arXiv subjects

He Wang

Publications and source records attributed to He Wang.

At least 109 records · Page 6Linked to original sources

arXiv2026

Robust Differentiable Collision Detection for General Objects

Collision detection is a core component of robotics applications such as simulation, control, and planning. Traditional algorithms like GJK+EPA compute witness points (i.e., the closest or deepest-penetration pairs between two objects) but are inherently non-differentiable, preventing gradient flow and limiting gradient-based optimization in contact-rich tasks such as grasping and manipulation. Recent work introduced efficient first-order randomized smoothing to make witness points differentiable; however, their direction-based formulation is restricted to convex objects and lacks robustness for complex geometries. In this work, we propose a robust and efficient differentiable collision detection framework that supports both convex and concave objects across diverse scales and configurations. Our method introduces distance-based first-order randomized smoothing, adaptive sampling, and equivalent gradient transport for robust and informative gradient computation. Experiments on complex meshes from DexGraspNet and Objaverse show significant improvements over existing baselines. Finally, we demonstrate a direct application of our method for dexterous grasp synthesis to refine the grasp quality. The code is available at https://github.com/JYChen18/DiffCollision.

cs.RO↗

arXiv2026

G-LNS: Generative Large Neighborhood Search for LLM-Based Automatic Heuristic Design

While Large Language Models (LLMs) have recently shown promise in Automated Heuristic Design (AHD), existing approaches typically formulate AHD around constructive priority rules or parameterized local search guidance, thereby restricting the search space to fixed heuristic forms. Such designs offer limited capacity for structural exploration, making it difficult to escape deep local optima in complex Combinatorial Optimization Problems (COPs). In this work, we propose G-LNS, a generative evolutionary framework that extends LLM-based AHD to the automated design of Large Neighborhood Search (LNS) operators. Unlike prior methods that evolve heuristics in isolation, G-LNS leverages LLMs to co-evolve tightly coupled pairs of destroy and repair operators. A cooperative evaluation mechanism explicitly captures their interaction, enabling the discovery of complementary operator logic that jointly performs effective structural disruption and reconstruction. Extensive experiments on challenging COP benchmarks, such as Traveling Salesman Problems (TSP) and Capacitated Vehicle Routing Problems (CVRP), demonstrate that G-LNS significantly outperforms LLM-based AHD methods as well as strong classical solvers. The discovered heuristics not only achieve near-optimal solutions with reduced computational budgets but also exhibit robust generalization across diverse and unseen instance distributions.

cs.AI↗

arXiv2026

Provably Efficient and Agile Randomized Q-Learning

While Bayesian-based exploration often demonstrates superior empirical performance compared to bonus-based methods in model-based reinforcement learning (RL), its theoretical understanding remains limited for model-free settings. Existing provable algorithms either suffer from computational intractability or rely on stage-wise policy updates which reduce responsiveness and slow down the learning process. In this paper, we propose a novel variant of Q-learning algorithm, refereed to as RandomizedQ, which integrates sampling-based exploration with agile, step-wise, policy updates, for episodic tabular RL. We establish an $\widetilde{O}(\sqrt{H^5SAT})$ regret bound, where $S$ is the number of states, $A$ is the number of actions, $H$ is the episode length, and $T$ is the total number of episodes. In addition, we present a logarithmic regret bound under a mild positive sub-optimality condition on the optimal Q-function. Empirically, RandomizedQ exhibits outstanding performance compared to existing Q-learning variants with both bonus-based and Bayesian-based exploration on standard benchmarks.

cs.LG↗

arXiv2026

CIM-Tuner: Balancing the Compute and Storage Capacity of SRAM-CIM Accelerator via Hardware-mapping Co-exploration

As an emerging type of AI computing accelerator, SRAM Computing-In-Memory (CIM) accelerators feature high energy efficiency and throughput. However, various CIM designs and under-explored mapping strategies impede the full exploration of compute and storage balancing in SRAM-CIM accelerator, potentially leading to significant performance degradation. To address this issue, we propose CIM-Tuner, an automatic tool for hardware balancing and optimal mapping strategy under area constraint via hardware-mapping co-exploration. It ensures universality across various CIM designs through a matrix abstraction of CIM macros and a generalized accelerator template. For efficient mapping with different hardware configurations, it employs fine-grained two-level strategies comprising accelerator-level scheduling and macro-level tiling. Compared to prior CIM mapping, CIM-Tuner's extended strategy space achieves 1.58$\times$ higher energy efficiency and 2.11$\times$ higher throughput. Applied to SOTA CIM accelerators with identical area budget, CIM-Tuner also delivers comparable improvements. The simulation accuracy is silicon-verified and CIM-Tuner tool is open-sourced at https://github.com/champloo2878/CIM-Tuner.git.

cs.AR↗

arXiv2026

Dressed-state relaxation in coupled qubits as a source of two-qubit gate errors

Understanding error mechanisms in two-qubit gate operations is essential for building high-fidelity quantum processors. While prior studies predominantly treat dephasing noise as either Markovian or predominantly low-frequency, realistic qubit environments exhibit structured, frequency-dependent spectra. Here we demonstrate that noise at frequencies matching the dressed-state energy splitting--set by the inter-qubit coupling strength g--induces a distinct relaxation channel that degrades gate performance. Through combined theoretical analysis and experimental verification on superconducting qubits with engineered noise spectra, we show that two-qubit gate errors scale predictably with the noise power spectral density at frequency 2g, extending the concept of $T_{1ρ}$ relaxation to interacting systems. This frequency-selective relaxation mechanism, universal across platforms, enriches our understanding of decoherence pathways during gate operations. The same mechanism sets coherence limits for dual-rail or singlet-triplet encodings.

quant-ph↗

arXiv2026

Mitigating Measurement Crosstalk via Pulse Shaping

Quantum error correction protocols require rapid and repeated qubit measurements. While multiplexed readout in superconducting quantum systems improves efficiency, fast probe pulses introduce spectral broadening, leading to signal leakage into neighboring readout resonators. This crosstalk results in qubit dephasing and degraded readout fidelity. Here, we introduce a pulse shaping technique inspired by the derivative removal by adiabatic gate (DRAG) protocol to suppress measurement crosstalk during fast readout. By engineering a spectral notch at neighboring resonator frequencies, the method effectively mitigates spurious signal interference. Our approach integrates seamlessly with existing readout architectures, enabling fast, low-crosstalk multiplexed measurements without additional hardware overhead - a critical advancement for scalable quantum computing.

quant-ph↗

arXiv2026

Hard Constraint Projection in a Physics Informed Neural Network

In this work, we embed hard constraints in a physics informed neural network (PINN) which predicts solutions to the 2D incompressible Navier Stokes equations. We extend the hard constraint method introduced by Chen et al. (arXiv:2012.06148) from a linear PDE to a strongly non-linear PDE. The PINN is used to estimate the stream function and pressure of the fluid, and by differentiating the stream function we can recover an incompressible velocity field. An unlearnable hard constraint projection (HCP) layer projects the predicted velocity and pressure to a hyperplane that admits only exact solutions to a discretised form of the governing equations.

physics.flu-dyn↗

arXiv2026

Artificial intelligence pioneers the double-strangeness factory

Artificial intelligence (AI) is transforming not only our daily experiences but also the technological development landscape and scientific research. In this study, we pioneered the application of AI in double-strangeness hypernuclear studies. These studies which investigate quantum systems with strangeness via hyperon interactions provide insights into fundamental baryon-baryon interactions and contribute to our understanding of the nuclear force and composition of neutron star cores. Specifically, we report the observation of a double hypernucleus in nuclear emulsion achieved via innovative integration of machine learning techniques. The proposed methodology leverages generative AI and Monte Carlo simulations to produce training datasets combined with object detection AI for effective event identification. Based on the kinematic analysis and charge identification, the observed event was uniquely identified as the production and decay of resulting from Ξ- capture by 14N in the nuclear emulsion. Assuming capture in the atomic 3D state, the binding energy of the two Λ hyperons in 13BΛΛ, BΛΛ, was determined as 25.57 +- 1.18(stat.) +- 0.07(syst.) MeV. The ΛΛ interaction energy obtained was 2.83 +- 1.18(stat.) +- 0.14(syst.) MeV. This study marks a new era in double-strangeness research.

nucl-ex↗

arXiv2026

Gravitational wave standard sirens: A brief review of cosmological parameter estimation

Gravitational wave (GW) observations are expected to serve as a powerful and independent probe of the expansion history of the universe. By providing direct and calibration-free measurements of luminosity distances through waveform analysis, GWs provide a fundamentally different and potentially more robust approach to measuring cosmic-scale distances compared to traditional electromagnetic (EM) observations, which is known as the standard siren method. In this review, we present an overview of recent developments in GW standard siren cosmology, including up-to-date $H_0$ constraints, and prospects for constraining cosmological parameters using future GW detections. A central focus of this review is the unique ability of GW observations to break cosmological parameter degeneracies inherent in the EM observations. We also briefly highlight the impact of systematic uncertainties, such as detector calibration, weak lensing, peculiar velocities, and host-galaxy catalog completeness, and corresponding potential mitigation strategies, which currently limit the constraint precision of cosmological parameters. Looking forward, we highlight the importance of combining GW standard sirens with other emerging late-universe cosmological probes such as fast radio bursts, 21 cm intensity mapping, and strong gravitational lensing to forge a precise cosmological probe for exploring the late universe.

astro-ph.CO↗

arXiv2025

MME-CC: A Challenging Multi-Modal Evaluation Benchmark of Cognitive Capacity

As reasoning models scale rapidly, the essential role of multimodality in human cognition has come into sharp relief, driving a growing need to probe vision-centric cognitive behaviors. Yet, existing multimodal benchmarks either overemphasize textual reasoning or fall short of systematically capturing vision-centric cognitive behaviors, leaving the cognitive capacity of MLLMs insufficiently assessed. To address this limitation, we introduce MME-CC (Multi-Modal Evaluation benchmark of Cognitive Capacity), a vision-grounded benchmark that organizes 11 representative reasoning tasks into three fundamental categories of visual information: spatial, geometric, and knowledge-based reasoning, and provides fine-grained analyses of MLLMs' cognitive capacity across these dimensions. Based on MME-CC, we conduct extensive experiments over 16 representative MLLMs. Our study reveals that closed-source models currently lead overall (e.g., 42.66 for Gemini-2.5-Pro vs. 30.45 for GLM-4.5V), while spatial and geometric reasoning remain broadly weak (less than or equal to 30%). We further identify common error patterns, including orientation mistakes, fragile cross-view identity persistence, and poor adherence to counterfactual instructions, and observe that Chain-of-Thought typically follows a three-stage process (extract -> reason -> verify) with heavy reliance on visual extraction. We hope this work catalyzes a shift toward treating the cognitive capacity of MLLMs as central to both evaluation and model design.

cs.CL↗

arXiv2025

Unleashing Humanoid Reaching Potential via Real-world-Ready Skill Space

Humans possess a large reachable space in the 3D world, enabling interaction with objects at varying heights and distances. However, realizing such large-space reaching on humanoids is a complex whole-body control problem and requires the robot to master diverse skills simultaneously-including base positioning and reorientation, height and body posture adjustments, and end-effector pose control. Learning from scratch often leads to optimization difficulty and poor sim2real transferability. To address this challenge, we propose Real-world-Ready Skill Space (R2S2). Our approach begins with a carefully designed skill library consisting of real-world-ready primitive skills. We ensure optimal performance and robust sim2real transfer through individual skill tuning and sim2real evaluation. These skills are then ensembled into a unified latent space, serving as a structured prior that helps task execution in an efficient and sim2real transferable manner. A high-level planner, trained to sample skills from this space, enables the robot to accomplish real-world goal-reaching tasks. We demonstrate zero-shot sim2real transfer and validate R2S2 in multiple challenging goal-reaching scenarios.

cs.RO↗

arXiv2025

Towards Modality- and Sampling-Universal Learning Strategies for Accelerating Cardiovascular Imaging: Summary of the CMRxRecon2024 Challenge

Cardiovascular health is vital to human well-being, and cardiac magnetic resonance (CMR) imaging is considered the {clinical reference standard} for diagnosing cardiovascular disease. However, its adoption is hindered by long scan times, complex contrasts, and inconsistent quality. While deep learning methods perform well on specific CMR imaging {sequences}, they often fail to generalize across modalities and sampling schemes. The lack of benchmarks for high-quality, fast CMR image reconstruction further limits technology comparison and adoption. The CMRxRecon2024 challenge, attracting over 200 teams from 18 countries, addressed these issues with two tasks: generalization to unseen {modalities} and robustness to diverse undersampling patterns. We introduced the largest public multi-{modality} CMR raw dataset, an open benchmarking platform, and shared code. Analysis of the best-performing solutions revealed that prompt-based adaptation and enhanced physics-driven consistency enabled strong cross-scenario performance. These findings establish principles for generalizable reconstruction models and advance clinically translatable AI in cardiovascular imaging.

eess.IV↗

arXiv2025

StockMem: An Event-Reflection Memory Framework for Stock Forecasting

Stock price prediction is challenging due to market volatility and its sensitivity to real-time events. While large language models (LLMs) offer new avenues for text-based forecasting, their application in finance is hindered by noisy news data and the lack of explicit answers in text. General-purpose memory architectures struggle to identify the key drivers of price movements. To address this, we propose StockMem, an event-reflection dual-layer memory framework. It structures news into events and mines them along two dimensions: horizontal consolidation integrates daily events, while longitudinal tracking captures event evolution to extract incremental information reflecting market expectation discrepancies. This builds a temporal event knowledge base. By analyzing event-price dynamics, the framework further forms a reflection knowledge base of causal experiences. For prediction, it retrieves analogous historical scenarios and reasons with current events, incremental data, and past experiences. Experiments show StockMem outperforms existing memory architectures and provides superior, explainable reasoning by tracing the information chain affecting prices, enhancing decision transparency in financial forecasting.

cs.AI↗

arXiv2025

Wall-Proximity Matters: Understanding the Effect of Device Placement with Respect to the Wall for Indoor Wi-Fi Sensing

Wi-Fi sensing has been extensively explored for various applications, including vital sign monitoring, human activity recognition, indoor localization, and tracking. However, practical implementation in real-world scenarios is hindered by unstable sensing performance and limited knowledge of wireless sensing coverage. While previous works have aimed to address these challenges, they have overlooked the impact of walls on dynamic sensing capabilities in indoor environments. To fill this gap, we present a theoretical model that accounts for the effect of wall-device distance on sensing coverage. By incorporating both the wall-reflected path and the line-of-sight (LoS) path for dynamic signals, we develop a comprehensive sensing coverage model tailored for indoor environments. This model demonstrates that strategically deploying the transmitter and receiver in proximity to the wall within a specific range can significantly expand sensing coverage. We assess the performance of our model through experiments in respiratory monitoring and stationary crowd counting applications, showcasing a notable 11.2% improvement in counting accuracy. These findings pave the way for optimized deployment strategies in Wi-Fi sensing, facilitating more effective and accurate sensing solutions across various applications.

cs.NI↗

arXiv2025

KSHSeek: Data-Driven Approaches to Mitigating and Detecting Knowledge-Shortcut Hallucinations in Generative Models

The emergence of large language models (LLMs) has significantly advanced the development of natural language processing (NLP), especially in text generation tasks like question answering. However, model hallucinations remain a major challenge in natural language generation (NLG) tasks due to their complex causes. We systematically expand on the causes of factual hallucinations from the perspective of knowledge shortcuts, analyzing hallucinations arising from correct and defect-free data and demonstrating that knowledge-shortcut hallucinations are prevalent in generative models. To mitigate this issue, we propose a high similarity pruning algorithm at the data preprocessing level to reduce spurious correlations in the data. Additionally, we design a specific detection method for knowledge-shortcut hallucinations to evaluate the effectiveness of our mitigation strategy. Experimental results show that our approach effectively reduces knowledge-shortcut hallucinations, particularly in fine-tuning tasks, without negatively impacting model performance in question answering. This work introduces a new paradigm for mitigating specific hallucination issues in generative models, enhancing their robustness and reliability in real-world applications.

cs.CL↗

arXiv2025

Automated Algorithmic Discovery for Scientific Computing through LLM-Guided Evolutionary Search: A Case Study in Gravitational-Wave Detection

Automated algorithm discovery in scientific computing faces fundamental challenges: vast design spaces with expensive evaluations, domain-specific physical constraints requiring expert knowledge, and the necessity for interpretable solutions that scientists can validate and understand. We present the Evo-MCTS (Evolutionary Monte Carlo Tree Search) framework, integrating large language models (LLMs) with tree-structured evolutionary search for interpretable algorithm discovery. Evo-MCTS combines reflective code synthesis leveraging LLM domain knowledge, multi-scale evolutionary operations on structured code representations, and interpretable algorithmic pathways emerging from tree-guided exploration. When applied to gravitational wave detection-a challenging domain with continuous parameter spaces and strict physical constraints-Evo-MCTS achieves 20.2% improvement over domain-specific methods and 59.1% over LLM-based optimization frameworks. This improvement arises from its ability to consistently converge toward interpretable algorithmic structures that integrate multiple functional components. Our domain-agnostic architecture establishes a generalizable methodology for automated algorithm discovery in scientific computing, where algorithmic transparency and physical validity are as essential as performance optimization.

cs.AI↗

arXiv2025

PC-Diffusion: Aligning Diffusion Models with Human Preferences via Preference Classifier

Diffusion models have achieved remarkable success in conditional image generation, yet their outputs often remain misaligned with human preferences. To address this, recent work has applied Direct Preference Optimization (DPO) to diffusion models, yielding significant improvements.~However, DPO-like methods exhibit two key limitations: 1) High computational cost,due to the entire model fine-tuning; 2) Sensitivity to reference model quality}, due to its tendency to introduce instability and bias. To overcome these limitations, we propose a novel framework for human preference alignment in diffusion models (PC-Diffusion), using a lightweight, trainable Preference Classifier that directly models the relative preference between samples. By restricting preference learning to this classifier, PC-Diffusion decouples preference alignment from the generative model, eliminating the need for entire model fine-tuning and reference model reliance.~We further provide theoretical guarantees for PC-Diffusion:1) PC-Diffusion ensures that the preference-guided distributions are consistently propagated across timesteps. 2)The training objective of the preference classifier is equivalent to DPO, but does not require a reference model.3) The proposed preference-guided correction can progressively steer generation toward preference-aligned regions.~Empirical results show that PC-Diffusion achieves comparable preference consistency to DPO while significantly reducing training costs and enabling efficient and stable preference-guided generation.

cs.CV↗

arXiv2025

Source Confusion of Massive Black Hole Binaries for the Taiji Mission

We systematically investigate the source confusion of massive black hole binaries (MBHBs) for the Taiji space-based gravitational wave mission. Source confusion, arising from the overlap of signals in both time and frequency domains, can degrade parameter recovery accuracy. To assess this effect, we simulate three representative models MBHB populations to estimate event overlap events. Assuming 100 detections per year, only 0.31-4.2 overlaps are expected annually. Based on Fisher information matrix with the $\texttt{IMRPhenomD}$ and $\texttt{IMRPhenomHM}$ waveform models, we find that overlap significantly enlarges parameter uncertainties, while the inclusion of higher-order modes (HMs) effectively mitigates this effect. Severe confusion ($Δ\mathcal{M}_z / \mathcal{M}_z<$ 0.2%) occurs in fewer than 0.14% across the three population models. The full Bayesian analysis further corroborates the Fisher predictions, and also reveals that HMs help break key parameter degeneracies, with or without signal overlap. These findings underscore the importance of incorporating HMs for accurate inference in future space-based observations.

gr-qc↗