Engineering Sciences

This study presents a large-scale empirical analysis of cross-OS portability issues across 2,042 open-source Python repositories, combining cross-OS test reexecution (500 projects, 11.2% showing OS-dependent test failures) with manual analysis of 240 GitHub issues (confirming 102 genuine portability problems across 95 additional projects), and develops a taxonomy of 7 primary categories, 24 sub-categories, 15 diagnostic signatures, and 4 systematic repair patterns, while evaluating existing static analysis tools as providing minimal support and large language models achieving 40-79% accuracy in identifying issues and 50-77% success in generating fixes under structured guidance, with practical applicability demonstrated through 33 contributed pull requests (17 merged, zero rejected).

The work presents a learning-based framework for continuous autonomous excavation that integrates terrain-aware target selection with reinforcement- and imitation-learning controllers: a shared task-conditioned RL policy handles waypoint-guided approach and loaded transport, an IL policy learns vision-based digging and lifting from expert demonstrations, and digging targets are selected from LiDAR elevation maps and converted into bucket-tip waypoints; deployed on a scaled hydraulic excavator with multimodal sensing and closed-loop actuator control, offline replay and physical experiments show more consistent target selection, shorter local motion time, and increased payload, with the learned digging policy achieving a mean payload of 6.52 kg per completed cycle versus 2.

The work supervises every intermediate depth of one causal speech enhancement model and fine-tunes its output heads so deeper outputs are never worse than shallower ones, yielding a family of static models that are more Pareto-efficient than equivalently-sized counterparts trained from scratch on the same budget (up to 0.11 higher PESQ at equivalent compute, matching the best PESQ at 30% less compute); after int8 quantization on an STM32N6 microcontroller, the dynamic enhancer lies on the same latency-quality frontier as the static models, with the policy running in 26 microseconds per frame on the companion Cortex-M55 and splitting the enhancer into separate NPU graphs adding 2.2% latency overhead.

The work releases a new dataset consisting of the full development history of a 21,000-line Python tool built entirely by Claude AI with no human-authored code or tests, together with two code-provenance tracing tools and three taxonomies for instruction intent, commit provenance, and response reliability; applying these to the dataset, it finds that user coding agent CLI instructions differ in kind from IDE-chat instructions with a greater focus on comprehension, planning and consultation, that code development is mainly proactive, that 14.3% of AI code-generation events contain a real error later caught by the AI-authored test suite, and that roughly 1 in 4-5 of the AI's interactive responses contains one or more factual errors.

The work proposes Agentic Robot with Tool-use (ART), a tool-injection framework that tunes any VLA model to leverage off-the-shelf tool modules for low-level vision, high-level affordance, and embodiment enhancement; the authors built a dataset of 30K tool-use trajectories and action demonstrations and designed a training regimen for long-trajectory tool-use reasoning in challenging environments, with experiments showing ART achieves a 20% higher success rate than mainstream baselines on simulation and real-world tasks such as pick-and-place in the dark at novel viewpoints.

The work defines pedestrian-vehicle interaction as a partially observable Markov decision process (POMDP) with theory-grounded perceptual, cognitive, and motor constraints, trains it in a simulator via deep reinforcement learning with domain randomization, and shows that the learned policies produce human-like behavior in complex scenarios including multiple lanes, heavy traffic, and dangerous driving styles, reproducing the broadest range of empirical findings on human crossing behavior so far, including gap acceptance, yielding acceptance, hesitation, and evasive speed adjustment; the learned policies also transfer to unseen traffic environments and can be adapted to local traffic norms with finetuning.

This work introduces a morphogenetic graph-generation framework in which a discrete dot matrix supplies candidate nodes and the final architecture is built by sequential cross-layer and intra-layer growth; a dataset of distinct three-dimensional lattices on a 3x3x3 nodal matrix with 27 candidate nodes is evaluated by beam-based finite element analysis and represented directly as graphs, a graph convolutional neural network with three graph-convolution layers and dual global pooling learns the topology-property mapping and predicts effective compressive stiffness, and coupling this surrogate with rapid structural sampling enables inverse design: for a target stiffness of 1000 MPa the selected design was predicted at 1042.43 MPa and validated by finite element analysis at 1027.

The work proposes a simple torque-observation alignment method for direct-drive (DD) actuators: dynamometer calibration identifies the motor torque constant K_tau* to correct the scale mismatch between simulated and real torque, torque differences delta_tau(t) = tau(t) - tau(t-1) are used as the observation in both domains to remove the domain-dependent constant offset, and Gaussian noise derived from dynamometer measurement data is injected during learning; the authors train a teacher-student grasping policy entirely in simulation, deploy the distilled student on a multifingered DD gripper for proprioceptive grasping using only joint positions and torque differences, and the proposed method achieves 100% grasp success in an ablation study on nine in-distribution (ID) objects.