arXiv subjects

Peng Du

Publications and source records attributed to Peng Du.

At least 19 recordsLinked to original sources

arXiv2026

Bridging Academia and Industry: A Comprehensive Benchmark for Attributed Graph Clustering

Attributed Graph Clustering (AGC) is a fundamental unsupervised task that integrates structural topology and node attributes to uncover latent patterns in graph-structured data. Despite its significance in industrial applications such as fraud detection and user segmentation, a significant chasm persists between academic research and real-world deployment. Current evaluation protocols suffer from the small-scale, high-homophily citation datasets, non-scalable full-batch training paradigms, and a reliance on supervised metrics that fail to reflect performance in label-scarce environments. To bridge these gaps, we present PyAGC, a comprehensive, production-ready benchmark and library designed to stress-test AGC methods across diverse scales and structural properties. We unify existing methodologies into a modular Encode-Cluster-Optimize framework and, for the first time, provide memory-efficient, mini-batch implementations for a wide array of state-of-the-art AGC algorithms. Our benchmark curates 12 diverse datasets, ranging from 2.7K to 111M nodes, specifically incorporating industrial graphs with complex tabular features and low homophily. Furthermore, we advocate for a holistic evaluation protocol that mandates unsupervised structural metrics and efficiency profiling alongside traditional supervised metrics. Battle-tested in high-stakes industrial workflows at Ant Group, this benchmark offers the community a robust, reproducible, and scalable platform to advance AGC research towards realistic deployment. The code and resources are publicly available via GitHub (https://github.com/Cloudy1225/PyAGC), PyPI (https://pypi.org/project/pyagc), and Documentation (https://pyagc.readthedocs.io). We invite readers to our latest survey (https://arxiv.org/abs/2603.20829) for the unified framework and industrial perspective that complement this benchmark.

cs.LG↗

arXiv2026

Noise-resistant off-resonance π-rotation realized in an NMR system

Precise manipulation of qubits (spins) requires strong suppression of noise arising from couplings between qubits and their environment, as well as from driving field fluctuations. We propose a double-pulse sequence and an eighteen-pulse sequence to realize a single spin pi-rotation under off-resonance conditions (instead of conventional on-resonance or near-resonance conditions). It is proved that the latter sequence performs much better than the former in simultaneously suppressing both dephasing and driving field noise. As a proof of principle, we experimentally demonstrate, by implementing both sequences in an NMR spin system with a polarized initial spin state, that the off-resonance pi-rotation fidelity reaches as high as 0.9992 +/- 0.0008 for the double-pulse sequence and 0.9997 +/- 0.0006 for the eighteen-pulse sequence. In addition, the noise-tolerance range of the eighteen-pulse sequence is about 20 percent of the driving field strength, approximately three times wider than that of the double-pulse sequence. Besides its potential application in the NMR community, our proposal provides an alternative practical way to accurately control qubits by suppressing the coexisting transversal and longitudinal noises.

quant-ph↗

arXiv2026

Denoising Blocks, Not Tokens: Efficient Compressed Continuous Diffusion with Branching Token Realization

Diffusion language models (DLMs) generate text through iterative parallel refinement, offering the potential for higher throughput than autoregressive (AR) decoding. However, most DLMs still maintain one generative state per token, so every denoising step processes a state sequence as long as the output sequence, limiting the throughput gains from parallel generation. Continuous DLMs provide an additional degree of freedom: a single continuous state can represent multiple tokens, allowing diffusion to operate on a much shorter latent sequence. We introduce \emph{Branching Latent Diffusion (BLD)}, which exploits this flexibility by compressing a 1024-token sequence into only 64 block latents, a $16\times$ reduction. BLD combines latent compression with \emph{branching token realization}, where each latent is decoded by a local AR branch and all branches run in parallel. Because strong compression makes joint latent generation difficult, BLD generates the latents in groups, conditioning each group on previously generated latents. In end-to-end evaluation on the same GPU, BLD reduces generation FLOPs by more than $80\times$ and increases throughput by more than $6\times$ relative to the similarly sized ELF-L baseline. Compared with the AR baseline, BLD achieves more than $6\times$ higher throughput and more than $4\times$ lower latency. Despite the compression, BLD maintains competitive local fluency and diversity, although long-range coherence remains challenging. Overall, BLD shows that moving diffusion from token-level states to compressed latent sequences can substantially improve the efficiency of long-sequence generation.

cs.LG↗

arXiv2026

Re4+ Luminescence as a Highly Sensitive Alternative to Ruby for Optical Pressure Sensing

Luminescence-based remote pressure sensing provides a powerful approach for pressure determination under conditions where conventional contact methods are difficult to implement. Although ruby remains the undisputed gold standard among luminescent pressure indicators, its relatively low pressure sensitivity and susceptibility to temperature variations represent important limitations. Therefore, in this work, Cs2HfCl6:Re4+ is proposed as an alternative luminescent pressure indicator exhibiting a 15-fold higher pressure sensitivity (6.93 nm GPa-1) than ruby while maintaining a comparable thermal sensitivity, making it a particularly attractive material for optical manometry. Furthermore, Cs2HfCl6:Re4+ enables ratiometric pressure readout, providing a high relative pressure sensitivity reaching 175.1% GPa-1. To the best of our knowledge, this work represents the first demonstration of Re4+ luminescence for pressure sensing, introducing a new class of luminescent pressure indicators and opening new opportunities for the development of highly sensitive optical manometers based on Re4+-activated materials.

cond-mat.mtrl-sci↗

arXiv2026

Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning

Palmyra x6 is a large language model optimized for use with enterprise-oriented agentic tasks. The model was built by post-training a Mixture-of-Experts base model with Anchored Supervised Fine-Tuning on a compact corpus of verified, synthetic tool-use trajectories, optimized with a Muon + Adam hybrid. The recipe is deliberately conservative and deliberately controlled: 626 trajectories, a single epoch, a low learning rate, and a KL anchor to the frozen base. The model shows substantial gains over the previous default model for Writer Agent, and compares favorably with several recent models on public benchmarks, scoring the highest on BFCL Core at $0.785$ and posts the highest six-benchmark mean of the cohort. Furthermore, the model has shown itself to be competitive or leading relative to comparators in our bias and safety evaluations.

cs.CL↗

arXiv2026

MidSurfNet: Learning Face Pairing for Mid-surface Abstraction of Thin-walled CAD Models

Mid-surface abstraction is an important preprocessing step for finite element analysis of thin-walled CAD models, and face pairing is its central subproblem. Existing face-pairing methods rely on handcrafted geometric criteria whose thresholds are hard to tune when a model has multiple local wall thicknesses; their groupings depend on threshold settings and processing order, so the same model can yield inconsistent results. We present MidSurfNet, a learning-based face-pairing method that couples a learned face-pair scorer with a deterministic face-group composition. The scorer evaluates every unordered face pair with two separately learned evidence streams: a geometry stream combining continuous pairing criteria with a conditional shape correction, and an attributed-topology stream over the B-Rep face-adjacency graph. A pair-conditioned gate fuses the two streams, and independent per-pair decisions retain opposing-face support relations at one operating threshold selected once on validation data, replacing rather than adding to the per-model thresholds of rule-based pipelines. Under a connected-and-bipartite condition, the composition stage organizes the retained relations into variable-cardinality m-to-n face groups, each independent of processing order for a fixed support graph and unique up to its two side labels. We also construct the MidSurf dataset, a benchmark of 1,575 manually annotated CAD models. On the test set, MidSurfNet attains a pair-level F1-Score of 87.32%, 23.22 percentage points above the strongest rule-based baseline, and an end-to-end Completion Rate of 75.42%, including 61.90% on the multi-wall-thickness category the evaluated rule-based implementations do not support. We demonstrate practical utility by generating mid-surfaces from the composed face groups through an industrial mid-surface API and running finite element analyses on the resulting shell models.

cs.GR↗

arXiv2026

TraceCAD: Trace-Guided Repair for Agentic CAD Generation

LLM-based CAD agents produce executable parametric programs, but their correction loops may lose evidence about satisfied requirements, faulty operations, and prior repairs. We introduce TraceCAD, a recovery layer that links requested features, modeling steps, failure evidence, and candidate outcomes as persistent state. TraceCAD diagnoses likely faulty operations, searches bounded edits in their dependency regions, validates candidates through execution and preservation checks, and retains successful and failed repair outcomes in reusable skill memory. On DeepCAD-derived benchmarks with 200-model ablations and a 1K-model comparison, TraceCAD achieves competitive geometric quality in terms of IoU, Chamfer distance, and Hausdorff distance. Removing persistent state nearly halves recovery score; removing localized search more than doubles geometric regression and doubles code-agent invocations. Initializing the skill store on disjoint training models further reduces retries, token cost, and latency. These results demonstrate that persistent, localized, and reusable recovery improves final CAD quality and repair reliability.

cs.AI↗

arXiv2026

CADIR: A Cross-Backend Editable Intermediate Representation for Agentic CAD Generation

Large language models have made it possible to generate executable computer-aided design (CAD) programs from natural-language descriptions or images. However, existing methods represent modeling processes as backend-specific sequential scripts with implicit dependencies or as static geometry, making it difficult to simultaneously preserve construction history, stable topological references, and feature-level editability across different CAD systems. We present CADIR, an agent-friendly executable intermediate representation for CAD generation and cross-backend editing. Built on the OCCT geometry kernel via OCP, CADIR provides explicit, compositional modeling operations and fine-grained execution diagnostics. During program execution, CADIR records modeling operations, parameter dependencies, constraints, and topology selections in a construction graph. To enable reliable cross-backend reconstruction, we introduce Geometric Signature Matching, which identifies corresponding edges and faces despite parameter changes and backend differences, allowing adapters to reconstruct native editable feature histories in FreeCAD, SolidWorks, and Fusion 360. Building on this representation, we further propose a construction-graph retrieval method for text and image queries that supports both full-graph and subgraph retrieval, enabling agents to leverage complete models and modeling substructures. Extensive experiments demonstrate that CADIR achieves higher geometric fidelity and execution reliability than existing CAD representations, that construction-graph retrieval further improves model generation quality, and that cross-backend editing enables reliable model reconstruction and post-reconstruction editing across multiple CAD environments.

cs.AI↗

arXiv2026

AirfoilGen: A valid-by-construction and performance-aware latent diffusion model for airfoil generation

Airfoil shape design is a fundamental task in aerospace engineering, with a direct impact on flight stability and fuel consumption. Deep learning has recently emerged as a promising tool for this task, but existing deep generative approaches remain limited in both geometric validity and physical controllability. They offer little control over the generated shapes, yielding invalid geometries, and they typically do not condition effectively on aerodynamic performance. To address these issues, this paper proposes AirfoilGen, a valid-by-construction and performance-aware latent diffusion model for airfoil. It first introduces a novel airfoil representation scheme, the circle sweeping representation, to constrain the generative process so that output shapes respect essential airfoil characteristics. It then enables explicit control over aerodynamic performance (e.g., lift and drag coefficients) by operating in a learned latent space: a transformer model encodes airfoil shapes into vector embeddings, and a conditional diffusion model denoises Gaussian noise into these latent embeddings while incorporating target aerodynamic performance. In addition, this paper presents a new dataset of over 200,000 airfoils, which is substantially larger than the widely used UIUC airfoil dataset (1,650 airfoils) and more suitable for training modern deep generative models. Experiments demonstrate that AirfoilGen enables airfoil generation with far greater geometric validity and aerodynamic performance controllability than previously achievable, with an average performance-conditioning accuracy of 98.41%.

cs.LG↗

arXiv2026

CADDesigner: Conceptual CAD Model Generation with a General-Purpose Agent

Computer-Aided Design (CAD) is widely used for conceptual design and parametric 3D modeling, but typically requires a high level of expertise from designers. To lower the entry barrier and facilitate early-stage CAD modeling, we present CADDesigner, an LLM-powered agent for conceptual CAD design. The agent accepts both textual descriptions and sketches as input, engaging in interactive dialogue with users to refine and clarify design requirements through comprehensive requirement analysis. Built upon a novel Explicit Context Imperative Paradigm (ECIP), the agent generates high-quality CAD modeling code. During the generation process, the agent incorporates iterative visual feedback to improve model quality. Generated design cases can be stored in a structured knowledge base, providing a mechanism for continual knowledge accumulation and future improvement of code generation. Experimental results show that CADDesigner achieves competitive performance and outperforms representative baselines on conceptual CAD model generation tasks.

cs.AI↗

arXiv2026

From Features to Transformers: Redefining Ranking for Scalable Impact

We present LiGR, a large-scale ranking framework developed at LinkedIn that brings state-of-the-art transformer-based modeling architectures into production. We introduce a modified transformer architecture that incorporates learned normalization and simultaneous set-wise attention to user history and ranked items. This architecture enables several breakthrough achievements, including: (1) the deprecation of most manually designed feature engineering, outperforming the prior state-of-the-art system using only few features (compared to hundreds in the baseline), (2) validation of the scaling law for ranking systems, showing improved performance with larger models, more training data, and longer context sequences, and (3) simultaneous joint scoring of items in a set-wise manner, leading to automated improvements in diversity. To enable efficient serving of large ranking models, we describe techniques to scale inference effectively using single-pass processing of user history and set-wise attention. We also summarize key insights from various ablation studies and A/B tests, highlighting the most impactful technical approaches.

cs.LG↗

arXiv2026

From Shallow Waters to Mariana Trench: A Survey of Bio-inspired Underwater Soft Robots

Sample Exploring the ocean environment holds profound significance in areas such as resource exploration and ecological protection. Underwater robots struggle with extreme water pressure and often cause noise and damage to the underwater ecosystem, while bio-inspired soft robots draw inspiration from aquatic creatures to address these challenges. These bio-inspired approaches enable robots to withstand high water pressure, minimize drag, operate with efficient manipulation and sensing systems, and interact with the environment in an eco-friendly manner. Consequently, bio-inspired soft robots have emerged as a promising field for ocean exploration. This paper reviews recent advancements in underwater bio-inspired soft robots, analyses their design considerations when facing different desired functions, bio-inspirations, ambient pressure, temperature, light, and biodiversity , and finally explores the progression from bio-inspired principles to practical applications in the field and suggests potential directions for developing the next generation of underwater soft robots.

cs.RO↗

arXiv2025

Sell It Before You Make It: Revolutionizing E-Commerce with Personalized AI-Generated Items

E-commerce has revolutionized retail, yet its traditional workflows remain inefficient, with significant resource costs tied to product design and inventory. This paper introduces a novel system deployed at Alibaba that uses AI-generated items (AIGI) to address these challenges with personalized text-to-image generation for e-commerce product design. AIGI enables an innovative business mode called "sell it before you make it", where merchants can design fashion items and generate photorealistic images with digital models based on textual descriptions. Only when the items have received a certain number of orders, do the merchants start to produce them, which largely reduces reliance on physical prototypes and thus accelerates time to market. For such a promising application, we identify the underlying key scientific challenge, i.e., capturing users' group-level personalized preferences towards multiple generated images. To this end, we propose a Personalized Group-Level Preference Alignment Framework for Diffusion Models (PerFusion). We first design PerFusion Reward Model for user preference estimation with a feature-crossing-based personalized plug-in. Then we develop PerFusion with a personalized adaptive network to model diverse preferences across users, and meanwhile derive the group-level preference optimization objective to model comparative behaviors among multiple images. Both offline and online experiments demonstrate the effectiveness of our proposed algorithm. The AI-generated items achieve over 13% relative improvements for both click-through rate and conversion rate, as well as 7.9% decrease in return rate, compared to their human-designed counterparts, validating the transformative potential of AIGI for e-commerce platforms.

cs.IR↗

arXiv2025

RLCAD: Reinforcement Learning Training Gym for Revolution Involved CAD Command Sequence Generation

A CAD command sequence is a typical parametric design paradigm in 3D CAD systems where a model is constructed by overlaying 2D sketches with operations such as extrusion, revolution, and Boolean operations. Although there is growing academic interest in the automatic generation of command sequences, existing methods and datasets only support operations such as 2D sketching, extrusion,and Boolean operations. This limitation makes it challenging to represent more complex geometries. In this paper, we present a reinforcement learning (RL) training environment (gym) built on a CAD geometric engine. Given an input boundary representation (B-Rep) geometry, the policy network in the RL algorithm generates an action. This action, along with previously generated actions, is processed within the gym to produce the corresponding CAD geometry, which is then fed back into the policy network. The rewards, determined by the difference between the generated and target geometries within the gym, are used to update the RL network. Our method supports operations beyond sketches, Boolean, and extrusion, including revolution operations. With this training gym, we achieve state-of-the-art (SOTA) quality in generating command sequences from B-Rep geometries.

cs.LG↗

arXiv2025

Enhancing Conversational Recommender Systems with Tree-Structured Knowledge and Pretrained Language Models

Recent advances in pretrained language models (PLMs) have significantly improved conversational recommender systems (CRS), enabling more fluent and context-aware interactions. To further enhance accuracy and mitigate hallucination, many methods integrate PLMs with knowledge graphs (KGs), but face key challenges: failing to fully exploit PLM reasoning over graph relationships, indiscriminately incorporating retrieved knowledge without context filtering, and neglecting collaborative preferences in multi-turn dialogues. To this end, we propose PCRS-TKA, a prompt-based framework employing retrieval-augmented generation to integrate PLMs with KGs. PCRS-TKA constructs dialogue-specific knowledge trees from KGs and serializes them into texts, enabling structure-aware reasoning while capturing rich entity semantics. Our approach selectively filters context-relevant knowledge and explicitly models collaborative preferences using specialized supervision signals. A semantic alignment module harmonizes heterogeneous inputs, reducing noise and enhancing accuracy. Extensive experiments demonstrate that PCRS-TKA consistently outperforms all baselines in both recommendation and conversational quality.

cs.AI↗

arXiv2025

Diffusion Transformer meets Multi-level Wavelet Spectrum for Single Image Super-Resolution

Discrete Wavelet Transform (DWT) has been widely explored to enhance the performance of image superresolution (SR). Despite some DWT-based methods improving SR by capturing fine-grained frequency signals, most existing approaches neglect the interrelations among multiscale frequency sub-bands, resulting in inconsistencies and unnatural artifacts in the reconstructed images. To address this challenge, we propose a Diffusion Transformer model based on image Wavelet spectra for SR (DTWSR). DTWSR incorporates the superiority of diffusion models and transformers to capture the interrelations among multiscale frequency sub-bands, leading to a more consistence and realistic SR image. Specifically, we use a Multi-level Discrete Wavelet Transform to decompose images into wavelet spectra. A pyramid tokenization method is proposed which embeds the spectra into a sequence of tokens for transformer model, facilitating to capture features from both spatial and frequency domain. A dual-decoder is designed elaborately to handle the distinct variances in low-frequency and high-frequency sub-bands, without omitting their alignment in image generation. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness of our method, with high performance on both perception quality and fidelity.

cs.CV↗

arXiv2025

Constructing a bifunctional platform based on Mn2+-doped Mg2Y8(SiO4)6O2 phosphors for multi-parameter optical thermometry and manometry

Series of the Mn2+-doped Mg2Y8(SiO4)6O2 phosphors were synthesized. Upon excitation at 408 nm, these phosphors exhibited intense orange emission originating from Mn2+, with concentration quenching observed beyond x = 0.07, and they also demonstrated excellent thermal stability. For optical thermometry, two independent parameters, emission band centroid (λ) and lifetime, were employed as thermal indicators, yielding sensitivities of dλ/dT = 0.053 nm K-1 and SR = 0.86% K-1, respectively. High-pressure in-situ X-ray diffraction revealed that the phosphors retained structural integrity under compression, accompanied by a progressive lattice contraction. With increasing pressure (0.13-10.89 GPa), a spectral red-shift was observed, corresponding to a pressure sensitivity of dλ/dp = 4.75 nm GPa-1. Additionally, pressure-dependent shifts in color coordinates allowed the development of a colorimetric manometric response, achieving a relative sensitivity of 3.27% GPa-1. Remarkably, the pressure-induced spectral shift of Mn2+ emission, characterized by low thermal cross-sensitivity, enabled a highly reliable ratiometric manometric strategy, with a relative sensitivity of 72% GPa-1. Notably, the system delivered the highest TIMF reported to date above 3 GPa, peaking at 1940 K GPa-1 at 7 GPa. These results position Mn2+-doped Mg2Y8(SiO4)6O2 phosphors as a highly promising bifunctional material for next-generation, multi-parameter optical sensing applications under extreme conditions.

cond-mat.mtrl-sci↗

arXiv2025

GACO-CAD: Geometry-Augmented and Conciseness-Optimized CAD Model Generation from Single Image

Generating editable, parametric CAD models from a single image holds great potential to lower the barriers of industrial concept design. However, current multi-modal large language models (MLLMs) still struggle with accurately inferring 3D geometry from 2D images due to limited spatial reasoning capabilities. We address this limitation by introducing GACO-CAD, a novel two-stage post-training framework. It is designed to achieve a joint objective: simultaneously improving the geometric accuracy of the generated CAD models and encouraging the use of more concise modeling procedures. First, during supervised fine-tuning, we leverage depth and surface normal maps as dense geometric priors, combining them with the RGB image to form a multi-channel input. In the context of single-view reconstruction, these priors provide complementary spatial cues that help the MLLM more reliably recover 3D geometry from 2D observations. Second, during reinforcement learning, we introduce a group length reward that, while preserving high geometric fidelity, promotes the generation of more compact and less redundant parametric modeling sequences. A simple dynamic weighting strategy is adopted to stabilize training. Experiments on the DeepCAD and Fusion360 datasets show that GACO-CAD achieves state-of-the-art performance under the same MLLM backbone, consistently outperforming existing methods in terms of code validity, geometric accuracy, and modeling conciseness.

cs.CV↗