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Zhi Zeng

Publications and source records attributed to Zhi Zeng.

At least 19 recordsLinked to original sources
arXiv2026

Decoding Motion-Blurred QR Codes: Motion-Parameter Error Tolerance and Model-Based Recovery

Quick Response (QR) code deblurring must ultimately recover the encoded payload, yet how accurately the underlying camera motion must be estimated remains an open question. To address this question, we formulate motion-blurred QR recovery within a unified mathematical framework of identifiability and systematically measure the decoding tolerance to perturbations in motion length, direction, rotation or scaling centre, and speed profile. These controlled experiments reveal three structural laws: 1) parameter tolerances shrink rapidly as blur amplitude grows and are tightest under uniform speed, whereas accelerating motion is substantially more forgiving; 2) length tolerance can differ markedly between underestimation and overestimation; 3) errors that are individually benign interact strongly and prevent decoding when combined. Guided by these findings, we propose a grey-box recovery pipeline in which two compact networks predict an initial corner trajectory, followed by tolerance-guided candidate search, alternating image-and-path fitting, and regularised linear inversion. On a new benchmark of 955 handheld motion-blurred photographs captured across four phone models and four motion categories, our method recovers the exact payload on 93.1% of the images, outperforming the strongest fine-tuned deblurring network at 83.6% and reducing the decoding error rate by 58.0%.

eess.IV↗
arXiv2026

Turning Quantum Compiler Configuration Selection Upside Down: A Top-Down Paradigm

Selecting an effective quantum compiler configuration requires coordinated decisions over both hardware targets and compiler settings, with the ultimate goal of improving circuit execution fidelity. Existing SOTA approaches typically decompose this problem into two sequential stages: a device is chosen first, after which compiler options are tuned on that device through RL-based search. This bottom-up strategy can be fundamentally limited by its initial device decision, since a poorly matched backend may constrain the best fidelity achievable regardless of how effectively later compiler options are optimized. In contrast to classical compiler tuning, where the search space is often prohibitively large, the configuration space of quantum compilers remains manageable enough to support exhaustive search. Consequently, rather than repeatedly searching from candidate configurations, one can derive reusable configuration knowledge from offline oracle results and apply it directly to new circuits. In this paper, we introduce a top-down configuration-selection paradigm and build QuOPTBench, an exhaustively searched benchmark containing oracle configurations for 268 quantum circuits. We first study whether LLMs can directly infer suitable configurations and evaluate ten representative LLMs. The results reveal that vanilla LLMs remain far from the oracle. We then employ an LLM to extract transferable rules from sampled circuits governing configuration selection. Using these rules together with predictors trained on the enumerated configuration data of the sampled circuits, we design QuTOP, a rule-guided ML framework that performs fast configuration selection without iterative online search. Across our evaluation, the distilled rules consistently strengthen LLM-based selection, while QuTOP provides the strongest overall balance between solution quality and selection latency among the baselines.

quant-ph↗
arXiv2026

Can Protein-Derived Knowledge Improve Pathology Foundation Models?

Molecularly guided pathology foundation models (PFMs) exploit transcriptomic or proteomic information to enrich whole-slide image (WSI) representations, yet effectively leveraging large standalone molecular corpora remains challenging. First, existing molecular foundation models encode protein sequences or single-cell states, not the patient-level bulk expression profiles paired with WSIs. Second, because cross-modal supervision is restricted to paired WSI-omics samples, knowledge from standalone molecular corpora reaches the pathology encoder only indirectly, creating a paired-support bottleneck. To address these challenges, we propose a three-stage framework that decouples proteomic knowledge acquisition from cross-modal transfer, yielding ProSlide, a slide-level hierarchical pathology foundation model. First, to close the modality gap, we pretrain a Proteomic Foundation Encoder (PFE) on 12,695 sample-level bulk protein profiles using virtual profile generation and expression-space multi-view pretraining. Second, we pretrain ProSlide, a patch-region-slide encoder, to predict protein expression from paired WSI-protein samples. Third, to relax the paired-support bottleneck, we introduce Prot2Path, a cross-modal relational distillation objective. For each paired sample, it aligns the similarity distributions of the WSI and its protein profile over a shared, frozen bank of PFE-encoded paired and standalone profiles. We evaluate ProSlide on 12 downstream tasks across breast, lung, and renal cancers. Despite being pretrained with only 2,229 WSIs and 12,695 sample-level protein profiles, ProSlide achieves the highest mean accuracy and AUC within each cancer group.

cs.CV↗
arXiv2026

Listen, See and Track: Spatio-Temporal Audio-Visual Sound Event Reasoning for Omni-Modal Language Models

Understanding dynamic sound sources requires jointly determining what produces a sound, where the source is located, and how it moves over time. Yet existing audio-language models often represent clips as global acoustic events, while vision-language models lack the spatial audio cues needed to localize and track individual sources. To evaluate this missing capability, we introduce ST-OmniQA, a spatio-temporal audio-visual question-answering benchmark built from panoramic videos paired with synchronized first-order Ambisonics (FOA) audio of moving sound sources. It contains 40K videos and 400K question-answer pairs organized into four capability levels covering sound-event recognition, direction of arrival, source distance, motion trajectories, and temporally grounded audio-visual reasoning. Building on this benchmark, we propose ST-Omni-R1, which integrates FOA-derived semantic and trajectory representations with panoramic visual context and is trained through progressive curriculum learning and reasoning-tree reinforcement learning. ST-Omni-R1 achieves 77.83\% average semantic accuracy across the four levels, compared with 37.28\% for the best evaluated baseline. Results on three public spatial-audio benchmarks further indicate that its learned spatial and motion representations transfer beyond ST-OmniQA.

cs.AI↗
arXiv2026

From Patches to Evidence Balls: Class-Conditioned Evidence Retrieval for Few-Shot Whole Slide Image Classification

Whole slide image (WSI) classification is an evidence-driven task, where diagnostic cues are often sparse, spatially organized, and class-dependent. Existing MIL and vision-language methods aggregate a large pool of patch features into a single global slide representation. Under few-shot supervision, limited slide-level labels make it difficult to learn a reliable aggregation mechanism that organizes sparse local cues into compact and coherent diagnostic evidence. Moreover, a shared slide representation compresses evidence supporting a candidate class and its alternatives into the same feature, limiting class-specific reasoning and interpretability. To address these issues, we propose EviBall, a class-conditioned evidence retrieval framework for few-shot WSI classification. EviBall organizes local patches into Evidence Balls through semantic-spatial assignment and center refinement, yielding compact and spatially coherent evidence units under weak supervision. It then uses task-specific class queries, including language-guided queries for morphology-oriented tasks and molecular-guided queries for molecular endpoint prediction, to retrieve supporting evidence balls and produce class-conditioned evidence representations for direct class-wise prediction. By introducing structured evidence units and task-relevant semantic guidance, EviBall reduces the reliance on learning an unconstrained global aggregation mechanism from scarce slide-level labels. It therefore reformulates few-shot WSI classification as structured evidence retrieval and competition among candidate classes. Extensive experiments across four morphology-oriented and molecular endpoint WSI tasks demonstrate that EviBall consistently outperforms conventional and vision-language MIL baselines under diverse few-shot settings, while providing spatially localized and class-specific evidence for each prediction.

cs.CV↗
arXiv2026

Hydrostatic Pressure-Induced Evolution of the Superconducting Transition Temperature of Bi-2212: Insights from First-Principles Calculations

High-pressure experiments on Bi$_2$Sr$_2$CaCu$_2$O$_{8+x}$ (Bi-2212) have reported apparently conflicting evolutions of the superconducting transition temperature $T_c$, ranging from weak enhancement to strong suppression and even a proposed second superconducting dome. To clarify the origin of these discrepancies, we combine first-principles density functional theory calculations with a pressure-dependent low-energy bilayer model solved by the slave-boson mean-field method together with a Berezinskii-Kosterlitz-Thouless estimate of phase coherence. Our results show that hydrostatic pressure induces a pronounced self-doping effect in Bi-2212: holes are transferred from the Bi-O charge-reservoir layers to the CuO$_2$ superconducting planes, leading to a systematic increase in the effective CuO$_2$-plane hole concentration $δ_x$. At the same time, pressure enhances the pairing scale through the renormalization of the hopping and superexchange parameters. As a consequence, the pressure evolution of $T_c$ is governed by the competition between pressure-enhanced pairing and pressure-driven motion along the common $T_c$-$δ_x$ dome, making $T_c(P)$ highly sensitive to the initial doping state. Even samples with very similar ambient-pressure $T_c$ but slightly different initial doping can therefore display qualitatively different pressure responses. This provides a unified interpretation of a large part of the disparate high-pressure behavior reported for Bi-2212 and suggests that slightly underdoped samples are more favorable than ambient-pressure optimal samples for achieving improved superconducting performance under pressure.

cond-mat.supr-con↗
arXiv2026

M3D-Stereo: A Multiple-Medium and Multiple-Degradation Dataset for Stereo Image Restoration

Image restoration under adverse conditions, such as underwater, haze or fog, and low-light environments, remains a highly challenging problem due to complex physical degradations and severe information loss. Existing datasets are predominantly limited to a single degradation type or heavily rely on synthetic data without stereo consistency, inherently restricting their applicability in real-world scenarios. To address this, we introduce M3D-Stereo, a stereo dataset with 7904 high-resolution image pairs for image restoration research acquired in multiple media with multiple controlled degradation levels. It encompasses four degradation scenarios: underwater scatter, haze/fog, underwater low-light, and haze low-light. Each scenario forms a subset, and is divided into six levels of progressive degradation, allowing fine-grained evaluations of restoration methods with increasing severity of degradation. Collected via a laboratory setup, the dataset provides aligned stereo image pairs along with their pixel-wise consistent clear ground truths. Two restoration tasks, single-level and mixed-level degradation, were performed to verify its validity. M3D-Stereo establishes a better controlled and more realistic benchmark to evaluate image restoration and stereo matching methods in complex degradation environments. It is made public under LGPLv3 license.

cs.CV↗
arXiv2026

From Manipulation to Mistrust: Explaining Diverse Micro-Video Misinformation for Robust Debunking in the Wild

The rise of micro-videos has reshaped how misinformation spreads, amplifying its speed, reach, and impact on public trust. Existing benchmarks typically focus on a single deception type, overlooking the diversity of real-world cases that involve multimodal manipulation, AI-generated content, cognitive bias, and out-of-context reuse. Meanwhile, most detection models lack fine-grained attribution, limiting interpretability and practical utility. To address these gaps, we introduce WildFakeBench, a large-scale benchmark of over 10,000 real-world micro-videos covering diverse misinformation types and sources, each annotated with expert-defined attribution labels. Building on this foundation, we develop FakeAgent, a Delphi-inspired multi-agent reasoning framework that integrates multimodal understanding with external evidence for attribution-grounded analysis. FakeAgent jointly analyzes content and retrieved evidence to identify manipulation, recognize cognitive and AI-generated patterns, and detect out-of-context misinformation. Extensive experiments show that FakeAgent consistently outperforms existing MLLMs across all misinformation types, while WildFakeBench provides a realistic and challenging testbed for advancing explainable micro-video misinformation detection. Data and code are available at: https://github.com/Aiyistan/FakeAgent.

cs.SI↗
arXiv2026

CARE: A Molecular-Guided Foundation Model with Adaptive Region Modeling for Whole Slide Image Analysis

Foundation models have recently achieved impressive success in computational pathology, demonstrating strong generalization across diverse histopathology tasks. However, existing models overlook the heterogeneous and non-uniform organization of pathological regions of interest (ROIs) because they rely on natural image backbones not tailored for tissue morphology. Consequently, they often fail to capture the coherent tissue architecture beyond isolated patches, limiting interpretability and clinical relevance. To address these challenges, we present Cross-modal Adaptive Region Encoder (CARE), a foundation model for pathology that automatically partitions WSIs into several morphologically relevant regions. Specifically, CARE employs a two-stage pretraining strategy: (1) a self-supervised unimodal pretraining stage that learns morphological representations from 34,277 whole-slide images (WSIs) without segmentation annotations, and (2) a cross-modal alignment stage that leverages RNA and protein profiles to refine the construction and representation of adaptive regions. This molecular guidance enables CARE to identify biologically relevant patterns and generate irregular yet coherent tissue regions, selecting the most representative area as ROI. CARE supports a broad range of pathology-related tasks, using either the ROI feature or the slide-level feature obtained by aggregating adaptive regions. Based on only one-tenth of the pretraining data typically used by mainstream foundation models, CARE achieves superior average performance across 33 downstream benchmarks, including morphological classification, molecular prediction, and survival analysis, and outperforms other foundation model baselines overall.

cs.CV↗
arXiv2026

Physics-Informed Spatial-Temporal Transformer for Terahertz Near-Field Beam Tracking

Terahertz (THz) ultra-massive multiple-input multiple-output (UM-MIMO) promises ultra-high throughput, while its highly directional beams demand rapid and accurate beam tracking driven by precise user-state estimation. Moreover, large array apertures at high frequencies induce near-field propagation effects, where far-field modeling becomes inaccurate and near-field parametric channel estimation is costly. Bypassing near-field codebook, PAST-TT is proposed to bridge near-field tracking with low-overhead far-field codebook probing by exploiting parallax, amplified by widely spaced subarrays. With comb-type frequency-division multiplexing pilots, each subarray yields frequency-affine phase signatures whose frequency and temporal increments encode propagation delay and its variation between frames. Building on these signatures, a Parallax-Aware Spatial Transformer (PAST) compresses them and outputs per-frame position estimates with token reliability to downweight bad frames, regularized by a physics-in-the-loop consistency loss. A causal Temporal Transformer (TT) then performs reliability-aware filtering and prediction over a sliding window to initialize the beam of the next frame. Acting on short token sequences, PAST-TT avoids a monolithic spatial-temporal network over raw pilots, which keeps the model lightweight with a critical path latency of 0.61 ms. Simulations show that at 15 dB signal-to-noise ratio, PAST achieves 7.81 mm distance RMSE and 0.0588° angle RMSE. Even with a bad-frame rate of 0.1, TT reduces the distance and angle prediction RMSE by 23.1% and 32.8% compared with the best competing tracker.

eess.SP↗
arXiv2026

Simulation Study on the Discrimination of $0νββ$ Events from Single-Electron Events Using Orthogonal-Strip HPGe Detectors

Neutrinoless double beta decay ($0νββ$) offers a sensitive probe of neutrino mass and its Majorana nature. Orthogonal-strip high-purity germanium (HPGe) detectors with high spatial resolution provide a promising approach for distinguishing $0νββ$ events from single-electron backgrounds. In this work, a simulation framework was developed to evaluate the discrimination performance of these detectors. The framework combined Geant4 simulations with a hybrid numerical-analytical approach to model charge cloud dynamics. A dual-branch convolutional neural network (CNN) was implemented to extract topological features for event classification. The impact of detector geometry on discrimination performance was quantitatively assessed. For a fixed crystal thickness of 15 mm, the background rejection efficiency decreased from 79.5\% to 59.0\% as the strip pitch increased from 0.1 mm to 0.5 mm. For a strip pitch of 0.25 mm, a crystal thickness of 20 mm was found to be optimal, balancing full-energy peak (FEP) efficiency with discrimination capability. These results demonstrate that orthogonal-strip HPGe detectors can effectively suppress single-electron backgrounds, and provide quantitative guidance for detector design in $^{76}$Ge $0νββ$ decay searches.

physics.ins-det↗
arXiv2026

Nonequilibrium Dynamics of Dirac Quantum Criticality in Imaginary Time

Quantum criticality within Dirac fermions harbors a plethora of exotic phenomena, attracting sustained attention in the past decades. Here, we explore the imaginary-time relaxation dynamics in a typical Dirac quantum criticality belonging to chiral Heisenberg universality class. Performing large-scale quantum Monte Carlo simulation, we unveil rich nonequilibrium critical phenomena from different initial states. In particular, we identify a non-stationary initial slip evolution characterized by an unconventional negative critical exponent $θ=-0.84(4)$, corroborating the significant impact of fermionic critical fluctuations. Furthermore, we generalize the nonequilibrium scaling theory to incorporate both fermionic and bosonic critical modes, capturing their distinct relaxation behaviors. Armed with the scaling theory, we establish a new framework to investigate fermionic quantum criticality based on short-time dynamics, paving a promising avenue to fathoming quantum criticality in diverse fermionic systems with high efficiency.

cond-mat.str-el↗
arXiv2026

Pressure and doping control of magnetic order and metallization in Ruddlesden-Popper La2NiO4

The discovery of superconductivity in multilayer nickelates under pressure has intensified interest in understanding the magnetic and electronic properties of Ruddlesden-Popper nickelates. Using density functional theory with Hubbard corrections, we investigate the magnetic ground state, electronic structure evolution under pressure, and Sr-doping effects in La$_2$NiO$_4$. We find that at ambient pressure, tetragonal La$_2$NiO$_4$ exhibits G-type antiferromagnetic order with negligible interlayer magnetic coupling. Under hydrostatic pressure, the system undergoes a continuous insulator-metal transition at ~50 GPa while maintaining robust magnetic order up to 75 GPa, contrasting sharply with the rapid magnetic suppression in La$_3$Ni$_2$O$_7$. Sr doping induces a systematic evolution from G-type to A-type, to striped antiferromagnetic orders, and eventually to ferromagnetic order, accompanied by metallization. Furthermore, LaSrNiO$_4$ displays weak charge and orbital orders. These results reveal the unique pressure and doping effects of single-layer nickelates and provide insights into the magnetic mechanisms underlying nickelate superconductivity.

cond-mat.supr-con↗
arXiv2026

The Facade of Truth: Uncovering and Mitigating LLM Susceptibility to Deceptive Evidence

To reliably assist human decision-making, LLMs must maintain factual internal beliefs against misleading injections. While current models resist explicit misinformation, we uncover a fundamental vulnerability to sophisticated, hard-to-falsify evidence. To systematically probe this weakness, we introduce MisBelief, a framework that generates misleading evidence via collaborative, multi-round interactions among multi-role LLMs. This process mimics subtle, defeasible reasoning and progressive refinement to create logically persuasive yet factually deceptive claims. Using MisBelief, we generate 4,800 instances across three difficulty levels to evaluate 7 representative LLMs. Results indicate that while models are robust to direct misinformation, they are highly sensitive to this refined evidence: belief scores in falsehoods increase by an average of 93.0\%, fundamentally compromising downstream recommendations. To address this, we propose Deceptive Intent Shielding (DIS), a governance mechanism that provides an early warning signal by inferring the deceptive intent behind evidence. Empirical results demonstrate that DIS consistently mitigates belief shifts and promotes more cautious evidence evaluation.

cs.CL↗
arXiv2025

MT-Mark: Rethinking Image Watermarking via Mutual-Teacher Collaboration with Adaptive Feature Modulation

Existing deep image watermarking methods follow a fixed embedding-distortion-extraction pipeline, where the embedder and extractor are weakly coupled through a final loss and optimized in isolation. This design lacks explicit collaboration, leaving no structured mechanism for the embedder to incorporate decoding-aware cues or for the extractor to guide embedding during training. To address this architectural limitation, we rethink deep image watermarking by reformulating embedding and extraction as explicitly collaborative components. To realize this reformulation, we introduce a Collaborative Interaction Mechanism (CIM) that establishes direct, bidirectional communication between the embedder and extractor, enabling a mutual-teacher training paradigm and coordinated optimization. Built upon this explicitly collaborative architecture, we further propose an Adaptive Feature Modulation Module (AFMM) to support effective interaction. AFMM enables content-aware feature regulation by decoupling modulation structure and strength, guiding watermark embedding toward stable image features while suppressing host interference during extraction. Under CIM, the AFMMs on both sides form a closed-loop collaboration that aligns embedding behavior with extraction objectives. This architecture-level redesign changes how robustness is learned in watermarking systems. Rather than relying on exhaustive distortion simulation, robustness emerges from coordinated representation learning between embedding and extraction. Experiments on real-world and AI-generated datasets demonstrate that the proposed method consistently outperforms state-of-the-art approaches in watermark extraction accuracy while maintaining high perceptual quality, showing strong robustness and generalization.

cs.CV↗
arXiv2025

Ultra-Low background germanium spectrometers at the China Jinping Underground Laboratory

Four ultra-low background germanium spectrometers, called GeTHU, have been installed at the first phase of China Jinping Underground Laboratory (CJPL-I), and served for material screening of dark matter and neutrino experiments. Recently, a new multi-detector spectrometer with five germanium detectors has been developed and installed at the second phase of CJPL (CJPL-II) with a minimum detectable activity (MDA) of about 10 μBq/kg. In addition, another fifteen GeTHU-like spectrometers have been installed at CJPL-II with an MDA of about 1 mBq/kg. This paper will introduce the ultra-low background germanium spectrometers including shielding design, background characteristics and application to material screening.

physics.ins-det↗
arXiv2025

DiFaR: Enhancing Multimodal Misinformation Detection with Diverse, Factual, and Relevant Rationales

Generating textual rationales from large vision-language models (LVLMs) to support trainable multimodal misinformation detectors has emerged as a promising paradigm. However, its effectiveness is fundamentally limited by three core challenges: (i) insufficient diversity in generated rationales, (ii) factual inaccuracies due to hallucinations, and (iii) irrelevant or conflicting content that introduces noise. We introduce DiFaR, a detector-agnostic framework that produces diverse, factual, and relevant rationales to enhance misinformation detection. DiFaR employs five chain-of-thought prompts to elicit varied reasoning traces from LVLMs and incorporates a lightweight post-hoc filtering module to select rationale sentences based on sentence-level factuality and relevance scores. Extensive experiments on four popular benchmarks demonstrate that DiFaR outperforms four baseline categories by up to 5.9% and boosts existing detectors by as much as 8.7%. Both automatic metrics and human evaluations confirm that DiFaR significantly improves rationale quality across all three dimensions.

cs.CL↗
arXiv2025

Three-dimensional position reconstruction of orthogonal-strip planar high-purity germanium detectors using maximum likelihood estimation

Orthogonal-strip planar high-purity germanium (HPGe) detectors can reconstruct three-dimensional (3D) positions of photon interactions through analysis of parameters extracted from multiple charge signals. The conventional method independently reconstructs positions in each dimension using amplitude-based parameters, leading to noise sensitivity and systematic biases. In this study, we propose a multi-parameter-joint reconstruction method based on maximum likelihood estimation (MLE) which establishes a mapping between pulse shape parameters and corresponding 3D positions. To mitigate the effects of electronic noise, we employ integral-based parameters. The reconstruction performance was evaluated using pulse shape simulations. For 100 keV photons under 1 keV root-mean-square (RMS) electronic noise, the maximum Z reconstruction bias was reduced from 0.4 mm to 0.02 mm in the central region and from 2 mm to 0.15 mm near the electrodes. The maximum reconstruction bias in the X/Y directions was reduced from 0.4 mm to 0.016 mm. Furthermore, the use of integral-based parameters mitigated the rapid degradation of resolution under high-noise conditions. The achieved position resolution ranged from 0.07 mm to 0.16 mm in the Z directions and from 0.07 mm to 0.44 mm in the X/Y direction. This method offers a promising approach to 3D position reconstruction with HPGe detectors for applications such as medical imaging and gamma-ray astronomy.

physics.ins-det↗