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Nan Li

Publications and source records attributed to Nan Li.

At least 55 records · Page 3Linked to original sources
arXiv2026

$E^3$-Agent: An Executable and Evolving Agent for Resource Management of Edge Generative Inference

Edge deployments of generative inference increasingly face two practical realities: per-device per-model performance is often unknown at deployment time, and it is non-stationary due to user-driven semantic events, background load, and device churn. Consequently, a resource manager that is tuned offline under a fixed regime can become brittle and expensive to maintain. This paper presents $E^3$-Agent, an executable and evolving agent for edge artificial intelligence generated content (AIGC) resource management. $E^3$-Agent separates a fast-path router that makes millisecond-level dispatch decisions from a slow-path, event-driven large language model (LLM) meta-controller that mitigates regime shifts through a small, explicit control surface exposed via a tool interface, including risk gating, router configuration, and rapid performance calibration. The agent learns online from execution feedback and continuously adapts to unknown and time-varying service-time mappings. We evaluate $E^3$-Agent in a discrete-event simulator driven by MLPerf-derived device-model measurement priors, covering cold-start warmup and three dynamic regimes: semantic dynamics, device churn, and hidden drift. Across the dynamic scenarios, $E^3$-Agent reduces average latency by 65%-73% compared to the best static baseline, stays within 7%-10% of an online full-information Oracle used for evaluation, and effectively suppresses stutter rate under semantic degradation.

cs.LG↗
arXiv2026

Memory burden effect of regular primordial black holes

Primordial black holes (PBHs) have attracted intensive research interest as a promising candidate of dark matter. However, because of the Hawking radiation, the PBHs lighter than $10^{15}~\rm{g}$ have already evaporated before today. To extend the PBH mass window to small-mass range, two possible ingredients are explored. The first is the consideration of regular PBHs with non-singular metrics, which can decrease the Hawking temperature, thereby lowering black hole evaporation. The second is the incorporation of the memory burden (MB) effect, which can further suppress the evaporation rate, after regular PBHs have lost a certain amount of their initial masses. In this work, we combine these two ingredients and study the MB effects of three types of regular PBHs (the Hayward, Bardeen and Simpson--Visser black holes). Assuming a phenomenological self-similar evaporation, we find that the MB effect significantly relaxes the evaporation constraints. For a benchmark of the MB strength parameter $k=1$, a new PBH mass window opens at around $10^6$--$10^8$ g, where regular PBHs can compose all dark matter without violating the Big Bang nucleosynthesis bounds.

astro-ph.CO↗
arXiv2026

Extracting redshifts from 2D slitless spectroscopic images using deep learning for the CSST galaxy survey

Wide-field slitless spectroscopic galaxy surveys, such as the one performed by the upcoming Chinese Space Station Survey Telescope (CSST), are crucial for precision cosmology but present formidable data analysis challenges. Because spectra are dispersed directly onto the detector, they are convolved with the 2-dimensional (2D) spatial morphology, which complicates wavelength calibration and consequently degrades the fidelity of subsequent 1-dimensional (1D) spectral extraction. To overcome these limitations, we present a deep learning framework that extracts redshifts directly from 2D slitless spectral images, bypassing 1D extraction entirely. We construct a realistic mock dataset for the CSST $GV$ and $GI$ band using high-resolution images from HSC-SSP PDR3 and spectral energy distributions (SEDs) from DESI DR1. A Bayesian convolutional neural network implemented by Monte Carlo dropout is employed to map the 2D spectral images to redshift estimations while simultaneously quantifying uncertainties. We find that our model can achieve a precision $σ_{\rm NMAD}=0.0104$ and mean uncertainty $\langle E / (1 + z_{\rm true}) \rangle=0.0155$ for sources with ${\rm SNR}_{GI}\geq1$. For sources with ${\rm SNR}_{GI}$ higher than 3.0, 5.0 and 10.0, $σ_{\rm NMAD}$ can achieve 0.0047, 0.0037 and 0.0024 respectively, matching the redshift precision requirements for studies such as BAO using the CSST slitless spectroscopic surveys. Furthermore, by utilizing spatial augmentations, the network demonstrates resilience to wavelength calibration errors. This work provides a novel and robust pathway for data analysis of next-generation slitless spectroscopic galaxy surveys.

astro-ph.IM↗
arXiv2026

Learning to Detect and Segment for Open Vocabulary Object Detection

Open vocabulary object detection has been greatly advanced by the recent development of vision-language pretrained model, which helps recognize novel objects with only semantic categories. The prior works mainly focus on knowledge transferring to the object proposal classification and employ class-agnostic box and mask prediction. In this work, we propose CondHead, a principled dynamic network design to better generalize the box regression and mask segmentation for open vocabulary setting. The core idea is to conditionally parameterize the network heads on semantic embedding and thus the model is guided with class-specific knowledge to better detect novel categories. Specifically, CondHead is composed of two streams of network heads, the dynamically aggregated head and the dynamically generated head. The former is instantiated with a set of static heads that are conditionally aggregated, these heads are optimized as experts and are expected to learn sophisticated prediction. The latter is instantiated with dynamically generated parameters and encodes general class-specific information. With such a conditional design, the detection model is bridged by the semantic embedding to offer strongly generalizable class-wise box and mask prediction. Our method brings significant improvement to the state-of-the-art open vocabulary object detection methods with very minor overhead, e.g., it surpasses a RegionClip model by 3.0 detection AP on novel categories, with only 1.1% more computation.

cs.CV↗
arXiv2026

Burst-Mode Ultrafast Laser Welding of Sapphire and Invar Alloy Across Large Interfacial Gaps up to 10 $μ$m

Achieving reliable joining between transparent materials and metals under non-optical-contact conditions remains challenging due to limited energy coupling and uncontrolled interfacial reaction across $μ$m-scale gaps. Burst-mode ultrafast lasers provide a potential solution for large-gap welding through temporally distributed energy deposition. However, the underlying interaction mechanisms and achievable joining limits remain unclear. In this study, burst-mode ultrafast laser welding of sapphire to Invar alloy was investigated under controlled interfacial gaps from 3 to 10 $μ$m. Cross-sectional microscopy, elemental mapping, white-light interferometry, and shear testing were employed to analyze joint morphology, elemental distribution, fracture behavior, and mechanical performance.After optimization of the processing parameters for burst-mode ultrafast laser welding, the interfacial morphological evolution and joint strength under different gap conditions were systematically investigated. At a 3 $μ$m gap, cyclic thermal stresses induced by burst pulses generate transverse micro-crack networks in sapphire, accompanied by a reduction in joint strength with increasing sub-pulse numbers. Notably, at a 10 $μ$m gap, where single-pulse welding fails, burst-mode ultrafast laser welding enables interfacial bridging with a maximum shear strength of 6.3 MPa, representing the highest level among published studies.These results indicate a gap-dependent evolution in burst-mode welding behavior governed by crack formation and energy accumulation. This study provides an important theoretical basis and practical guidance for achieving high-performance joining of dissimilar materials under large gap conditions.

physics.optics↗
arXiv2026

StampFormer: A Physics-Guided Material-Geometry-Coupled Multimodal Model for Rapid Prediction of Physical Fields in Sheet Metal Stamping

Traditional sheet metal forming relies on time-consuming and expensive Finite Element Analysis (FEA) for design validation, a process that significantly prolongs design cycles. While surrogate models offer faster iteration, current approaches have limitations: scalar-based methods cannot capture comprehensive field-based FEA results, while existing image-based models often ignore the critical role of material properties by focusing solely on geometry. To address this gap, we develop a physics-guided deep learning framework, namely StampFormer, which simultaneously uses component geometry and material stress-strain responses to predict FEA outcomes. The StampFormer framework uses three core components to process data. A Material-Augmented Geometric Network (MAGN) first fuses geometric and material data. This information is then integrated at various levels by a Hierarchical Material Embedding Injection Unit (HMEIU) before being processed by the primary network backbone, an adapted Swin-UNet. We evaluated our model on the stamping of a crossmember panel with two simulation datasets for steel and aluminium panels, and results demonstrate that StampFormer provides high-fidelity predictions of critical physical fields - including thinning, major strain, minor strain, plastic strain, and displacement - in under a second. Compared with ground truth FEA, our model achieved an average relative error of less than 8.5% on the four 2D fields and a mean squared error of less than 1.2 mm2 for the 3D displacement field. In summary, we introduce a practical and efficient framework that integrates multimodal information, namely geometry and material properties, to provide fast and accurate predictions, enabling designers to perform real-time manufacturability assessments.

cs.LG↗
arXiv2026

Enhancing Discrete Particle Swarm Optimization for Hypergraph-Modeled Influence Maximization

Influence maximization (IM) is a fundamental problem in complex network analysis, with a wide range of real-world applications. To date, existing approaches to influential node identification in IM have predominantly relied on standard graphs, failing to capture higher-order intrinsic interactions embedded in many real-world systems. Hypergraphs can be employed to better capture higher-order interactions. However, using hypergraphs may lead to an excessively large search space and increased complexity in modeling cascading dynamics, making it challenging to accurately identify influential nodes. Therefore, in this study, we propose a new hypergraph-modeled IM method, based on the Discrete Particle Swarm Optimization algorithm and the threshold model. In the proposed method, a particle (i.e., a candidate solution) represents the selection information of seed nodes, and the fitness function is designed to accurately and efficiently evaluate the influence of seed nodes via a two-layer local influence approximation. We also propose a degree-based initialization strategy to improve the quality of initial solutions and develop rules for updating particles' velocity and position, incorporated with a local search to drive particles toward better solutions. Experimental results demonstrate that the proposed method outperforms baseline methods on both synthetic and real-world hypergraphs. In addition, ablation studies validate the effectiveness of both the local search and the initialization strategies.

cs.SI↗
arXiv2026

MSCT: Differential Cross-Modal Attention for Deepfake Detection

Audio-visual deepfake detection typically employs a complementary multi-modal model to check the forgery traces in the video. These methods primarily extract forgery traces through audio-visual alignment, which results from the inconsistency between audio and video modalities. However, the traditional multi-modal forgery detection method has the problem of insufficient feature extraction and modal alignment deviation. To address this, we propose a multi-scale cross-modal transformer encoder (MSCT) for deepfake detection. Our approach includes a multi-scale self-attention to integrate the features of adjacent embeddings and a differential cross-modal attention to fuse multi-modal features. Our experiments demonstrate competitive performance on the FakeAVCeleb dataset, validating the effectiveness of the proposed structure.

cs.CV↗
arXiv2026

Adaptive Obstacle-Aware Task Assignment and Planning for Heterogeneous Robot Teaming

Multi-Agent Task Assignment and Planning (MATP) has attracted growing attention but remains challenging in terms of scalability, spatial reasoning, and adaptability in obstacle-rich environments. To address these challenges, we propose OATH - Adaptive Obstacle-Aware Task Assignment and Planning for Heterogeneous Robot Teaming - which advances MATP by introducing a novel obstacle-aware strategy for task assignment. First, we develop an adaptive Halton sequence map, the first known application of Halton sampling with obstacle-aware adaptation in MATP, which adjusts sampling density based on obstacle distribution. Second, we propose a cluster-auction-selection framework that integrates obstacle-aware clustering with weighted auctions and intra-cluster task selection. These mechanisms jointly enable effective coordination among heterogeneous robots while maintaining scalability and suboptimal allocation performance. In addition, our framework leverages an LLM to interpret human instructions and directly guide the planner in real time. We validate OATH in both NVIDIA Isaac Sim and real-world hardware experiments using TurtleBot platforms, demonstrating substantial improvements in task assignment quality, scalability, adaptability to dynamic changes, and overall execution performance compared to state-of-the-art MATP baselines. A project website is available at https://llm-oath.github.io/.

cs.RO↗
arXiv2026

CURLING -- III. Identifying Candidates of Wide-separation Gravitationally Lensed Quasars from the CatNorth Catalogue

Wide-separation lensed quasars (WSLQs) are a rare subclass of strongly lensed quasars produced by massive galaxy clusters. They provide valuable probes of dark-matter halos and quasar host galaxies. However, only about ten WSLQ systems are currently known, which limits further studies. To enlarge the sample from wide-area surveys, we developed a catalog-based pipeline and applied it to the CatNorth database, a catalog of quasar candidates constructed from Gaia DR3. CatNorth contains 1,545,514 quasar candidates with about 90% purity and a Gaia G-band limiting magnitude of roughly 21. The pipeline has three stages. First, we identify groups with separations between 10 and 72 arcsec using a HEALPix grid with 25.6 arcsec spacing and a friends-of-friends search. We then filter by intra-group color and spectral similarity, reducing the 1,545,514 sources to 14,244 groups while retaining all known, discoverable WSLQs. Finally, a visual check, guided by image geometry and the presence of likely foreground lenses, yields the candidate list with quality labels. We identify 333 new WSLQ candidates with separations from 10 to 56.8 arcsec. Using available SDSS DR16 and DESI DR1 spectroscopy, we uncover two new candidate systems; the remaining 331 candidates lack sufficient spectra and are labeled as 45 grade A, 98 grade B, and 188 grade C. We also compile 29 confirmed dual quasars as a by-product. When feasible, we plan follow-up spectroscopy and deeper imaging to confirm WSLQs among these candidates and enable the related science.

astro-ph.GA↗
arXiv2026

The Universe Favors Primes: A Study in the Primality of Cosmic Structures

The cosmological principle states that the universe is uniform and does not favor any specific position or direction. However, research conducted by \cite{Shen2025} has revealed that the universe demonstrates a notable inclination towards parity-odd states. Furthermore, it remains uncertain whether the universe also favors prime numbers. In this study, we examine the largest available catalogs of galaxy groups to investigate this hypothesis. Specifically, we assess whether the number of galaxies within a galaxy group or cluster is more likely to be a prime number. Our results strongly suggest that the universe does indeed have a preference for prime numbers, with findings exceeding the 4.1 sigma significance threshold. This insight explains why the Primes consistently triumphs over Unicorn. Consequently, it may be necessary to consider revising the cosmological principle in the context of a higher-dimensional feature space. Moreover, our research establishes a connection between the Riemann Zeta function and cosmology pioneeringly, paving the way for the development of Cosmozetaology.

astro-ph.CO↗
arXiv2026

Advances and Challenges in Solar Flare Prediction: A Review

Solar flares, as one of the most prominent manifestations of solar activity, have a profound impact on both the Earth's space environment and human activities. As a result, accurate solar flare prediction has emerged as a central topic in space weather research. In recent years, substantial progress has been made in the field of solar flare forecasting, driven by the rapid advancements in space observation technology and the continuous improvement of data processing capabilities. This paper presents a comprehensive review of the current state of research in this area, with a particular focus on tracing the evolution of data-driven approaches -- which have progressed from early statistical learning techniques to more sophisticated machine learning and deep learning paradigms, and most recently, to the emergence of Multimodal Large Models (MLMs). Furthermore, this study examines the realistic performance of existing flare forecasting platforms, elucidating their limitations in operational space weather applications and thereby offering a practical reference for future advancements in technological optimization and system design.

astro-ph.SR↗
arXiv2026

Detecting Complex-Energy Braiding Topology in a Dissipative Atomic Simulator with Transformer-Based Geometric Tomography

Machine learning (ML) is shaping our exploration of topological matter, whose existence is inherently tied to the geometry of quantum states or energy spectra. In non-Hermitian systems, distinctive spectral geometry can lead to topological braiding of complex-energy bands, yet directly probing this topology-geometry interplay remains challenging. Here, we introduce a Transformer-based ML framework to capture this interplay and experimentally demonstrate it in a dissipative cold-atom simulator. Using a Bose-Einstein condensate, we engineer tunable dissipative two-level systems whose complex eigenenergies form braids. Owing to the density-dependent dissipation, the instantaneous energy braids exhibit topologically distinct structures at short and long times. The Transformer not only accurately predicts topological invariants for diverse energy braids but also, through its self-attention mechanism, autonomously highlights band crossings as the governing underlying geometric feature. Our work paves the way for ML-guided exploration of non-Hermitian topological phases in cold atoms and beyond.

cond-mat.quant-gas↗
arXiv2026

Goal-Oriented Framework for Optical Flow-based Multi-User Multi-Task Video Transmission

Efficient multi-user multi-task video transmission is an important research topic within the realm of current wireless communication systems. To reduce the transmission burden and save communication resources, we propose a goal-oriented semantic communication framework for optical flow-based multi-user multi-task video transmission (OF-GSC). At the transmitter, we design a semantic encoder that consists of a motion extractor and a patch-level optical flow-based semantic representation extractor to effectively identify and select important semantic representations. At the receiver, we design a transformer-based semantic decoder for high-quality video reconstruction and video classification tasks. To minimize the communication time, we develop a deep deterministic policy gradient (DDPG)-based bandwidth allocation algorithm for multi-user transmission. For video reconstruction tasks, our OF-GSC framework achieves a significant improvement in the received video quality, as evidenced by a 13.47% increase in the structural similarity index measure (SSIM) score in comparison to DeepJSCC. For video classification tasks, OF-GSC achieves a Top-1 accuracy slightly surpassing the performance of VideoMAE with only 25% required data under the same mask ratio of 0.3. For bandwidth allocation optimization, our DDPG-based algorithm reduces the maximum transmission time by 25.97% compared with the baseline equal-bandwidth allocation scheme.

eess.IV↗
arXiv2026

Grounded Misunderstandings in Asymmetric Dialogue: A Perspectivist Annotation Scheme for MapTask

Collaborative dialogue relies on participants incrementally establishing common ground, yet in asymmetric settings they may believe they agree while referring to different entities. We introduce a perspectivist annotation scheme for the HCRC MapTask corpus (Anderson et al., 1991) that separately captures speaker and addressee grounded interpretations for each reference expression, enabling us to trace how understanding emerges, diverges, and repairs over time. Using a scheme-constrained LLM annotation pipeline, we obtain 13k annotated reference expressions with reliability estimates and analyze the resulting understanding states. The results show that full misunderstandings are rare once lexical variants are unified, but multiplicity discrepancies systematically induce divergences, revealing how apparent grounding can mask referential misalignment. Our framework provides both a resource and an analytic lens for studying grounded misunderstanding and for evaluating (V)LLMs' capacity to model perspective-dependent grounding in collaborative dialogue.

cs.CL↗
arXiv2026

Confirming lensed-quasar candidates with DESI and P200 spectroscopy I. 14 lensed quasars and 8 lensed galaxies

Lensed quasars are powerful probes of cosmology, the co-evolution of supermassive black holes and their host galaxies, and the distribution of dark matter. We cross-match 1,724 previously identified candidates from KiDS, HSC, and the DESI Legacy Imaging Surveys (DESI-LS) with DESI DR1, obtaining 937 DESI spectra for 677 unique systems. Combining DESI spectroscopy with observations from the Palomar 200-inch Double Spectrograph (P200/DBSP), we confirm two lensed quasars with source redshifts $z_s={1.93,3.23}$ and Einstein radii $θ_{\rm E}={0.39,1.07}$ arcsec, respectively. We further identify 12 likely lensed quasars that are well reproduced by a simple SIE model, exhibit lens galaxies in the image modeling, and have at least one available spectrum; across these, $θ_{\rm E}$ spans $045$-$2.34$ arcsec and $z_s$ spans $1.13$-$2.88$. In 9 of the 12 cases, the systems already satisfy our lensing criteria except that only one quasar image currently has a spectrum; obtaining a second spectrum for the other image would enable immediate confirmation. Moreover, we report eight new static strong lenses spanning galaxy- to group- scale lenses. These results provide valuable targets for follow-up studies and underscore the efficiency of wide-field spectroscopic surveys such as DESI in confirming gravitationally lensed quasars and galaxies.

astro-ph.GA↗
arXiv2026

BioChemInsight: An Online Platform for Automated Extraction of Chemical Structures and Activity Data from Patents

The automated extraction of chemical structures and their corresponding bioactivity data is essential for accelerating drug discovery and enabling data-driven research. Current optical chemical structure recognition tools lack the capability to autonomously link molecular structures with their bioactivity profiles, posing a significant bottleneck in structure-activity relationship analysis. To address this, we present BioChemInsight, an open-source pipeline that integrates DECIMER Segmentation with MolNexTR for chemical structure recognition, GLM-4.5V for compound identifier association, and PaddleOCR combined with GLM-4.6 for bioactivity extraction and unit normalization. We evaluated BioChemInsight on 181 patents covering 15 therapeutic targets. The system achieved an average extraction accuracy of above 90% across three key tasks: chemical structure recognition, bioactivity data extraction, and compound identifier association. Our analysis indicates that the chemical space covered by patents is largely complementary to that contained in established public database ChEMBL. Consequently, by enabling systematic patent mining, BioChemInsight provides access to chemical information underrepresented in ChEMBL. This capability expands the landscape of explorable compound-target interactions, enriches the data foundation for quantitative structure-activity relationship modeling and targeted screening, and reduces data preprocessing time from weeks to hours. BioChemInsight is available at https://github.com/dahuilangda/BioChemInsight.

q-bio.QM↗
arXiv2026

Performance Analysis of Digital Flux-locked Loop Circuit with Different SQUID $V$-$ϕ$ Transfer Curves for TES Readout System

A superconducting quantum interference device (SQUID), functioning as a nonlinear response device, typically requires the incorporation of a flux-locked loop (FLL) circuit to facilitate linear amplification of the current signal transmitted through a superconducting transition-edge sensor (TES) across a large dynamic range.This work presents a reasonable model of the SQUID-FLL readout system, based on a digital proportional-integral-differential (PID) flux negative feedback algorithm.This work investigates the effect of $V$-$ϕ$ shape on the performance of digital FLL circuits.Such as the impact factors of bandwidth, design limits of slew rate of the system and the influence of the shapes of SQUID $V$-$ϕ$ curve.Furthermore, the dynamic response of the system to X-ray pulse signals with rise time ranging from $4.4{\sim}281$ $\mathrm{μs}$ and amplitudes ranging from $6{\sim}8$ $\mathrm{ϕ_0}$ was simulated.All the simulation results were found to be consistent with the existing mature theories, thereby validating the accuracy of the model.The results also provide a reliable modelling reference for the design of digital PID flux negative feedback and multiplexing SQUID readout electronic systems.

astro-ph.IM↗