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Peng Jiang

Publications and source records attributed to Peng Jiang.

At least 73 records · Page 4Linked to original sources
arXiv2026

R&F-Inventory: A Large-Scale Dataset for Monotonic Inventory Estimation in Reach and Frequency Advertising

Reach and Frequency (R&F) contract advertising is an important form of widely used brand advertising. Unlike performance advertising, R&F contracts emphasize controllable delivery of UV and PV under given targeting, scheduling, and frequency control constraints. In practical systems, advertisers typically need to view the UV, PV change curves at different budget levels in real time when creating an R&F contract. However, most existing publicly available advertising datasets are based on independent samples, lacking a characterization of the core structure of the "budget-performance curve" (including UV and PV) in R&F contracts.This paper proposes and releases a large-scale R&F contract inventory estimation dataset. This dataset uses the R&F contract context consisting of "targeting-scheduling-frequency control" as the basic context, providing observations of UV and PV corresponding to multiple budget points within the same context, thus forming a complete budget-performance curve. The dataset explicitly includes a time-window-based frequency control mechanism (e.g.,"no more than 3 times within 5 days") and naturally satisfies the monotonicity and diminishing marginal returns characteristics in the budget and scheduling dimensions. We further derive the theoretical maximum exposure ceiling and use it as a consistency check to evaluate data quality and the feasibility of model predictions. Using this data set, this paper defines two standardized benchmark tasks: single-point performance prediction and reconstruction of budget-performance curves, and provides a set of reproducible baseline methods and evaluation protocols. This dataset can support systematic research on problems such as structural constraint learning, monotonic regression, curve consistency modeling, and R&F contract planning.The code for our experiments can be found at https://github.com/pengyunshan/RF-Inventory.

cs.LG↗
arXiv2026

Generative Recommendation for Large-Scale Advertising

Generative recommendation has recently attracted widespread attention in industry due to its potential for scaling and stronger model capacity. However, deploying real-time generative recommendation in large-scale advertising requires designs beyond large-language-model (LLM)-style training and serving recipes. We present a production-oriented generative recommender co-designed across architecture, learning, and serving, named GR4AD (Generative Recommendation for ADdvertising). As for tokenization, GR4AD proposes UA-SID (Unified Advertisement Semantic ID) to capture complicated business information. Furthermore, GR4AD introduces LazyAR, a lazy autoregressive decoder that relaxes layer-wise dependencies for short, multi-candidate generation, preserving effectiveness while reducing inference cost, which facilitates scaling under fixed serving budgets. To align optimization with business value, GR4AD employs VSL (Value-Aware Supervised Learning) and proposes RSPO (Ranking-Guided Softmax Preference Optimization), a ranking-aware, list-wise reinforcement learning algorithm that optimizes value-based rewards under list-level metrics for continual online updates. For online inference, we further propose dynamic beam serving, which adapts beam width across generation levels and online load to control compute. Large-scale online A/B tests show up to 4.2% ad revenue improvement over an existing DLRM-based stack, with consistent gains from both model scaling and inference-time scaling. GR4AD has been fully deployed in Kuaishou advertising system with over 400 million users and achieves high-throughput real-time serving.

cs.IR↗
arXiv2026

AutoCut: End-to-end advertisement video editing based on multimodal discretization and controllable generation

Short-form videos have become a primary medium for digital advertising, requiring scalable and efficient content creation. However, current workflows and AI tools remain disjoint and modality-specific, leading to high production costs and low overall efficiency. To address this issue, we propose AutoCut, an end-to-end advertisement video editing framework based on multimodal discretization and controllable editing. AutoCut employs dedicated encoders to extract video and audio features, then applies residual vector quantization to discretize them into unified tokens aligned with textual representations, constructing a shared video-audio-text token space. Built upon a foundation model, we further develop a multimodal large language model for video editing through combined multimodal alignment and supervised fine-tuning, supporting tasks covering video selection and ordering, script generation, and background music selection within a unified editing framework. Finally, a complete production pipeline converts the predicted token sequences into deployable long video outputs. Experiments on real-world advertisement datasets show that AutoCut reduces production cost and iteration time while substantially improving consistency and controllability, paving the way for scalable video creation.

cs.CV↗
arXiv2026

RRAT J1541+4703: A Rotating Radio Transient Exhibiting Normal Pulsar States

Rotating Radio Transients (RRATs) are a class of pulsar-like objects characterized by intermittent radio emissions. Among them, RRATs that exhibit both RRAT and normal pulsar (NP) states may represent a key evolutionary stage from nulling pulsars to RRATs. We performed a detailed analysis of RRAT J1574+4703 using the Five-hundred-meter Aperture Spherical Radio Telescope (FAST) at a frequency of 1250 MHz. Our findings indicate that this RRAT spends approximately 98% of its time in the RRAT state, with the remainder spent in an NP state exhibiting nulling behavior. Additionally, we observed distinct integral pulse profiles and polarization properties between the two states, suggesting that they originate from different emission heights and magnetospheric structures. Furthermore, it was observed that the NP states of this RRAT exhibit mode switching, with ~44% of the time spent in the normal mode and ~39% in the abnormal mode. Notably, abnormal modes are predominantly detected at the onset and termination of the NP states. This discrepancy between the modes indicates potential instability in the magnetospheric processes that govern the NP states.

astro-ph.HE↗
arXiv2026

Adaptive Video Distillation: Mitigating Oversaturation and Temporal Collapse in Few-Step Generation

Video generation has recently emerged as a central task in the field of generative AI. However, the substantial computational cost inherent in video synthesis makes model distillation a critical technique for efficient deployment. Despite its significance, there is a scarcity of methods specifically designed for video diffusion models. Prevailing approaches often directly adapt image distillation techniques, which frequently lead to artifacts such as oversaturation, temporal inconsistency, and mode collapse. To address these challenges, we propose a novel distillation framework tailored specifically for video diffusion models. Its core innovations include: (1) an adaptive regression loss that dynamically adjusts spatial supervision weights to prevent artifacts arising from excessive distribution shifts; (2) a temporal regularization loss to counteract temporal collapse, promoting smooth and physically plausible sampling trajectories; and (3) an inference-time frame interpolation strategy that reduces sampling overhead while preserving perceptual quality. Extensive experiments and ablation studies on the VBench and VBench2 benchmarks demonstrate that our method achieves stable few-step video synthesis, significantly enhancing perceptual fidelity and motion realism. It consistently outperforms existing distillation baselines across multiple metrics.

cs.CV↗
arXiv2026

From emission to absorption: the FAST observation of the OH 18-cm lines from the Comet C2025/A6

We observed comet C/2025 A6 with the Five-hundred-meter Aperture Spherical radio Telescope (FAST) equipped with the ultra-wideband receiver from 2025 October 23 to November 8, and it was the first detection for this comet with FAST.Through trapezoidal fitting of the OH line profiles, we derived the expansion velocities of the water which showed an increase from 1.5$\pm$0.3 km s$^{-1}$ at the heliocentric distance of 0.65 AU to 3.0$\pm$0.9 km s$^{-1}$ at 0.54 AU. Based on these results, we estimated the OH production rates of C/2025 A6 for October 23, October 26, November 4 and November 5 which were (1.0$\pm$0.1)$\times$10$^{29}$, (1.2$\pm$0.1)$\times$10$^{29}$, (1.4$\pm$0.3)$\times$10$^{29}$, and (1.5$\pm$0.4)$\times$10$^{29}$ s$^{-1}$ respectively. The results show a significant upward trend.

astro-ph.EP↗
arXiv2026

A Candidate Open Cluster Pulsar: Timing Analysis of PSR J1922+3745 in NGC 6791

PSR J1922+3745 was recently identified as a radio pulsar toward the old open cluster NGC 6791, raising the prospect of the first pulsar associated with an open cluster. We report FAST follow-up observations that yield a phase-coherent timing solution, a precise position, a measurement of the spin-down rate and the pulsar's polarization properties. PSR J1922+3745 is consistent with an isolated slow pulsar with a characteristic age of 7.8 Myr, comparable to the small population of long-period pulsars found in globular clusters. Motivated by the potential cluster association, we re-process deeper searches of the NGC 6791 field at higher sensitivity but detect no additional pulsars. We also assess whether HI absorption spectroscopy can provide a useful distance constraint and find that such measurements are unlikely to be constraining with currently available sensitivity. Consequently, existing evidence does not yet establish membership in NGC 6791. Further deep searches for additional pulsars with similar dispersion measures in the cluster field will likely be the most direct path to confirming a physical association.

astro-ph.HE↗
arXiv2026

Narrative Weaver: Towards Controllable Long-Range Visual Consistency with Multi-Modal Conditioning

We present "Narrative Weaver", a novel framework that addresses a fundamental challenge in generative AI: achieving multi-modal controllable, long-range, and consistent visual content generation. While existing models excel at generating high-fidelity short-form visual content, they struggle to maintain narrative coherence and visual consistency across extended sequences - a critical limitation for real-world applications such as filmmaking and e-commerce advertising. Narrative Weaver introduces the first holistic solution that seamlessly integrates three essential capabilities: fine-grained control, automatic narrative planning, and long-range coherence. Our architecture combines a Multimodal Large Language Model (MLLM) for high-level narrative planning with a novel fine-grained control module featuring a dynamic Memory Bank that prevents visual drift. To enable practical deployment, we develop a progressive, multi-stage training strategy that efficiently leverages existing pre-trained models, achieving state-of-the-art performance even with limited training data. Recognizing the absence of suitable evaluation benchmarks, we construct and release the E-commerce Advertising Video Storyboard Dataset (EAVSD) - the first comprehensive dataset for this task, containing over 330K high-quality images with rich narrative annotations. Through extensive experiments across three distinct scenarios (controllable multi-scene generation, autonomous storytelling, and e-commerce advertising), we demonstrate our method's superiority while opening new possibilities for AI-driven content creation.

cs.CV↗
arXiv2026

LBM: Hierarchical Large Auto-Bidding Model via Reasoning and Acting

The growing scale of ad auctions on online advertising platforms has intensified competition, making manual bidding impractical and necessitating auto-bidding to help advertisers achieve their economic goals. Current auto-bidding methods have evolved to use offline reinforcement learning or generative methods to optimize bidding strategies, but they can sometimes behave counterintuitively due to the black-box training manner and limited mode coverage of datasets, leading to challenges in understanding task status and generalization in dynamic ad environments. Large language models (LLMs) offer a promising solution by leveraging prior human knowledge and reasoning abilities to improve auto-bidding performance. However, directly applying LLMs to auto-bidding faces difficulties due to the need for precise actions in competitive auctions and the lack of specialized auto-bidding knowledge, which can lead to hallucinations and suboptimal decisions. To address these challenges, we propose a hierarchical Large autoBidding Model (LBM) to leverage the reasoning capabilities of LLMs for developing a superior auto-bidding strategy. This includes a high-level LBM-Think model for reasoning and a low-level LBM-Act model for action generation. Specifically, we propose a dual embedding mechanism to efficiently fuse two modalities, including language and numerical inputs, for language-guided training of the LBM-Act; then, we propose an offline reinforcement fine-tuning technique termed GQPO for mitigating the LLM-Think's hallucinations and enhancing decision-making performance without simulation or real-world rollout like previous multi-turn LLM-based methods. Experiments demonstrate the superiority of a generative backbone based on our LBM, especially in an efficient training manner and generalization ability.

cs.CL↗
arXiv2026

RAIN: Secure and Robust Aggregation under Shuffle Model of Differential Privacy

Secure aggregation is a foundational building block of privacy-preserving learning, yet achieving robustness under adversarial behavior remains challenging. Modern systems increasingly adopt the shuffle model of differential privacy (Shuffle-DP) to locally perturb client updates and globally anonymize them via shuffling for enhanced privacy protection. However, these perturbations and anonymization distort gradient geometry and remove identity linkage, leaving systems vulnerable to adversarial poisoning attacks. Moreover, the shuffler, typically a third party, can be compromised, undermining security against malicious adversaries. To address these challenges, we present Robust Aggregation in Noise (RAIN), a unified framework that reconciles privacy, robustness, and verifiability under Shuffle-DP. At its core, RAIN adopts sign-space aggregation to robustly measure update consistency and limit malicious influence under noise and anonymization. Specifically, we design two novel secret-shared protocols for shuffling and aggregation that operate directly on additive shares and preserve Shuffle-DP's tight privacy guarantee. In each round, the aggregated result is verified to ensure correct aggregation and detect any selective dropping, achieving malicious security with minimal overhead. Extensive experiments across comprehensive benchmarks show that RAIN maintains strong privacy guarantees under Shuffle-DP and remains robust to poisoning attacks with negligible degradation in accuracy and convergence. It further provides real-time integrity verification with complete tampering detection, while achieving up to 90x lower communication cost and 10x faster aggregation compared with prior work.

cs.CR↗
arXiv2026

Stylos: Multi-View 3D Stylization with Single-Forward Gaussian Splatting

We present Stylos, a single-forward 3D Gaussian framework for 3D style transfer that operates on unposed content, from a single image to a multi-view collection, conditioned on a separate reference style image. Stylos synthesizes a stylized 3D Gaussian scene without per-scene optimization or precomputed poses, achieving geometry-aware, view-consistent stylization that generalizes to unseen categories, scenes, and styles. At its core, Stylos adopts a Transformer backbone with two pathways: geometry predictions retain self-attention to preserve geometric fidelity, while style is injected via global cross-attention to enforce visual consistency across views. With the addition of a voxel-based 3D style loss that aligns aggregated scene features to style statistics, Stylos enforces view-consistent stylization while preserving geometry. Experiments across multiple datasets demonstrate that Stylos delivers high-quality zero-shot stylization, highlighting the effectiveness of global style-content coupling, the proposed 3D style loss, and the scalability of our framework from single view to large-scale multi-view settings. Our codes are available at https://github.com/HanzhouLiu/Stylos.

cs.CV↗
arXiv2026

Hierarchical Semantic RL: Tackling the Problem of Dynamic Action Space for RL-based Recommendations

Recommender Systems (RS) are fundamental to modern online services. While most existing approaches optimize for short-term engagement, recent work has begun to explore reinforcement learning (RL) to model long-term user value. However, these efforts face significant challenges due to the vast, dynamic action spaces inherent in RS, which hinder stable policy learning. To resolve this bottleneck, we introduce Hierarchical Semantic RL (HSRL), which reframes RL-based recommendation over a fixed Semantic Action Space (SAS). HSRL encodes items as Semantic IDs (SIDs) for policy learning, and maps SIDs back to their original items via a fixed lookup during execution. To align decision-making with SID generation, the Hierarchical Policy Network (HPN) operates in a coarse-to-fine manner, employing hierarchical residual state modeling to refine each level's context from the previous level's residual, thereby reducing representation-decision mismatch. In parallel, a Multi-level Critic (MLC) provides token-level value estimates, enabling fine-grained credit assignment. Across public benchmarks and a large-scale production dataset from a leading short-video advertising platform, HSRL consistently surpasses state-of-the-art baselines. In online deployment over a 7-day A/B testing, it delivers an 18.421% ADVV lift and a 1.251% increase in Revenue, supporting HSRL as a scalable paradigm for RL-based recommendation.

cs.IR↗
arXiv2026

Navigate the Unknown: Enhancing LLM Reasoning with Intrinsic Motivation Guided Exploration

Reinforcement Learning (RL) has become a key approach for enhancing the reasoning capabilities of large language models. However, prevalent RL approaches like proximal policy optimization and group relative policy optimization suffer from sparse, outcome-based rewards and weak exploration incentives, limiting their effectiveness. Specifically, sparse rewards offer limited feedback, especially on difficult problems, and introduce biases favoring familiar trajectories over novel reasoning paths. These issues critically undermine performance on complex tasks that inherently require iterative reasoning. To overcome these challenges, we propose Intrinsic MotivAtion Guided exploratIoN for Enhanced reasoning (IMAGINE), which delivers dense rewards and encourages exploration. IMAGINE introduces three innovations: a trajectory-aware exploration reward that reduces token-level bias efficiently; an error-conditioned reward allocation that promotes efficient exploration on hard samples while stabilizing training; and an advantage-preserving integration mechanism that retains distributional integrity during learning. Experiments on four public datasets show that IMAGINE improves performance by 22.23% on AIME 2024.

cs.LG↗
arXiv2026

Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation

Multimodal recommendation aims to integrate collaborative signals with heterogeneous content such as visual and textual information, but remains challenged by modality-specific noise, semantic inconsistency, and unstable propagation over user-item graphs. These issues are often exacerbated by naive fusion or shallow modeling strategies, leading to degraded generalization and poor robustness. While recent work has explored the frequency domain as a lens to separate stable from noisy signals, most methods rely on static filtering or reweighting, lacking the ability to reason over spectral structure or adapt to modality-specific reliability. To address these challenges, we propose a Structured Spectral Reasoning (SSR) framework for frequency-aware multimodal recommendation. Our method follows a four-stage pipeline: (i) Decompose graph-based multimodal signals into spectral bands via graph-guided transformations to isolate semantic granularity; (ii) Modulate band-level reliability with spectral band masking, a training-time masking with a prediction-consistency objective that suppresses brittle frequency components; (iii) Fuse complementary frequency cues using hyperspectral reasoning with low-rank cross-band interaction; and (iv) Align modality-specific spectral features via contrastive regularization to promote semantic and structural consistency. Experiments on three real-world benchmarks show consistent gains over strong baselines, particularly under sparse and cold-start settings. Additional analyses indicate that structured spectral modeling improves robustness and provides clearer diagnostics of how different bands contribute to performance.

cs.IR↗
arXiv2026

FITMM: Adaptive Frequency-Aware Multimodal Recommendation via Information-Theoretic Representation Learning

Multimodal recommendation aims to enhance user preference modeling by leveraging rich item content such as images and text. Yet dominant systems fuse modalities in the spatial domain, obscuring the frequency structure of signals and amplifying misalignment and redundancy. We adopt a spectral information-theoretic view and show that, under an orthogonal transform that approximately block-diagonalizes bandwise covariances, the Gaussian Information Bottleneck objective decouples across frequency bands, providing a principled basis for separate-then-fuse paradigm. Building on this foundation, we propose FITMM, a Frequency-aware Information-Theoretic framework for multimodal recommendation. FITMM constructs graph-enhanced item representations, performs modality-wise spectral decomposition to obtain orthogonal bands, and forms lightweight within-band multimodal components. A residual, task-adaptive gate aggregates bands into the final representation. To control redundancy and improve generalization, we regularize training with a frequency-domain IB term that allocates capacity across bands (Wiener-like shrinkage with shut-off of weak bands). We further introduce a cross-modal spectral consistency loss that aligns modalities within each band. The model is jointly optimized with the standard recommendation loss. Extensive experiments on three real-world datasets demonstrate that FITMM consistently and significantly outperforms advanced baselines.

cs.IR↗
arXiv2026

C2:Cross learning module enhanced decision transformer with Constraint-aware loss for auto-bidding

Decision Transformer (DT) shows promise for generative auto-bidding by capturing temporal dependencies, but suffers from two critical limitations: insufficient cross-correlation modeling among state, action, and return-to-go (RTG) sequences, and indiscriminate learning of optimal/suboptimal behaviors. To address these, we propose C2, a novel framework enhancing DT with two core innovations: (1) a Cross Learning Block (CLB) via cross-attention to strengthen inter-sequence correlation modeling; (2) a Constraint-aware Loss (CL) incorporating budget and Cost-Per-Acquisition (CPA) constraints for selective learning of optimal trajectories. Extensive offline evaluations on the AuctionNet dataset demonstrate consistent performance gains (up to 3.2% over state-of-the-art method) across diverse budget settings; ablation studies verify the complementary synergy of CLB and CL, confirming C2's superiority in auto-bidding. The code for reproducing our results is available at: https://github.com/Dingjinren/C2.

cs.LG↗
arXiv2026

Aggregate and Broadcast: Scalable and Efficient Feature Interaction for Recommender Systems

Feature interaction is a core ingredient in ranking models for large-scale recommender systems, yet making it both expressive and efficiently scalable remains challenging. Exhaustive pairwise interaction is powerful but incurs quadratic complexity in the number of tokens/features, while many efficient alternatives rely on restrictive structures that limit information exchange. We further identify two common bottlenecks in practice: (1) early aggregation of behavior sequences compresses fine-grained signals, making it difficult for deeper layers to reuse item-level details; and (2) late fusion injects task signals only at the end, preventing task objectives from directly guiding the interaction process. To address these issues, we propose the Information Flow Network (INFNet), a lightweight architecture that enables scalable, task-aware feature interaction with linear complexity. INFNet represents categorical features, behavior sequences, and task identifiers as tokens, and introduces a small set of hub tokens for each group to serve as communication hubs. Interaction is realized through an efficient aggregate-and-broadcast information flow: hub tokens aggregate global context across groups via cross-attention, and a lightweight gated broadcast unit injects the refined context back to update the categorical, sequence, and task tokens. This design supports width-preserving stacking that preserves item-level signals in sequence and enables task-guided interaction throughout the network, while reducing interaction cost from quadratic to linear in the number of feature tokens. Experiments on a public benchmark and a large-scale industrial dataset demonstrate that INFNet consistently outperforms strong baselines and exhibits strong scaling behavior. In a commercial online advertising system, deploying INFNet improves revenue by +1.587% and click-through rate by +1.155%.

cs.IR↗
arXiv2026

Experimental investigation of wall-pressure fluctuations on a fully appended submarine model at high Reynolds numbers

This paper addresses a critical gap in hydroacoustics through a systematic wind tunnel investigation of wall-pressure fluctuations on the fully appended DARPA SUBOFF model at operationally relevant Reynolds numbers ranging from $5.6 \times 10^{6}$ to $1.4 \times 10^{7}$. The experimental campaign encompasses baseline straight-ahead flow, complex maneuvering (yaw and pitch) conditions, and a first-of-its-kind assessment of a novel vortex control baffle (VCB). To ensure benchmark-quality spectral data, rigorous signal processing techniques were applied, specifically Wiener filtering for background noise suppression and dynamic transfer function correction for pinhole sensors. Key findings indicate that while spectral self-similarity holds across Reynolds numbers, the primary finding is the critical role of appendages in noise amplification. Unstable horseshoe vortex dynamics at the sail-hull junction drive localized pressure fluctuations of up to 300%, establishing this feature as a major coherent noise source. To address this, the study provides the pioneering experimental validation of the VCB. By physically suppressing horseshoe vortex formation at the sail-hull junction, the VCB achieves a global stabilization of the downstream flow, resulting in a significant 35% reduction in root-mean-square wall-pressure fluctuations at the stern and an approximately 14% reduction along the parallel mid-body. Furthermore, maneuvering conditions are shown to fundamentally reshape the pressure field, introducing substantial crossflow effects and non-monotonic spectral behaviors. This comprehensive dataset and the demonstrated efficacy of the VCB provide essential physical insights and a critical validation benchmark for the design of next-generation quiet submarines.

physics.flu-dyn↗