arXiv subjects

Wentao Song

Publications and source records attributed to Wentao Song.

7 recordsLinked to original sources

arXiv2026

Compilation Is Not Correctness: Scan-by-Scan Evaluation of LLM-Generated PLC Programs

Programmable logic controllers (PLCs) automate industrial processes through repeated scans, each reading inputs, executing control logic, and updating outputs. Engineers write this logic in Structured Text (ST), a high-level textual language defined in IEC 61131-3. Large language models (LLMs) can now generate ST from natural-language requirements, yet compilation success and checks on selected properties or sampled scans can leave behavioral errors undetected. We introduce CycleBench, a benchmark of 301 ST tasks with behavioral requirements, public interfaces, reference programs, and executable scenarios. Its runtime executes each generated program and its reference under aligned inputs, initialization, and logical time, comparing their outputs after every scan. Across eight LLMs and baseline generation methods, execution pass rate trails compilation pass rate by 11.92 percentage points; observed failures include output-update, arithmetic, and timing errors. On fixed traces of programs directly generated by five LLMs, checking outputs only at every fifth scan misses 21.66% of the 157 failures detected by per-scan comparison; across sampling phases, the missed-failure rate ranges from 12.10% to 28.03%. These misses stem from transient output discrepancies, whose detection depends on the sampling phase. Building on this evaluation, we propose CycleGen, which generates multiple candidate ST programs, repairs their compilation errors with bounded compiler feedback, and selects one using runtime self-tests generated in a separate model context. CycleGen improves the execution pass rate over direct generation by 8.68 percentage points. Component ablations distinguish repair and self-test selection; candidate-count analysis shows diminishing returns, with no execution-pass-rate improvement beyond five candidates in our experiments despite 38.8% higher token consumption at eight.

cs.SE↗

arXiv2026

Rotational spectroscopy and astronomical search of propane-1-thiol (CH3CH2CH2SH) and propane-2-thiol ((CH3)2CHSH)

This study focuses on propanethiol (or propyl mercaptan, C3H7SH), a sulfur analogue of propanol and a key target for investigating interstellar sulfur chemistry. We report the laboratory rotational spectroscopic characterization of its two structural isomers, propane-1-thiol (1PT) and propane-2-thiol (2PT). For 1PT, experimental analysis led to the observation of three almost isoenergetic conformers, Aa,Ag and Gg, marking the first-time report of the latter. For 2PT, both the anti and gauche conformers were identified. We also assigned, for the first time, several monosubstituted isotopologues in natural abundance: the 34S, 13C1, and 13C2 species for the anti-2PT conformer, as well as the 34S species for the gauche-2PT conformer.Resolvable tunneling splitting, caused by the large-amplitude motion of the sulfhydryl (SH) group, was observed for both the Ag-1PT and gauche-2PT conformers. Global fittings, integrating previous measurements, allowed for the accurate determination of rotational constants, quartic centrifugal distortion constants, and vibrational coupling parameters. The newly derived spectroscopic parameters were utilized to conduct an astronomical search toward the chemically rich Galactic Center molecular cloud G+0.693-0.027. While no evidence for propanethiol was found, we established rigorous upper limits for their column densities. These results indicate that 1PT and 2PT are approximately 13 and 5 times less abundant than ethanethiol in this source, respectively. Furthermore, the derived lower limit for the O/S ratio (29) for the 1-propanol/1-propanethiol pair is consistent with the solar value (37), proviiding a valuable benchmark for understanding the depletion of sulfur in the ISM.

astro-ph.GA↗

arXiv2026

BRF-GS: Hyperspectral Bidirectional Reflectance Factor Modeling and Image Generation Based on 3D Gaussian Splatting

The bidirectional reflectance factor (BRF) characterizes the directional radiative properties of terrestrial surfaces. However, existing three-dimensional (3D) radiative transfer models require complex scene construction and computationally intensive radiative transfer solvers, limiting efficient generation of multi-angle hyperspectral reflectance imagery. 3D Gaussian Splatting (3DGS) offers an efficient framework for neural scene representation and novel view synthesis, but its low-order spherical harmonics representation is insufficient for complex directional reflectance, while the high dimensionality and inter-band quality differences of hyperspectral data introduce additional challenges. To address these challenges, we propose BRF-GS, a 3DGS-based framework for BRF modeling and hyperspectral reflectance image generation. BRF-GS introduces a hybrid BRDF-driven kernel to represent complex directional reflectance, selects geometry-reliable spectral bands for robust 3D scene initialization, and adopts a two-stage training strategy that decouples geometry optimization from spectral modeling. We further construct the AIR-BRF dataset, a multi-angle hyperspectral directional reflectance dataset comprising three scenes with diverse natural and artificial targets. Experiments demonstrate that BRF-GS achieves superior spatial and spectral fidelity and accurately reproduces characteristic view-dependent BRF responses. The proposed framework provides an efficient data-driven approach for BRF modeling and multi-angle hyperspectral reflectance image generation in remote sensing scenes.

cs.CV↗

arXiv2026

Universal 3:1 Scaling of Quantum-Confined Stark Spectra Revealed by a Three-Dimensional Profile

We report that the quantum-confined Stark effect spectrum exhibits a nearly rigid redshift while preserving its characteristic peak spacing patterns when increasing the electric field strength F. Using InGaN as a model system, we uncover two electric-field-independent scaling laws for the spectral peaks in both the sub-bandgap and above-bandgap regions and the coefficient ratio is near 3:1. With a novel three-dimensional (3D) visualization, we reveal that the sub-bandgap peak spacings scale as $\frac{12π\hbar^2}{L^2\sqrt{m_em_h}}$ while the above-bandgap peak spacings scale as $\frac{4π\hbar^2}{L^2\sqrt{m_em_h}}$, explaining the origin of the 3:1 ratio. This scaling behavior, validated in both InGaN and GaAs systems and at electroluminescence working conditions, shows that increasing F only expands the energy range and increases the number of peaks without altering the spacing. Beyond these laws, the 3D profile offers new insights into the Tauc background, Franz-Keldysh oscillations and coherence length, providing a powerful tool for the design and diagnostics of electro-optic devices.

cond-mat.mtrl-sci↗

arXiv2026

Attention Needs to Focus: A Unified Perspective on Attention Allocation

The Transformer architecture, a cornerstone of modern Large Language Models (LLMs), has achieved extraordinary success in sequence modeling, primarily due to its attention mechanism. However, despite its power, the standard attention mechanism is plagued by well-documented issues: representational collapse and attention sink. Although prior work has proposed approaches for these issues, they are often studied in isolation, obscuring their deeper connection. In this paper, we present a unified perspective, arguing that both can be traced to a common root -- improper attention allocation. We identify two failure modes: 1) Attention Overload, where tokens receive comparable high weights, blurring semantic features that lead to representational collapse; 2) Attention Underload, where no token is semantically relevant, yet attention is still forced to distribute, resulting in spurious focus such as attention sink. Building on this insight, we introduce Lazy Attention, a novel mechanism designed for a more focused attention distribution. To mitigate overload, it employs positional discrimination across both heads and dimensions to sharpen token distinctions. To counteract underload, it incorporates Elastic-Softmax, a modified normalization function that relaxes the standard softmax constraint to suppress attention on irrelevant tokens. Experiments on the FineWeb-Edu corpus, evaluated across nine diverse benchmarks, demonstrate that Lazy Attention successfully mitigates attention sink and achieves competitive performance compared to both standard attention and modern architectures, while reaching up to 59.58% attention sparsity.

cs.LG↗

arXiv2025

Sliding Window Attention Training for Efficient Large Language Models

Recent advances in transformer-based Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks. However, their quadratic computational complexity concerning sequence length remains a significant bottleneck for processing long documents. As a result, many efforts like sparse attention and state space models have been proposed to improve the efficiency of LLMs over long sequences. Though effective, these approaches compromise the performance or introduce structural complexity. This calls for a simple yet efficient model that preserves the fundamental Transformer architecture. To this end, we introduce SWAT, which enables efficient long-context handling via Sliding Window Attention Training. This paper first attributes the inefficiency of Transformers to the attention sink phenomenon resulting from the high variance of softmax operation. Then, we replace softmax with the sigmoid function and utilize a balanced ALiBi and Rotary Position Embedding for efficient information compression and retention. Experiments demonstrate that SWAT achieves SOTA performance compared with state-of-the-art linear recurrent architectures on eight benchmarks. Code is available at https://github.com/Fzkuji/swat-attention.

cs.CL↗

arXiv2025

IPENS:Interactive Unsupervised Framework for Rapid Plant Phenotyping Extraction via NeRF-SAM2 Fusion

Advanced plant phenotyping technologies play a crucial role in targeted trait improvement and accelerating intelligent breeding. Due to the species diversity of plants, existing methods heavily rely on large-scale high-precision manually annotated data. For self-occluded objects at the grain level, unsupervised methods often prove ineffective. This study proposes IPENS, an interactive unsupervised multi-target point cloud extraction method. The method utilizes radiance field information to lift 2D masks, which are segmented by SAM2 (Segment Anything Model 2), into 3D space for target point cloud extraction. A multi-target collaborative optimization strategy is designed to effectively resolve the single-interaction multi-target segmentation challenge. Experimental validation demonstrates that IPENS achieves a grain-level segmentation accuracy (mIoU) of 63.72% on a rice dataset, with strong phenotypic estimation capabilities: grain volume prediction yields R2 = 0.7697 (RMSE = 0.0025), leaf surface area R2 = 0.84 (RMSE = 18.93), and leaf length and width predictions achieve R2 = 0.97 and 0.87 (RMSE = 1.49 and 0.21). On a wheat dataset,IPENS further improves segmentation accuracy to 89.68% (mIoU), with equally outstanding phenotypic estimation performance: spike volume prediction achieves R2 = 0.9956 (RMSE = 0.0055), leaf surface area R2 = 1.00 (RMSE = 0.67), and leaf length and width predictions reach R2 = 0.99 and 0.92 (RMSE = 0.23 and 0.15). This method provides a non-invasive, high-quality phenotyping extraction solution for rice and wheat. Without requiring annotated data, it rapidly extracts grain-level point clouds within 3 minutes through simple single-round interactions on images for multiple targets, demonstrating significant potential to accelerate intelligent breeding efficiency.

cs.CV↗