arXiv subjects

Wentao Liu

Publications and source records attributed to Wentao Liu.

At least 55 records · Page 3Linked to original sources

arXiv2025

Influence of dark matter on quantum entanglement and coherence in curved spacetime

Dark matter (DM) remains undetected, and developing theoretical models such as the promising perfect fluid dark matter (PFDM) is a key challenge in modern cosmology. In this work, we investigate the quantum characteristics of PFDM by analyzing the behavior of quantum entanglement and coherence for both fermionic and bosonic fields near a Schwarzschild black hole embedded in a PFDM halo. Our results reveal that PFDM can either enhance or degrade quantum entanglement and coherence, depending sensitively on its density. Notably, bosonic entanglement shows greater susceptibility to PFDM effects compared to fermionic entanglement, while fermionic coherence exhibits a stronger dependence on PFDM than its bosonic counterpart. These findings highlight the necessity of selecting appropriate quantum probes for DM detection based on the type of quantum resources, as different quantum fields exhibit significantly different responses to PFDM in curved spacetime.

gr-qc↗

arXiv2025

KptLLM++: Towards Generic Keypoint Comprehension with Large Language Model

The emergence of Multimodal Large Language Models (MLLMs) has revolutionized image understanding by bridging textual and visual modalities. However, these models often struggle with capturing fine-grained semantic information, such as the precise identification and analysis of object keypoints. Keypoints, as structure-aware, pixel-level, and compact representations of objects, particularly articulated ones, play a crucial role in applications such as fine-grained image analysis, object retrieval, and behavior recognition. In this paper, we propose KptLLM++, a novel multimodal large language model that specifically designed for generic keypoint comprehension through the integration of diverse input modalities guided by user-defined instructions. By unifying keypoint detection across varied contexts, KptLLM++ establishes itself as an advanced interface, fostering more effective human-AI collaboration. The model is built upon a novel identify-then-detect paradigm, which first interprets keypoint semantics and subsequently localizes their precise positions through a structured chain-of-thought reasoning mechanism. To push the boundaries of performance, we have scaled up the training dataset to over 500K samples, encompassing diverse objects, keypoint categories, image styles, and scenarios with complex occlusions. This extensive scaling enables KptLLM++ to unlock its potential, achieving remarkable accuracy and generalization. Comprehensive experiments on multiple keypoint detection benchmarks demonstrate its state-of-the-art performance, underscoring its potential as a unified solution for fine-grained image understanding and its transformative implications for human-AI interaction.

cs.CV↗

arXiv2025

A Large Language Model for Chemistry and Retrosynthesis Predictions

Large language models (LLM) have achieved impressive progress across a broad range of general-purpose tasks, but their effectiveness in chemistry remains limited due to scarce domain-specific datasets and the demand for precise symbolic and structural reasoning. Here we introduce ECNU-ChemGPT(name after East China Normal University), a chemistry-specialized LLM engineered for deep chemical knowledge understanding and accurate retrosynthetic route planning. Our approach is distinguished by four key strategies: structured prompt-based knowledge distillation from authoritative chemistry textbooks to construct a high-quality question-answering dataset; domain-specific prompt engineering using curated chemical keywords, combined with LLMs APIs for data derivation and knowledge distillation; large-scale fine-tuning on a meticulously cleaned and enriched Pistachio reaction dataset to enhance retrosynthesis prediction accuracy; and integration of BrainGPT, a dynamic multi-model scheduling framework that enables task-specific invocation of multiple specialized models trained for diverse chemistry-related tasks. ECNU-ChemGPT exhibits superior performance on chemistry question-answering and retrosynthetic planning benchmarks, outperforming leading general-purpose models-including Deepseek-R1, Qwen-2.5, and GPT-4o. In retrosynthesis, it achieves a Top-1 accuracy of 68.3% on the USPTO_50K dataset and successfully reconstructed 13 complete experimental pathways for real-world drug molecules from medicinal chemistry journals. These results underscore the effectiveness of domain-adapted fine-tuning combined with dynamic multi-model task scheduling, providing a scalable and robust solution for chemical knowledge question answering and retrosynthetic planning.

physics.chem-ph↗

arXiv2025

Observer-Based Distributed Model Predictive Control for String-Stable Multi-vehicle Systems with Markovian Switching Topology

Switching communication topologies can cause instability in vehicle platoons, as vehicle information may be lost during the dynamic switching process. This highlights the need to design a controller capable of maintaining the stability of vehicle platoons under dynamically changing topologies. However, capturing the dynamic characteristics of switching topologies and obtaining complete vehicle information for controller design while ensuring stability remains a significant challenge. In this study, we propose an observer-based distributed model predictive control (DMPC) method for vehicle platoons under directed Markovian switching topologies. Considering the stochastic nature of the switching topologies, we model the directed switching communication topologies using a continuous-time Markov chain. To obtain the leader vehicle's information for controller design, we develop a fully distributed adaptive observer that can quickly adapt to the randomly switching topologies, ensuring that the observed information is not affected by the dynamic topology switches. Additionally, a sufficient condition is derived to guarantee the mean-square stability of the observer. Furthermore, we construct the DMPC terminal update law based on the observer and formulate a string stability constraint based on the observed information. Numerical simulations demonstrate that our method can reduce tracking errors while ensuring string stability.

eess.SY↗

arXiv2025

Entangled Unruh-DeWitt detectors amplify quantum coherence

We explore the quantum coherence between a pair of entangled Unruh-DeWitt detectors, interacting with a quantum field, using a nonperturbative approach in a (3+1)-dimensional Minkowski spacetime with instantaneous switching ($δ$-switching). It is intriguing to observe that for a maximally entangled state, increasing the coupling strength enhances the detectors' initial quantum coherence while simultaneously causing a monotonic decrease in their initial entanglement. This reveals a remarkable phenomenon: through nonperturbative interactions, entangled Unruh-DeWitt detectors can exhibit a dual effect-amplifying quantum coherence while degrading quantum entanglement. This finding stands in stark contrast to previous studies based on perturbative methods or Gaussian switching functions, which generally concluded that interactions between detectors and the field lead to a simultaneous degradation of quantum coherence and entanglement due to environmental decoherence. Notably, while initially separable detectors successfully harvest quantum coherence from the vacuum, entanglement extraction remains fundamentally prohibited. These contrasting behaviors underscore the fundamental distinction between coherence and entanglement as quantum resources, and highlight their complementary roles in field-detector interactions.

quant-ph↗

arXiv2025

Harvesting correlations from BTZ black hole coupled to a Lorentz-violating vector field

In this paper, we investigate the effects of Lorentz violation on correlations harvesting, specifically focusing on the harvested entanglement and harvested mutual information between two Unruh-DeWitt detectors interacting with a quantum field in the Lorentz-violating BTZ-like black hole spacetime. Our findings reveal that Lorentz symmetry breaking has contrasting impacts on entanglement harvesting and mutual information harvesting in BTZ backgrounds: it enhances mutual information harvesting while suppressing entanglement harvesting. This phenomenon suggests that the increase in total correlations in Lorentz-violating vector field backgrounds with gravitational coupling is predominantly driven by classical components, with quantum correlations contributing less to the overall mutual information. These results indicate that Lorentz violation, as a quantum property of spacetime, may impose intrinsic constraints on the quantum information capacity encoded in spacetime due to competition among quantum degrees of freedom for resources. Furthermore, Lorentz symmetry breaking expands the \textit{entanglement shadow} region, further demonstrating its disruptive effect on quantum correlations.

gr-qc↗

arXiv2025

Lorentz violation signatures in the low-energy sector of Hořava gravity from black hole shadow observations

In this paper, we use the Hořava gravity model and EHT observations of supermassive black holes (BHs) to investigate signatures of Lorentz violation in real astrophysical environments. The Lorentz violation in the rotating Hořava BH spacetime are confined to the strong gravitational field region, being induced by the BH's rotation. Due to the non-separability of the photon motion equations in this spacetime, we employed a numerical backward ray-tracing method to generate shadow images for various BH parameters. Subsequently, we extracted coordinate positions characterizing the shadow shape from high-pixel images to evaluate the parameter space of the BH. When evaluating M87*, Lorentz violation can occur with arbitrary strength. However, for Sgr A*, we can impose certain parameter constraints on Lorentz violation. These constraints depend on the BH's spin. If future observations confirm Sgr A*'s spin parameter less than 0.81 at maximum inclination, current EHT results would challenge general relativity and support Lorentz violation in low-energy regimes.

gr-qc↗

arXiv2025

Thermodynamic topological classes of the rotating, accelerating black holes

In this paper, we investigate the topological numbers for the rotating, accelerating neutral black hole and its AdS extension, as well as the rotating, accelerating charged black hole and its AdS extension. We find that the topological number of an asymptotically flat accelerating black hole consistently differs by one from that of its non-accelerating counterpart. Furthermore, we show that for an asymptotically AdS accelerating black hole, the topological number is reduced by one compared to its non-accelerating AdS counterpart. In addition, we demonstrate that within the framework of general relativity, the acceleration parameter and the negative cosmological constant each independently add one to the topological number. However, when both factors are present, their effects neutralize each other, resulting in no overall change to the topological number.

hep-th↗

arXiv2025

Harmonizing Visual Representations for Unified Multimodal Understanding and Generation

Unifying visual understanding and generation within a single multimodal framework remains a significant challenge, as the two inherently heterogeneous tasks require representations at different levels of granularity. Current approaches that utilize vector quantization (VQ) or variational autoencoders (VAE) for unified visual representation prioritize intrinsic imagery features over semantics, compromising understanding performance. In this work, we take inspiration from masked image modelling (MIM) that learns rich semantics via a mask-and-reconstruct pre-training and its successful extension to masked autoregressive (MAR) image generation. A preliminary study on the MAR encoder's representation reveals exceptional linear probing accuracy and precise feature response to visual concepts, which indicates MAR's potential for visual understanding tasks beyond its original generation role. Based on these insights, we present \emph{Harmon}, a unified autoregressive framework that harmonizes understanding and generation tasks with a shared MAR encoder. Through a three-stage training procedure that progressively optimizes understanding and generation capabilities, Harmon achieves state-of-the-art image generation results on the GenEval, MJHQ30K and WISE benchmarks while matching the performance of methods with dedicated semantic encoders (e.g., Janus) on image understanding benchmarks. Our code and models will be available at https://github.com/wusize/Harmon.

cs.CV↗

arXiv2025

F-LMM: Grounding Frozen Large Multimodal Models

Endowing Large Multimodal Models (LMMs) with visual grounding capability can significantly enhance AIs' understanding of the visual world and their interaction with humans. However, existing methods typically fine-tune the parameters of LMMs to learn additional segmentation tokens and overfit grounding and segmentation datasets. Such a design would inevitably cause a catastrophic diminution in the indispensable conversational capability of general AI assistants. In this paper, we comprehensively evaluate state-of-the-art grounding LMMs across a suite of multimodal question-answering benchmarks, observing drastic performance drops that indicate vanishing general knowledge comprehension and weakened instruction following ability. To address this issue, we present F-LMM -- grounding frozen off-the-shelf LMMs in human-AI conversations -- a straightforward yet effective design based on the fact that word-pixel correspondences conducive to visual grounding inherently exist in the attention mechanism of well-trained LMMs. Using only a few trainable CNN layers, we can translate word-pixel attention weights to mask logits, which a SAM-based mask refiner can further optimise. Our F-LMM neither learns special segmentation tokens nor utilises high-quality grounded instruction-tuning data, but achieves competitive performance on referring expression segmentation and panoptic narrative grounding benchmarks while completely preserving LMMs' original conversational ability. Additionally, with instruction-following ability preserved and grounding ability obtained, F-LMM can be directly applied to complex tasks like reasoning segmentation, grounded conversation generation and visual chain-of-thought reasoning. Our code can be found at https://github.com/wusize/F-LMM.

cs.CV↗

arXiv2025

Novel topological classes in black hole thermodynamics

By viewing black hole solutions as topological defects in thermodynamic parameter space, we unveil a novel topological class and two new topological subclasses, respectively denoted $W^{0-\leftrightarrow 1+}$, $\overline{W}^{1+}$, and $\hat{W}^{1+}$, that extend beyond the four established categories proposed by Wei et al. [Phys. Rev. D 110, L081501 (2024)]. Within the newly identified class and these two novel subclasses, the innermost small black hole states exhibit a distinct sequence of unstable, stable, and stable behaviours, while the outermost large black hole states display a uniform pattern of stable behaviours. These classifications indicate thermodynamic properties in both the low and high Hawking temperature regimes that are strikingly different from the previously known four topological classes. In particular, we demonstrate that static charged AdS black holes in gauged supergravity exhibit an intricate thermodynamic evolution that is notably distinct from that of the Reissner-Nordström anti-de Sitter (RN-AdS) black hole. From a topological perspective, we emphasize the advantages and potential of investigating thermodynamic phase transitions in these black hole spacetimes, an area that has been rarely explored in previous research. Our findings not only enrich and sharpen the framework of topological classifications in black hole thermodynamics, but also represent a significant stride toward unraveling the fundamental nature of black holes and gravity.

hep-th↗

arXiv2025

Autonomous Guidewire Navigation for Robot-assisted Endovascular Interventions: A Knowledge-Driven Visual Guidance Approach

Autonomous robots for endovascular interventions hold significant potential to enhance procedural safety and reliability by navigating guidewires with precision, minimizing human error, and reducing surgical time. However, existing methods of guidewire navigation rely on manual demonstration data and have a suboptimal success rate. In this work, we propose a knowledge-driven visual guidance (KVG) method that leverages available visual information from interventional imaging to facilitate guidewire navigation. Our approach integrates image segmentation and detection techniques to extract surgical knowledge, including vascular maps and guidewire positions. We introduce BDA-star, a novel path planning algorithm with boundary distance constraints, to optimize trajectory planning for guidewire navigation. To validate the method, we developed the KVD-Reinforcement Learning environment, where observations consist of real-time guidewire feeding images highlighting the guidewire tip position and the planned path. We proposed a reward function based on the distances from both the guidewire tip to the planned path and the target to evaluate the agent's actions.Additionally, to address stability issues and slow convergence rates associated with direct learning from raw pixels, we incorporated a pre-trained convolutional neural network into the policy network for feature extraction. Experiments conducted on the aortic simulation autonomous guidewire navigation platform demonstrated that the proposed method, targeting the left subclavian artery, left carotid artery and the brachiocephalic artery, achieved a 100\% guidewire navigation success rate, along with reduced movement and retraction distances and trajectories tend to the center of the vessels.

cs.RO↗

arXiv2025

Light rings and shadows of static black holes in effective quantum gravity II: A new solution without Cauchy horizons

Among the three known types of static solutions proposed within the Hamiltonian constraint approach to effective quantum gravity (EQG), the first two have been extensively investigated, whereas the third type-which preserves general covariance, is free of Cauchy horizons, and was only recently obtained-remains relatively unexplored. This solution can describe a black hole with an event horizon for certain parameter ranges, or a horizonless compact object beyond those ranges. In this paper, we focus on the third type and show that its light rings feature both stable and unstable branches, and that the black hole shadow size grows with the quantum parameter-unlike in the first two types. However, when we account for both the shadow and the lensing ring, the overall behavior closely resembles that of the second type, in which an increasing quantum parameter leads to a larger portion of the lensing ring being occupied by the shadow. This feature can serve as a hallmark of black holes in EQG, offering a potential way to distinguish them from their GR counterparts. Remarkably, the parameter ranges under which the solution remains a black hole are highly consistent with the current observational constraints on black hole shadows, lending strong support to the classification of the third type of compact object in EQG as a black hole endowed with an event horizon.

gr-qc↗

arXiv2025

Observational signature of Lorentz violation in acceleration radiation

In recent years, Lorentz violation (LV) has emerged as a vibrant area of research in fundamental physics. Despite predictions from quantum gravity theories that Lorentz symmetry may break down at Planck-scale energies, which are currently beyond experimental reach, its low-energy signatures could still be detectable through alternative methods. In this paper, we propose a quantum optical approach to investigate potential LV effects on the acceleration radiation of a freely falling atom within a black hole spacetime coupled to a Lorentz-violating vector field. Our proposed experimental setup employs a Casimir-type apparatus, wherein a two-level atom serves as a dipole detector, enabling its interaction with the field to be modeled using principles from quantum optics. We demonstrate that LV can introduce distinct quantum signatures into the radiation flux, thereby significantly modulating particle emission rates. It is found that while LV effects are negligible at high mode frequencies, they become increasingly pronounced at lower frequencies. This suggests that detecting LV at low-energy scales may depend on advancements in low-frequency observational techniques or detectors.

gr-qc↗

arXiv2025

Mathematical Language Models: A Survey

In recent years, there has been remarkable progress in leveraging Language Models (LMs), encompassing Pre-trained Language Models (PLMs) and Large-scale Language Models (LLMs), within the domain of mathematics. This paper conducts a comprehensive survey of mathematical LMs, systematically categorizing pivotal research endeavors from two distinct perspectives: tasks and methodologies. The landscape reveals a large number of proposed mathematical LLMs, which are further delineated into instruction learning, tool-based methods, fundamental CoT techniques, advanced CoT methodologies and multi-modal methods. To comprehend the benefits of mathematical LMs more thoroughly, we carry out an in-depth contrast of their characteristics and performance. In addition, our survey entails the compilation of over 60 mathematical datasets, including training datasets, benchmark datasets, and augmented datasets. Addressing the primary challenges and delineating future trajectories within the field of mathematical LMs, this survey is poised to facilitate and inspire future innovation among researchers invested in advancing this domain.

cs.CL↗

arXiv2024

Lorentz violation alleviates gravitationally induced entanglement degradation

Lorentz violation is a significant phenomenon in the framework of quantum physics, with implications for fundamental symmetries. In this paper, we explore the effects of Lorentz violation on quantum entanglement through a black hole spacetime that is coupled with a Lorentz-violating field. We establish the relationship between the Hartle-Hawking vacuum state and the Boulware number states for this case, and employ the near horizon approximation in an appropriate form to rewrite the black hole metric into a Rindler-like form. Subsequently, using this revised metric, the analytical forms of logarithmic negativity and mutual information are derived and plotted as functions of Rob's distance from the $ r=0 $ point. Based on the results, we find that the coupling between spacetime and the Lorentz-violating vector field alleviates gravity-induced entanglement degradation. At high mode frequencies, the effects of Lorentz violation are negligible, but they become significant at low frequencies. This suggests that investigating Lorentz violation at astrophysical scales requires low-frequency detectors, as the low energy of these fields enhances the significance of the Lorentz-violating field's non-zero vacuum expectation value.

gr-qc↗

arXiv2024

AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision Tasks

Automated machine learning (AutoML) is a collection of techniques designed to automate the machine learning development process. While traditional AutoML approaches have been successfully applied in several critical steps of model development (e.g. hyperparameter optimization), there lacks a AutoML system that automates the entire end-to-end model production workflow for computer vision. To fill this blank, we propose a novel request-to-model task, which involves understanding the user's natural language request and execute the entire workflow to output production-ready models. This empowers non-expert individuals to easily build task-specific models via a user-friendly language interface. To facilitate development and evaluation, we develop a new experimental platform called AutoMMLab and a new benchmark called LAMP for studying key components in the end-to-end request-to-model pipeline. Hyperparameter optimization (HPO) is one of the most important components for AutoML. Traditional approaches mostly rely on trial-and-error, leading to inefficient parameter search. To solve this problem, we propose a novel LLM-based HPO algorithm, called HPO-LLaMA. Equipped with extensive knowledge and experience in model hyperparameter tuning, HPO-LLaMA achieves significant improvement of HPO efficiency. Dataset and code are available at https://github.com/yang-ze-kang/AutoMMLab.

cs.LG↗

arXiv2024

NADER: Neural Architecture Design via Multi-Agent Collaboration

Designing effective neural architectures poses a significant challenge in deep learning. While Neural Architecture Search (NAS) automates the search for optimal architectures, existing methods are often constrained by predetermined search spaces and may miss critical neural architectures. In this paper, we introduce NADER (Neural Architecture Design via multi-agEnt collaboRation), a novel framework that formulates neural architecture design (NAD) as a LLM-based multi-agent collaboration problem. NADER employs a team of specialized agents to enhance a base architecture through iterative modification. Current LLM-based NAD methods typically operate independently, lacking the ability to learn from past experiences, which results in repeated mistakes and inefficient exploration. To address this issue, we propose the Reflector, which effectively learns from immediate feedback and long-term experiences. Additionally, unlike previous LLM-based methods that use code to represent neural architectures, we utilize a graph-based representation. This approach allows agents to focus on design aspects without being distracted by coding. We demonstrate the effectiveness of NADER in discovering high-performing architectures beyond predetermined search spaces through extensive experiments on benchmark tasks, showcasing its advantages over state-of-the-art methods. The codes will be released soon.

cs.CV↗