arXiv subjects

Li Zhou

Publications and source records attributed to Li Zhou.

At least 37 records · Page 2Linked to original sources

arXiv2026

Bridging What the Model Thinks and How It Speaks: Expressive Speech Generation via Self-Aware Intent-Realization Alignment

Speech Language Models (SLMs) exhibit strong semantic understanding, yet often fail to translate this capacity into expressive acoustic realization, producing speech with flattened prosody and misaligned emotion. We identify this mismatch as the semantic understanding-acoustic realization gap. Existing approaches typically rely on externally specified proxies, such as emotion labels or style prompts, which require annotations and struggle to capture dynamically evolving expressive intent throughout dialogue. To overcome these limitations, we propose SASLM (Self-Aware Speech Language Model), a proxy-free framework that bridges what the model thinks and how it speaks through self-aware intent-realization alignment: (1) Intent-Aware Bridging self-distills expressive intent from the model's own evolving semantic generation states via a Variational Information Bottleneck (VIB), thereby guiding expressive speech realization without external expressive supervision; while (2) Realization-Aware Alignment reflectively aligns generated acoustics with intended expression through self-reward optimization, progressively improving intent-realization consistency during speech generation. Despite using only 3B parameters and 800 hours of expressive speech data, SASLM achieves state-of-the-art performance on EchoMind among open-source systems, surpassing models over 10 times larger and approaching commercial systems.

cs.CL↗

arXiv2026

Max-Window Scale Estimation for Near-Lossless HiF8 W8A8 Quantization-Aware Training

Quantization-aware training (QAT) with low-bit floating-point formats enables efficient LLM deployment, yet introduces subtle failure modes invisible to standard training metrics. We present a systematic study of HiF8 W8A8 QAT for OpenPangu-Embedded-1B through the lens of Delayed Tensor Scaling (DTS). Across eight controlled experiments, we identify and disentangle two orthogonal failure modes: (i)amax saturation, where delayed scale estimates silently corrupt knowledge-sensitive representations via forward-pass clipping, and (ii)catastrophic forgetting, where an aggressive learning rate overwrites pretrained commonsense knowledge independently of quantization. Neither is detectable from training loss alone. We address amax saturation with a conservative max-algorithm DTS strategy over a 64-step history window, and mitigate forgetting via a 500-step BF16 warmup followed by QAT at lr=10^{-5}. Both fixes are necessary and sufficient: our final configuration achieves 0.43% MMLU drop, 0.58% HellaSwag drop, and 0.22% ARC-Challenge drop versus a matched BF16 baseline, with a training loss APE of only 0.11% over 10,000 steps.

cs.LG↗

arXiv2026

CAM-Bench: A Benchmark for Computational and Applied Mathematics in Lean

Formal theorem-proving benchmarks enable mechanically verifiable evaluation of mathematical reasoning in large language models. However, existing benchmarks mainly focus on Olympiad-style problems and algebraic domains, leaving computational and applied mathematics underrepresented. We introduce CAM-Bench, a Lean 4 theorem-proving benchmark of 1,000 Lean proof targets in computational and applied mathematics, with coverage spanning optimization, numerical linear algebra, and numerical analysis. These problems are adapted from textbook exercises and often depend on locally introduced definitions, notation, algorithms, and elementary results. To construct CAM-Bench, we develop a dependency-recovery pipeline that reconstructs the local textbook context needed to state each problem faithfully. It then normalizes each problem into a standalone informal theorem and translates it into a Lean target. We validate the resulting formal problems through Lean compilation and semantic review, checking both formal correctness and semantic alignment with the original exercises. For each problem, we release the raw exercise, recovered context, normalized informal theorem, and final Lean target. CAM-Bench complements existing formal mathematics benchmarks by targeting applied mathematics problems that rely on textbook concepts and elementary theorems, many of which are not directly available as standard Mathlib4 lemmas. We evaluate widely used large language models and formalization agents on CAM-Bench, and analyze common failure modes in tracking local assumptions, applying elementary results, decomposing proofs, and maintaining long-horizon control in Lean.

cs.AI↗

arXiv2026

Mixing plant for JUNO liquid scintillator: Design, construction, installation and commissioning

The most challenging part of building the Jiangmen Underground Neutrino Observatory (JUNO) is the production of 20 kilotons of ultra pure Liquid Scintillator (LS). This paper presents the design, construction, installation, and commissioning of the LS Mixing Plant, a core facility dedicated to blending the primary organic solvent (LAB) with essential functional solutes (PPO, bis-MSB, and BHT). The main purpose of the Mixing Plant is to prepare and purify the concentrated Master Solution (MS) to achieve a low radioactive contamination background. The amount of radioactive contaminants in the MS are lowered by approximately two orders of magnitude after acid and water extraction, followed by a multi-stage filtration procedure. The purified MS is mixed with LAB and then diluted into the LS for JUNO experiments. Commissioning results of the LS verify that the Mixing Plant achieved its design goal, delivering ultra pure LS that satisfies the stringent radiopurity requirements for neutrino physics.

physics.ins-det↗

arXiv2026

A Reality Check on SBOM-based Vulnerability Management: An Empirical Study and A Path Forward

The Software Bill of Materials (SBOM) is a critical tool for securing the software supply chain (SSC), but its practical utility is undermined by inaccuracies in both its generation and its application in vulnerability scanning. This paper presents a large-scale empirical study on 2,414 open-source repositories to address these issues from a practical standpoint. First, we demonstrate that using lock files with strong package managers enables the generation of accurate and consistent SBOMs, establishing a reliable foundation for security analysis. Using this high-fidelity foundation, however, we expose a more fundamental flaw in practice: downstream vulnerability scanners produce a staggering 92.0\% false positive rate in our case study. We pinpoint the primary cause as the flagging of vulnerabilities within unreachable code. We then demonstrate that function call analysis can effectively prune 61.9\% of these false alarms. Our work validates a practical, two-stage approach for SSC security: first, generate an accurate SBOM using lock files and strong package managers, and second, enrich it with function call analysis to produce actionable, low-noise vulnerability reports that alleviate developers' alert fatigue.

cs.CR↗

arXiv2026

FashionStylist: An Expert Knowledge-enhanced Multimodal Dataset for Fashion Understanding

Fashion understanding requires both visual perception and expert-level reasoning about style, occasion, compatibility, and outfit rationale. However, existing fashion datasets remain fragmented and task-specific, often focusing on item attributes, outfit co-occurrence, or weak textual supervision, and thus provide limited support for holistic outfit understanding. In this paper, we introduce FashionStylist, an expert-annotated benchmark for holistic and expert-level fashion understanding. Constructed through a dedicated fashion-expert annotation pipeline, FashionStylist provides professionally grounded annotations at both the item and outfit levels. It supports three representative tasks: outfit-to-item grounding, outfit completion, and outfit evaluation. These tasks cover realistic item recovery from complex outfits with layering and accessories, compatibility-aware composition beyond co-occurrence matching, and expert-level assessment of style, season, occasion, and overall coherence. Experimental results show that FashionStylist serves not only as a unified benchmark for multiple fashion tasks, but also as an effective training resource for improving grounding, completion, and outfit-level semantic evaluation in MLLM-based fashion systems.

cs.CV↗

arXiv2026

Synthesis imaging with a lunar orbit array: II. Impacts of instrument-induced phase errors

A lunar orbit interferometer array suffers from a number of systematics. Beyond systematics induced by the imaging algorithm itself and thermal noise considered in Paper I, phase errors due to instrumental inconsistency between receivers, geometric error in baseline determination, and clock synchronization error between satellites will also affect synthesis imaging with the space array. In this paper, we model different sources of phase errors and quantify their impacts on all-sky and patchy-sky map-making, respectively, for the ultra-long wavelength sky ($f\lesssim30$ MHz), using the Discovering the Sky at the Longest wavelength (DSL) mission (also known as the Hongmeng mission) as an example. We find that in the scheme of all-sky imaging, the angular power spectrum can be suppressed uniformly for various sources of phase errors. To ensure a reconstruction of large-scale structures with $\gtrsim 95\%$ of the angular power spectrum, the phase error should be controlled below $\sim 12^\circ$ on the random instrumental component, or below $\sim 12^\circ$ for constant deviation, or below $1.1$ ns on the temporal component. With multiple baseline measurements, the baseline determination errors below $1$ m can also meet the requirement. In the scheme of patchy-sky imaging, the S/N of point source detections does not change significantly, except with instrumental phase errors or at high frequencies. The impact of geometric phase error is relatively stronger in the patchy-sky imaging with higher resolution because longer baselines are used and fewer times of baseline measurements can be averaged over within an integration time. When scaled with wavelength, these results set the basic reference for instrumental requirements for future space interferometers.

astro-ph.IM↗

arXiv2026

Clinical Metadata Guided Limited-Angle CT Image Reconstruction

Limited-angle computed tomography (LACT) offers improved temporal resolution and reduced radiation dose for cardiac imaging, but suffers from severe artifacts due to truncated projections. To address the ill-posedness of LACT reconstruction, we propose a two-stage diffusion framework guided by structured clinical metadata. In the first stage, a transformer-based diffusion model conditioned exclusively on metadata, including acquisition parameters, patient demographics, and diagnostic impressions, generates coarse anatomical priors from noise. The second stage further refines the images by integrating both the coarse prior and metadata to produce high-fidelity results. Physics-based data consistency is enforced at each sampling step in both stages using an Alternating Direction Method of Multipliers module, ensuring alignment with the measured projections. Extensive experiments on both synthetic and real cardiac CT datasets demonstrate that incorporating metadata significantly improves reconstruction fidelity, particularly under severe angular truncation. Compared to existing metadata-free baselines, our method achieves superior performance in SSIM, PSNR, nMI, and PCC. Ablation studies confirm that different types of metadata contribute complementary benefits, particularly diagnostic and demographic priors under limited-angle conditions. These findings highlight the dual role of clinical metadata in improving both reconstruction quality and efficiency, supporting their integration into future metadata-guided medical imaging frameworks.

cs.CV↗

arXiv2026

High-resolution spectroscopic atmospheric studies of 5 hot Jupiters across the edge of the Neptune desert

Hot Jupiters (HJs), especially the Ultra-Hot Jupiters (UHJs), are ideal targets for robust atmospheric characterization, thanks to their high equilibrium temperatures and large atmospheric scale heights, which result from their proximity to their host stars and intense stellar irradiation. Here, we present atmospheric studies of five planets, namely WASP-50b, WASP-117b, WASP-156b, WASP-167b, and WASP-173Ab. These five planets include two UHJs, two classic HJs, and one hot Neptune, with four of them just on the upper and middle borders of the Neptune desert, providing an interesting sample for investigating the connection between planetary atmospheric composition and bulk properties. We have not detected any significant absorption signals exceeding 3$σ$ in the three less-inflated, relatively high-density HJs (WASP-50b, WASP-156b, and WASP-173Ab). We marginally detect H$α$ and Li I with 3.2$σ$ and 3.1$σ$ in WASP-117b, respectively. In WASP-167b, we report tentative detection of H$α$ and Fe I at 4.6$σ$ and $\sim3.4σ$, receptively. In addition, Fe I is significantly detected with a max SNR of 7.3 $σ$ using the cross-correlation technique, which exhibits a blue-shifted signal. For WASP-167b, we perform an atmospheric retrieval and yield the abundances of Fe, Mg, Ca, Ti, V, and equilibrium temperature of ${2479^{+193}_{-174}}$K. Comparing WASP-173Ab and WASP-167b, both are UHJ, but with quite different extents of atmospheric signals, we propose that there may be a transition in $T_{\rm eq}$ between 1900 and 2300K.

astro-ph.EP↗

arXiv2026

Small Target Detection Based on Mask-Enhanced Attention Fusion of Visible and Infrared Remote Sensing Images

Targets in remote sensing images are usually small, weakly textured, and easily disturbed by complex backgrounds, challenging high-precision detection with general algorithms. Building on our earlier ESM-YOLO, this work presents ESM-YOLO+ as a lightweight visible infrared fusion network. To enhance detection, ESM-YOLO+ includes two key innovations. (1) A Mask-Enhanced Attention Fusion (MEAF) module fuses features at the pixel level via learnable spatial masks and spatial attention, effectively aligning RGB and infrared features, enhancing small-target representation, and alleviating cross-modal misalignment and scale heterogeneity. (2) Training-time Structural Representation (SR) enhancement provides auxiliary supervision to preserve fine-grained spatial structures during training, boosting feature discriminability without extra inference cost. Extensive experiments on the VEDAI and DroneVehicle datasets validate ESM-YOLO+'s superiority. The model achieves 84.71\% mAP on VEDAI and 74.0\% mAP on DroneVehicle, while greatly reducing model complexity, with 93.6\% fewer parameters and 68.0\% lower GFLOPs than the baseline. These results confirm that ESM-YOLO+ integrates strong performance with practicality for real-time deployment, providing an effective solution for high-performance small-target detection in complex remote sensing scenes.

cs.CV↗

arXiv2026

EchoMind: An Interrelated Multi-level Benchmark for Evaluating Empathetic Speech Language Models

Speech Language Models (SLMs) have made significant progress in spoken language understanding. Yet it remains unclear whether they can fully perceive non lexical vocal cues alongside spoken words, and respond with empathy that aligns with both emotional and contextual factors. Existing benchmarks typically evaluate linguistic, acoustic, reasoning, or dialogue abilities in isolation, overlooking the integration of these skills that is crucial for human-like, emotionally intelligent conversation. We present EchoMind, the first interrelated, multi-level benchmark that simulates the cognitive process of empathetic dialogue through sequential, context-linked tasks: spoken-content understanding, vocal-cue perception, integrated reasoning, and response generation. All tasks share identical and semantically neutral scripts that are free of explicit emotional or contextual cues, and controlled variations in vocal style are used to test the effect of delivery independent of the transcript. EchoMind is grounded in an empathy-oriented framework spanning 3 coarse and 12 fine-grained dimensions, encompassing 39 vocal attributes, and evaluated using both objective and subjective metrics. Testing 12 advanced SLMs reveals that even state-of-the-art models struggle with high-expressive vocal cues, limiting empathetic response quality. Analyses of prompt strength, speech source, and ideal vocal cue recognition reveal persistent weaknesses in instruction-following, resilience to natural speech variability, and effective use of vocal cues for empathy. These results underscore the need for SLMs that integrate linguistic content with diverse vocal cues to achieve truly empathetic conversational ability.

cs.CL↗

arXiv2026

Thermal emission spectra of the ultra-hot Jupiter WASP-33 b

Observations of exoplanetary atmospheres provide critical insights into their chemical composition, formation and evolution history. Ultra-hot Jupiters serve as excellent targets for atmospheric characterization; studies of these planets may yield key understanding of gas giant's formation and evolution history. We present a thermal emission study of WASP-33 b's dayside atmosphere, based on two secondary eclipse observations with CFHT/WIRCam in two specific narrow band filters, namely the CO and CH4$_{\rm on}$ filters, and archival data with HST/WFC3 and Spitzer. Stellar pulsations of the host star induce some quasi-periodic photometric variations, particularly in the CH4$_{\rm on}$ band, which are modelled and corrected in the high-precision differential light curves. An eclipse depth of $1565.2^{+228.6}_{-237.5}$ ppm and $914.3^{+56.1}_{-57.0}$ ppm is determined for the CO and CH4$_{\rm on}$ bands, respectively. Combined with HST/WFC3 and Spitzer data, our joint retrieval of WASP-33 b's dayside atmosphere reveals a high metallicity ([Fe/H] $= 1.52^{+0.35}_{-0.52}$), high C/O ratio (C/O $= 0.78^{+0.03}_{-0.04}$), and a thermal inversion layer, suggesting a formation history involving metal-rich gas accretion. We confirm the presence of the molecules H$_{2}$O, H$^{-}$ and CO, and report a tentative detection of TiO in the dayside atmosphere of WASP-33 b. Future higher precision observations with JWST may provide better understand constraints on the chemical abundances of oxygen and refractory element abundances to better WASP-33 b's formation and evolutionary pathway.

astro-ph.EP↗

arXiv2026

Refinement orders for quantum programs

Refinement is a fundamental technique in the verification and systematic development of computer programs. It supports a disciplined approach to software construction through stepwise refinement, whereby an abstract specification is gradually transformed into a concrete implementation that satisfies the desired requirements. Central to this methodology is the notion of a refinement order, which guarantees that each refinement step preserves program correctness. This paper presents the first comprehensive study of refinement orders for quantum programs, covering both deterministic and nondeterministic settings under total and partial correctness criteria. We investigate three natural classes of quantum predicates: projectors, representing qualitative properties; effects, capturing quantitative properties; and sets of effects, modeling demonic nondeterminism. For deterministic quantum programs, we show that refinement with respect to effect-based and set-of-effects based specifications coincides with the standard complete-positivity order on superoperators, whereas refinement induced by projector-based specifications can be characterized by the linear span of Kraus operators. For nondeterministic quantum programs with set-of-effects based specifications, we establish precise correspondences with classical domain-theoretic notions: the Smyth order characterizes refinement under total correctness, while the Hoare order characterizes refinement under partial correctness. Moreover, effect-based and projector-based specifications lead to strictly weaker refinement orders.

cs.LO↗

arXiv2026

Adversarial Graph Traversal

Suppose a Bayesian agent seeks to traverse a graph. Each time she crosses an edge, she pays a price. The first time she reaches a node, there is a payoff. She has an opponent who can reduce the payoffs. This paper uses adversarial risk analysis to find a solution to her route selection problem. It shows how the traveler is advantaged by having an accurate subjective distribution over the costs/payoffs and by having a Bayesian prior for her opponent's strategic choices. The results are relevant to military convoy routing, corporate competition, and certain games.

math.OC↗

arXiv2026

AverageTime: Enhance Long-Term Time Series Forecasting with Simple Averaging

Multivariate long-term time series forecasting aims to predict future sequences by utilizing historical observations, with a core focus on modeling intra-sequence and cross-channel dependencies. Numerous studies have developed diverse architectures to capture these patterns, achieving significant improvements in forecasting accuracy. Among them, iTransformer, a representative method for channel information extraction, leverages the Transformer architecture to model channel-wise dependencies, thereby facilitating sequence transformation for enhanced forecasting performance. Building upon iTransformer's channel extraction concept, we propose AverageTime, a simple, efficient, and scalable forecasting model. Beyond iTransformer, AverageTime retains the original sequence information and reframes channel extraction as a stackable and extensible architecture. This allows the model to generate multiple novel sequences through various structural mechanisms, rather than being limited to transforming the original input. Moreover, the newly extracted sequences are not restricted to channel processing; other techniques such as series decomposition can also be incorporated to enhance predictive accuracy. Additionally, we introduce a channel clustering technique into AverageTime, which substantially improves training and inference efficiency with negligible performance loss. Experiments on real-world datasets demonstrate that with only two straightforward averaging operations, applied to both the extracted sequences and the original series. AverageTime surpasses state-of-the-art models in forecasting performance while maintaining near-linear complexity. This work offers a new perspective on time series forecasting: enriching sequence information through extraction and fusion. The source code is available at https://github.com/ UniqueoneZ/AverageTime.

cs.LG↗

arXiv2026

EmoShift: Lightweight Activation Steering for Enhanced Emotion-Aware Speech Synthesis

Achieving precise and controllable emotional expression is crucial for producing natural and context-appropriate speech in text-to-speech (TTS) synthesis. However, many emotion-aware TTS systems, including large language model (LLM)-based designs, rely on scaling fixed emotion embeddings or external guidance, limiting their ability to model emotion-specific latent characteristics. To address this gap, we present EmoShift, a lightweight activation-steering framework incorporating a EmoSteer layer, which learns a steering vector for each target emotion in the output embedding space to capture its latent offset and maintain stable, appropriate expression across utterances and categories. With only 10M trainable parameters,less than 1/30 of full fine-tuning, EmoShift outperforms zero-shot and fully fine-tuned baselines in objective and subjective evaluations, enhancing emotional expressiveness while preserving naturalness and speaker similarity. Further analysis confirms the proposed EmoSteer layer's effectiveness and reveals its potential for controllable emotional intensity in speech synthesis.

eess.AS↗

arXiv2026

Detecting Performance Degradation under Data Shift in Pathology Vision-Language Model

Vision-Language Models have demonstrated strong potential in medical image analysis and disease diagnosis. However, after deployment, their performance may deteriorate when the input data distribution shifts from that observed during development. Detecting such performance degradation is essential for clinical reliability, yet remains challenging for large pre-trained VLMs operating without labeled data. In this study, we investigate performance degradation detection under data shift in a state-of-the-art pathology VLM. We examine both input-level data shift and output-level prediction behavior to understand their respective roles in monitoring model reliability. To facilitate systematic analysis of input data shift, we develop DomainSAT, a lightweight toolbox with a graphical interface that integrates representative shift detection algorithms and enables intuitive exploration of data shift. Our analysis shows that while input data shift detection is effective at identifying distributional changes and providing early diagnostic signals, it does not always correspond to actual performance degradation. Motivated by this observation, we further study output-based monitoring and introduce a label-free, confidence-based degradation indicator that directly captures changes in model prediction confidence. We find that this indicator exhibits a close relationship with performance degradation and serves as an effective complement to input shift detection. Experiments on a large-scale pathology dataset for tumor classification demonstrate that combining input data shift detection and output confidence-based indicators enables more reliable detection and interpretation of performance degradation in VLMs under data shift. These findings provide a practical and complementary framework for monitoring the reliability of foundation models in digital pathology.

cs.CV↗

arXiv2025

Visionary: The World Model Carrier Built on WebGPU-Powered Gaussian Splatting Platform

Neural rendering, particularly 3D Gaussian Splatting (3DGS), has evolved rapidly and become a key component for building world models. However, existing viewer solutions remain fragmented, heavy, or constrained by legacy pipelines, resulting in high deployment friction and limited support for dynamic content and generative models. In this work, we present Visionary, an open, web-native platform for real-time various Gaussian Splatting and meshes rendering. Built on an efficient WebGPU renderer with per-frame ONNX inference, Visionary enables dynamic neural processing while maintaining a lightweight, "click-to-run" browser experience. It introduces a standardized Gaussian Generator contract, which not only supports standard 3DGS rendering but also allows plug-and-play algorithms to generate or update Gaussians each frame. Such inference also enables us to apply feedforward generative post-processing. The platform further offers a plug in three.js library with a concise TypeScript API for seamless integration into existing web applications. Experiments show that, under identical 3DGS assets, Visionary achieves superior rendering efficiency compared to current Web viewers due to GPU-based primitive sorting. It already supports multiple variants, including MLP-based 3DGS, 4DGS, neural avatars, and style transformation or enhancement networks. By unifying inference and rendering directly in the browser, Visionary significantly lowers the barrier to reproduction, comparison, and deployment of 3DGS-family methods, serving as a unified World Model Carrier for both reconstructive and generative paradigms.

cs.CV↗