arXiv subjects

George Xu

Publications and source records attributed to George Xu.

5 recordsLinked to original sources

arXiv2026

AI Agent Pull Requests on GitHub: Frequency, Structure, and Merge Conflict Rates

AI coding agents may generate and submit Pull Requests (PRs) to the same repository at the same time. However, research concerning the extent of concurrent submission by AI coding agents to a common repository does not exist. This paper uses the AIDev-pop dataset (33,596 PRs in 2,807 repositories) to provide the first empirical examination of the prevalence of concurrent submission using PRs authored by agents. We report that when considering exact temporal overlap, 40.2% of repositories contain co-active agent-authored PR pairs; further, the co-active pairs account for 79.4% of all PRs generated by an AI agent. When we examine co-activity within a one week collaboration window, the percentages are increased to 53.4% and 95.0%, respectively. For the majority of the co-active PR pairs (underlying the vast majority of which are intra-agent authored), both PRs were authored by the same agent, while only 0.5% of co-active pairs were cross-agent, and occurred in only 122 out of 2807 total repositories examined (or approximately 4.3%). Additionally, we replayed actual three way git merges on 747 unique co-active pairs (one per repository), and computed the percentage of textual conflict encountered during the merge operation to combine the two PRs in each pair. We observed that the percentage of textual conflict encountered was significantly higher for cross-agent pairs compared to intra-agent pairs: 41.7% vs. 19.8%, respectively, with non-overlapping 95% confidence intervals. Lastly, we developed a classification system based on the detection of conflict reported by git, and determined that the majority of conflicts resulted from modifications to source code files (84.4% of conflicted files) and not dependency manifest files; further, nearly 42% of conflicts we observed were structural (i.e., modify/delete or add/add).

cs.SE↗

arXiv2026

Field-rigid Ising antiferromagnetism with giant spin-flip fields in Van der Waals UOTe

Van der Waals antiferromagnets provide a route to thickness-controlled magnetic order, but few combine high-temperature Ising order with conducting, correlated, and topological electronic structure. Here we show that UOTe realizes this combination. Magnetic susceptibility reveals a strongly anisotropic paramagnetic response, while neutron diffraction establishes c-axis antiferromagnetic order below $T_N \simeq 150$ K with an order-parameter exponent $\beta = 0.14$, close to the two-dimensional Ising value. Torque magnetometry further shows that the ordered state remains well described by a uniaxial antiferromagnet below the high-field transition. Pulsed-field magnetization up to 73 T shows that the ordered state survives to very large fields applied along the c axis before entering a broad metamagnetic regime that begins near 50 T, and remains unsaturated at the highest measured field. Angle-dependent proximity detector oscillator measurements show that the metamagnetic instability is set by the field component along the ordered moment direction, providing direct evidence for Ising-like field rigidity. UOTe therefore establishes a field-rigid Ising antiferromagnet with giant spin-flip fields in a compensated Van der Waals metal, where high-temperature c-axis order, quasi-two-dimensional magnetic criticality, Kondo-associated uranium 5f hybridization, metallic transport, and symmetry-enabled topology coexist in a single material.

cond-mat.str-el↗

arXiv2026

Branched Signature Kernel Solvers for ODEs with rough Single-Trajectory signals

We develop a branched signature kernel solver for linear and nonlinear ordinary differential equations driven by a \emph{single observed trajectory} of a possibly rough forcing signal--a setting common within earthquake engineering, finance, biology, and structural health monitoring, where only one forcing realization is available, and the solver must respect the underlying physical law without an ensemble of realizations. We first introduce a count-sampling construction method to turn the single observation into a hierarchical family of $N+1$ nested training paths on which the branched signature kernel can be evaluated; this allows the signature kernel machinery, originally designed for multi-realization regression problems, to operate on a single-trajectory observation. Then we build a kernel-collocation framework, which places the ansatz either on the highest-order derivative of the solution or on the solution itself. We prove a universal approximation theorem for the branched signature kernel, leveraging the Hairer--Kelly morphism to express branched signature evaluations through geometric signatures of time-extended paths. The offline solver is extended to a streaming Test/Train/Retrain protocol with optional closed-form online updates in both linear and nonlinear cases. Numerical experiments on six benchmarks show accurate, stable predictions across all regimes.

math.NA↗

arXiv2026

RAMS: Resource-Adaptive and Detection-Conditioned Model Switching for Embedded Edge Perception

Edge object detection on embedded hardware requires balancing inference latency and detection quality under changing resource pressure. We present RAMS, a lightweight runtime controller that monitors device pressure, calibrates switching thresholds from idle behavior, and dynamically selects among three resident YOLOv8 tiers (NANO/SMALL/MEDIUM at 320/416/640 px) without model-reload latency. RAMS defines five switching policies, including two detection-conditioned variants that prevent aggressive downgrades after recent vulnerable-road-user (VRU) detections. We further introduce the VRU-Weighted Accuracy Score (SWAS), a scalar metric for offline policy comparison without ground-truth annotations, together with an oracle-bounded variant that separates detector circularity from genuine tier-retention benefit. Across Raspberry Pi 5, x86 laptops, and Jetson Orin ONNX/TensorRT deployments, the same controller equations operate over a 37x latency range. On Jetson Orin TensorRT under heavy load, the safety2 policy achieves 3.41 ms mean latency, 5.6x faster than fixed-MEDIUM inference, while retaining 74% of its proxy accuracy through near-NANO operation with selective SMALL and MEDIUM locks during VRU-positive windows. Detection-conditioned switching improves SWAS by 25.4% under oracle scoring and 47.3% under detector-derived scoring relative to threshold-only policies under heavy load. Live KITTI evaluation reports per-tier VRU recall of 24.2%, 41.2%, and 59.0%, showing that reactive overrides are fundamentally limited by baseline detector recall.

cs.CV↗

arXiv2020

Airborne dispersion of droplets during coughing: a physical model of viral transmission

The Covid-19 pandemic has focused attention on airborne transmission of viruses. Using realistic air flow simulation, we model droplet dispersion from coughing and study the transmission risk related to SARS-CoV-2. Although most airborne droplets are 8-16 $μ$m in diameter, the droplets with the highest transmission potential are, in fact, 32-40 $μ$m. Use of face masks is therefore recommended for both personal and social protection. We found social distancing effective at reducing transmission potential across all droplet sizes. However, the presence of a human body 1 m away modifies the aerodynamics so that downstream droplet dispersion is enhanced, which has implications on safe distancing in queues. Based on median viral load, we found that an average of 0.55 viral copies is inhaled at 1 m distance per cough. Droplet evaporation results in significant reduction in droplet counts, but airborne transmission remains possible even under low humidity conditions.

physics.flu-dyn↗