to different internal layers of the VLM and whose predictions are aggregated using an ensembling mechanism. Experiments show that I-FailSense outperforms state-of-the-art VLMs, both comparable in size and larger, in detecting semantic misalignment errors. Notably, despite being trained only on semantic misalignment detection, I-FailSense generalizes to broader robotic failure categories and effectively transfers to other simulation environments and real-world with zero-shot or minimal post-training. The datasets and models are publicly released on HuggingFace (Webpage: https://clemgris.github.io/I-FailSense/).

Effective policy learning for robotic manipulation requires scene representations that selectively capture task-relevant environmental features. Current approaches typically employ task-agnostic representation extraction, failing to emulate the dynamic perceptual adaptation observed in human cognition. We present HyperTASR, a hypernetwork-driven framework that modulates scene representations based on both task objectives and the execution phase. Our architecture dynamically generates representation transformation parameters conditioned on task specifications and progression state, enabling representations to evolve contextually throughout task execution. This approach maintains architectural compatibility with existing policy learning frameworks while fundamentally reconfiguring how visual features are processed. Unlike methods that simply concatenate or fuse task embeddings with task-agnostic representations, HyperTASR establishes computational separation between task-contextual and state-dependent processing paths, enhancing learning efficiency and representational quality. Comprehensive evaluations in both simulation and real-world environments demonstrate substantial performance improvements across different representation paradigms. Through ablation studies and attention visualization, we confirm that our approach selectively prioritizes task-relevant scene information, closely mirroring human adaptive perception during manipulation tasks. The project website is at https://lisunphil.github.io/HyperTASR_projectpage/.

Recent research has developed benchmarks for memory-augmented reinforcement learning (RL) algorithms, providing Partially Observable Markov Decision Process (POMDP) environments where agents depend on past observations to make decisions. While many benchmarks incorporate sufficiently complex real-world problems, they lack controllabil-ity over the degree of challenges posed to memory models. In contrast, synthetic environments enable fine-grained manipulation of dynamics, making them critical for detailed and rigorous evaluation of memory-augmented RL. Our study focuses on POMDP synthesis with three key contributions: 1. A theoretical framework for analyzing POMDPs, grounded in Memory Demand Structure (MDS), transition invariance, and related concepts; 2. A methodology leveraging linear process dynamics, state aggregation, and reward redistribution to construct customized POMDPs with predefined properties; 3. Empirically validated series of POMDP environments with increasing difficulty levels, designed based on our theoretical insights. Our work clarifies the challenges of memory-augmented RL in solving POMDPs, provides guidelines for analyzing and designing POMDP environments, and offers empirical support for selecting memory models in RL tasks.