arXiv subjects

Junkai Wang

Publications and source records attributed to Junkai Wang.

8 recordsLinked to original sources

arXiv2026

Benchmark Radar: A Living Database and Search Engine for AI Benchmarks and Evaluation

Benchmark researchers and developers of large language models (LLMs) and other AI systems need to find relevant evaluations, locate their benchmark datasets and code, and understand the settings behind reported scores. We present Benchmark Radar, a living database and search engine for retrieval and discovery of AI benchmarks, covering LLM evaluation, agentic and tool-use benchmarks, coding, reasoning, safety, and domain-specific evaluations. The system combines daily discovery of benchmark papers, repositories, datasets, and releases with a searchable benchmark catalog, mentions in model cards and technical reports, and score histories. It retains source identities and citations so readers can inspect candidate benchmarks and their evaluation evidence. Daily discovery draws on 37 sources: 13 direct connectors and 24 first-party research and engineering feeds. The catalog contains 1,283 source records drawn from 4 benchmark catalogs and 12,916 numeric observations on 790 records. We describe collection and retrieval, audit the full catalog, and examine benchmark saturation, adoption trends, and the limits of score comparisons. A worked example walks through a complete prior-art search, showing how to query the catalog and inspect benchmark evidence when designing a new evaluation. We release the web dashboard with a benchmark leaderboard, a Pareto frontier view of score against measured use, saturation and trend views, daily feeds, downloadable evidence, a command-line interface (CLI) for offline queries, and reproducible analysis.

cs.AI↗

arXiv2026

Zhinv: Real-time hub-height wind field reconstruction using only local sparse observations

The high proportion of wind power connected to the grid places higher demands on fine-grained knowledge of regional wind fields. Since the wind information directly obtainable in actual operations is mostly sparse, discrete, and irregularly distributed local observations, it is difficult to directly meet the needs of tasks such as wind power regulation, wind resource assessment, and low-altitude environmental perception of continuous regional wind fields. Therefore, we propose Zhinv, an end-to-end reconstruction framework that directly weaves sparse and irregular observations into a fine-grid wind field at hub-height. Experiments in Northeast China, Europe, and Southeast Asia demonstrate that Zhinv can accurately, robustly, and efficiently reconstruct fine-grid wind fields from sparse observations, reducing the error by about 66% compared with Kriging. With local wind-power observations as input, Zhinv enables wind power centers to bypass NWP and complex assimilation processes, supporting direct and real-time wind resource assessment from locally available data.

cs.LG↗

arXiv2026

LeanCat: A Benchmark Suite for Formal Category Theory in Lean (Part I: 1-Categories)

While large language models (LLMs) have demonstrated impressive capabilities in formal theorem proving, current benchmarks fail to adequately measure library-grounded abstraction -- the ability to reason with high-level interfaces and reusable structures central to modern mathematics and software engineering. We introduce LeanCat, a challenging benchmark comprising 100 fully formalized category-theory tasks in Lean. Unlike algebra or arithmetic, category theory serves as a rigorous stress test for structural, interface-level reasoning. Our evaluation reveals a severe abstraction gap: the best state-of-the-art model solves only 12.0% of tasks at pass@4, with performance collapsing from 55.0% on Easy tasks to 0.0% on High-difficulty tasks, highlighting a failure in compositional generalization. To overcome this, we evaluate LeanBridge, a retrieval-augmented agent that employs a retrieve-generate-verify loop. LeanBridge achieves a peak success rate of 24.0% -- doubling the performance of the best static baseline. These results empirically demonstrate that iterative refinement and dynamic library retrieval are not merely optimizations but strict necessities for neuro-symbolic reasoning in abstract domains. LeanCat offers a compact, reusable testbed for tracking progress toward reliable, research-level formalization.

cs.LO↗

arXiv2025

Learning Robust Regions of Attraction Using Rollout-Enhanced Physics-Informed Neural Networks with Policy Iteration

The region of attraction is a key metric of the robustness of systems. This paper addresses the numerical solution of the generalized Zubov's equation, which produces a special Lyapunov function characterizing the robust region of attraction for perturbed systems. To handle the highly nonlinear characteristic of the generalized Zubov's equation, we propose a physics-informed neural network framework that employs a policy iteration training scheme with rollout to approximate the viscosity solution. In addition to computing the optimal disturbance during the policy improvement process, we incorporate neural network-generated value estimates as anchor points to facilitate the training procedure to prevent singularities in both low- and high-dimensional systems. Numerical simulations validate the effectiveness of the proposed approach.

eess.SY↗

arXiv2025

Thermodynamically consistent modelling and simulation of the moving contact line problem in non-isothermal compressible two-phase flows

According to the dynamic van der Waals theory, we propose a thermodynamically consistent model for non-isothermal compressible two-phase flows with contact line motion. In this model, fluid temperature is treated as a primary variable, characterized by the proposed temperature equation instead of being obtained from intermediate variables such as total energy density, internal energy density and entropy density. The hydrodynamic boundary conditions, which represent a generalization of the generalized Navier slip boundary condition in non-isothermal flows, are imposed on the proposed model. We then develop the dimensionless form of the model and prove that it rigorously satisfies the first and second laws of thermodynamics. Two numerical schemes based on the dimensionless system are constructed: one is fully coupled and thermodynamically consistent, namely strictly satisfying the temporally discrete first and second laws of thermodynamics; the other, designed by extending the multiple scalar auxiliary variable approach to entropy production, is decoupled, linear, and unconditionally entropy-stable. Several numerical results are presented to validate the effectiveness and stability of the proposed method.

physics.flu-dyn↗

arXiv2025

Quantum Gravity Meets DESI: Dynamical Dark Energy in Light of the Trans-Planckian Censorship Conjecture

Recent DESI DR2 observations indicate that dark energy has crossed from phantom to quintessence regime, a behavior known as the quintom-B realization. In this work we constrain dynamical dark energy and modified gravity using the swampland Trans-Planckian Censorship Conjecture (TCC), which forbids eternal acceleration since in this case any trans-Planckian quantum fluctuation would eventually stretch beyond the Hubble radius, breaking the applicability of any effective field theory and cosmological techniques. By combining DESI DR2 data with the TCC criterion, we impose tight constraints on the dark energy equation of state and its parameter space in scenarios such as the Chevallier-Polarski-Linder, Barboza-Alcaniz, Jassal-Bagla-Padmanabhan, EXP and LOG parameterizations, significantly constraining the quintom-A behavior. Also we examine models within the framework of $f(T)$ and $f(Q)$ modified gravity theories, demonstrating that TCC is very powerful to constrain or exclude them, a result that indicates the necessity to consider infrared modifications on General Relativity apart from the usual ultraviolet ones. Our findings imply that viable dynamical dark energy scenarios must asymptotically transit to deceleration, shedding light on new physics consistent with both cosmological observations and quantum gravity principles.

astro-ph.CO↗

arXiv2024

Improving Fast Adversarial Training via Self-Knowledge Guidance

Adversarial training has achieved remarkable advancements in defending against adversarial attacks. Among them, fast adversarial training (FAT) is gaining attention for its ability to achieve competitive robustness with fewer computing resources. Existing FAT methods typically employ a uniform strategy that optimizes all training data equally without considering the influence of different examples, which leads to an imbalanced optimization. However, this imbalance remains unexplored in the field of FAT. In this paper, we conduct a comprehensive study of the imbalance issue in FAT and observe an obvious class disparity regarding their performances. This disparity could be embodied from a perspective of alignment between clean and robust accuracy. Based on the analysis, we mainly attribute the observed misalignment and disparity to the imbalanced optimization in FAT, which motivates us to optimize different training data adaptively to enhance robustness. Specifically, we take disparity and misalignment into consideration. First, we introduce self-knowledge guided regularization, which assigns differentiated regularization weights to each class based on its training state, alleviating class disparity. Additionally, we propose self-knowledge guided label relaxation, which adjusts label relaxation according to the training accuracy, alleviating the misalignment and improving robustness. By combining these methods, we formulate the Self-Knowledge Guided FAT (SKG-FAT), leveraging naturally generated knowledge during training to enhance the adversarial robustness without compromising training efficiency. Extensive experiments on four standard datasets demonstrate that the SKG-FAT improves the robustness and preserves competitive clean accuracy, outperforming the state-of-the-art methods.

cs.CV↗

arXiv2023

OceanChat: Piloting Autonomous Underwater Vehicles in Natural Language

In the trending research of fusing Large Language Models (LLMs) and robotics, we aim to pave the way for innovative development of AI systems that can enable Autonomous Underwater Vehicles (AUVs) to seamlessly interact with humans in an intuitive manner. We propose OceanChat, a system that leverages a closed-loop LLM-guided task and motion planning framework to tackle AUV missions in the wild. LLMs translate an abstract human command into a high-level goal, while a task planner further grounds the goal into a task sequence with logical constraints. To assist the AUV with understanding the task sequence, we utilize a motion planner to incorporate real-time Lagrangian data streams received by the AUV, thus mapping the task sequence into an executable motion plan. Considering the highly dynamic and partially known nature of the underwater environment, an event-triggered replanning scheme is developed to enhance the system's robustness towards uncertainty. We also build a simulation platform HoloEco that generates photo-realistic simulation for a wide range of AUV applications. Experimental evaluation verifies that the proposed system can achieve improved performance in terms of both success rate and computation time. Project website: \url{https://sites.google.com/view/oceanchat}

cs.RO↗