arXiv subjects

Tianxiang Zhu

Publications and source records attributed to Tianxiang Zhu.

7 recordsLinked to original sources

arXiv2026

AcademiClaw: When Students Set Challenges for AI Agents

Benchmarks within the OpenClaw ecosystem have thus far evaluated exclusively assistant-level tasks, leaving the academic-level capabilities of OpenClaw largely unexamined. We introduce AcademiClaw, a bilingual benchmark of 80 complex, long-horizon tasks sourced directly from university students' real academic workflows -- homework, research projects, competitions, and personal projects -- that they found current AI agents unable to solve effectively. Curated from 230 student-submitted candidates through rigorous expert review, the final task set spans 25+ professional domains, ranging from olympiad-level mathematics and linguistics problems to GPU-intensive reinforcement learning and full-stack system debugging, with 16 tasks requiring CUDA GPU execution. Each task executes in an isolated Docker sandbox and is scored on task completion by multi-dimensional rubrics combining six complementary techniques, with an independent five-category safety audit providing additional behavioral analysis. Experiments on six frontier models show that even the best achieves only a 55\% pass rate. Further analysis uncovers sharp capability boundaries across task domains, divergent behavioral strategies among models, and a disconnect between token consumption and output quality, providing fine-grained diagnostic signals beyond what aggregate metrics reveal. We hope that AcademiClaw and its open-sourced data and code can serve as a useful resource for the OpenClaw community, driving progress toward agents that are more capable and versatile across the full breadth of real-world academic demands. All data and code are available at https://github.com/GAIR-NLP/AcademiClaw.

cs.AI↗

arXiv2026

ATSim3D: Towards Accurate Thermal Simulator for Heterogeneous 3D-IC Systems Considering Nonlinear Leakage and Conductivity

Thermal simulation plays a fundamental role in the thermal design of integrated circuits, especially 3D ICs. Current simulators require significant runtime for high-resolution simulation, and dismiss the complex nonlinear thermal effects, such as nonlinear thermal conductivity and leakage power. To address these issues, we propose ATSim3D, a thermal simulator for simulating the steady-state temperature profile of nonlinear and heterogeneous 3D IC systems. We utilize the global-local approach, combining a compact thermal model at the global level, and a finite volume method at the local level. We tackle the nonlinear effects with Kirchhoff transformation and iteration. ATSim3D enables local-level parallelization that helps achieve an average speedup of 40x compared to COMSOL, with a relative error <3% and a state-of-the-art resolution of 4096 x 4096, holding promise for enhancing thermal-aware design in 3D ICs.

physics.app-ph↗

arXiv2026

ATSim3.5D: A Multiscale Thermal Simulator for 3.5D-IC Systems based on Nonlinear Multigrid Method

To resolve the rising temperatures in 3.5D-ICs, a thermal-aware design flow becomes increasingly crucial, necessitating an accurate and efficient thermal simulation tool. However, previous tools struggle to handle the unique heterogeneous multiscale structures in 3.5D-ICs and the nonlinear thermal effects caused by high temperatures. In this work, we present a multiscale thermal simulator for 3.5D-ICs. We propose a hybrid tree structure to generate multilevel grids and capture the multiscale features and employ the nonlinear multigrid method for quick solving. Compared to ANSYS Icepak, it exhibits high accuracy (mean absolute relative error <1%, max error $<\SI{2}{\degreeCelsius}$), and efficiency ($80\times$ acceleration), delivering a powerful means to evaluate and refine thermal designs.

physics.app-ph↗

arXiv2025

ATMPlace: Analytical Thermo-Mechanical-Aware Placement Framework for 2.5D-IC

Rising demand in AI and automotive applications is accelerating 2.5D IC adoption, with multiple chiplets tightly placed to enable high-speed interconnects and heterogeneous integration. As chiplet counts grow, traditional placement tools, limited by poor scalability and reliance on slow simulations, must evolve beyond wirelength minimization to address thermal and mechanical reliability, critical challenges in heterogeneous integration. In this paper, we present ATMPlace, the first analytical placer for 2.5D ICs that jointly optimizes wirelength, peak temperature, and operational warpage using physics-based compact models. It generates Pareto optimal placements for systems with dozens of chiplets. Experimental results demonstrate superior performance: 146 percent and 52 percent geometric mean wirelength improvement over TAP 2.5D and TACPlace, respectively, with 3 to 13 percent lower temperature and 5 to 27 percent less warpage, all achieved approximately 10 times faster. The proposed framework is general and can be extended to enable fast, scalable, and reliable design exploration for next-generation 2.5D systems.

math.NA↗

arXiv2025

Self-aligned multilayered nitrogen vacancy diamond nanoparticles for high spatial resolution magnetometry of microelectronic currents

Nitrogen Vacancy diamond nanoparticles (NVNPs) are increasingly integrated with methods for optical detection of magnetic resonance (ODMR), providing new opportunities in magnetic characterization that span the visualization of magnetic fields in microelectronic circuits, environmental sensing and biology. However, only a small number of studies utilize aggregates of NVNPs for surface-wide magnetometry being that spin orientations in aggregate NVNPs are inherently misaligned, precluding their use for proper magnetic field detection compared with expensive monocrystalline diamonds. A postprocessing method for layering NVNPs with aligned NV center orientations can potentially facilitate superior NV magnetometry by allowing sensitive detection combined with simplified probe preparation. We present a novel technology for creating densely stacked monolayers of NVNP with inherent interlayer alignment for sensitive measurement of local magnetic field perturbations in microelectronic traces. We establish spatial characteristics of deposited aggregates and demonstrate their ability to capture magnetic dipoles from conducting microwires via ODMR. Our approach forms a novel accessible protocol that can be used for broad applications in micromagnetometry.

cond-mat.mes-hall↗

arXiv2025

Online Location Planning for AI-Defined Vehicles: Optimizing Joint Tasks of Order Serving and Spatio-Temporal Heterogeneous Model Fine-Tuning

Advances in artificial intelligence (AI) including foundation models (FMs), are increasingly transforming human society, with smart city driving the evolution of urban living.Meanwhile, vehicle crowdsensing (VCS) has emerged as a key enabler, leveraging vehicles' mobility and sensor-equipped capabilities. In particular, ride-hailing vehicles can effectively facilitate flexible data collection and contribute towards urban intelligence, despite resource limitations. Therefore, this work explores a promising scenario, where edge-assisted vehicles perform joint tasks of order serving and the emerging foundation model fine-tuning using various urban data. However, integrating the VCS AI task with the conventional order serving task is challenging, due to their inconsistent spatio-temporal characteristics: (i) The distributions of ride orders and data point-of-interests (PoIs) may not coincide in geography, both following a priori unknown patterns; (ii) they have distinct forms of temporal effects, i.e., prolonged waiting makes orders become instantly invalid while data with increased staleness gradually reduces its utility for model fine-tuning.To overcome these obstacles, we propose an online framework based on multi-agent reinforcement learning (MARL) with careful augmentation. A new quality-of-service (QoS) metric is designed to characterize and balance the utility of the two joint tasks, under the effects of varying data volumes and staleness. We also integrate graph neural networks (GNNs) with MARL to enhance state representations, capturing graph-structured, time-varying dependencies among vehicles and across locations. Extensive experiments on our testbed simulator, utilizing various real-world foundation model fine-tuning tasks and the New York City Taxi ride order dataset, demonstrate the advantage of our proposed method.

cs.LG↗

arXiv2024

MORE-Stress: Model Order Reduction based Efficient Numerical Algorithm for Thermal Stress Simulation of TSV Arrays in 2.5D/3D IC

Thermomechanical stress induced by through-silicon vias (TSVs) plays an important role in the performance and reliability analysis of 2.5D/3D ICs. While the finite element method (FEM) adopted by commercial software can provide accurate simulation results, it is very time- and memory-consuming for large-scale analysis. Over the past decade, the linear superposition method has been utilized to perform fast thermal stress estimations of TSV arrays, but it suffers from a lack of accuracy. In this paper, we propose MORE-Stress, a novel strict numerical algorithm for efficient thermal stress simulation of TSV arrays based on model order reduction. Extensive experimental results demonstrate that our algorithm can realize a 153-504 times reduction in computational time and a 39-115 times reduction in memory usage compared with the commercial software ANSYS, with negligible errors less than 1%. Our algorithm is as efficient as the linear superposition method, with an order of magnitude smaller errors and fast convergence.

cs.CE↗