Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.03290

UniCon: A Unified Context-Centric Modeling Paradigm for CTR Prediction

Jiajun Cui·Zhengqi Xu·Fan Zhang·Zhangteng·Gu Tang·Honghong Zhu·Mengxi Wu·Yulin Liang·Xingxing Wang
View original source ↗Read source document ↗Export metadata ↓

Abstract

Unified modeling has become a major direction for industrial click-through rate (CTR) prediction. Existing approaches typically unify sequential and non-sequential signals at the token level, model their interactions in a shared backbone, and increase model capacity to improve scaling behavior. However, this division originates from legacy feature-engineering practice and is misaligned with the underlying decision process. User behavior is inherently a sequence of homogeneous context units; at the level of input organization, historical behavior and the current request differ only in whether their outcomes are observed or remain to be predicted. Treating them as heterogeneous signals obscures structural dependencies within the user's decision context, limiting both scaling efficiency and prediction quality. This limitation is particularly pronounced in context-rich scenarios such as e-commerce shelves and waterfall feeds. To address this, we propose UniCon, a unified context-centric modeling architecture that treats the request context as the basic modeling unit and organizes history and prediction targets as homogeneous context units. Intra-context attention captures local coupling among items within a context (Locality), while inter-context attention models the dynamic evolution of decision states across contexts (Dynamics). This organization bridges the structural gap between history and target and supports more effective scaling of unified CTR models. Context-unit-level sequence compression further reduces deployment overhead. On Meituan search advertising, UniCon improves offline AUC by 0.0139 over a strong production baseline and achieves statistically significant online lifts of 3.09% in RPM, 2.07% in CTR, and 2.95% in revenue.

Explore related subjects

cs.IR

Keep this discovery

BibTeXRIS

Jiajun Cui, Zhengqi Xu, Fan Zhang, Zhangteng, Gu Tang, Honghong Zhu, Mengxi Wu, Yulin Liang, Xingxing Wang. 2026-09-04. UniCon: A Unified Context-Centric Modeling Paradigm for CTR Prediction. https://arxiv.org/abs/2609.03290

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

cs.IR subject

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related papers

arXiv2026

RGB-to-IR image translation for infrared vehicle detection in unseen UAV domains

Synthetic training data is crucial for developing vision AI when real-world data is scarce, as in thermal infrared (IR) aerial vehicle detection. While abundant UAV RGB imagery motivates RGB-to-IR translation for data augmentation, unobservable thermal traits (e.g., engine heat) make learning transferable mappings challenging. This work investigates whether modern generative translators can overcome this cross-modal gap to improve infrared vehicle detection on unseen UAV target domains. Translators are trained on paired RGB-IR source datasets and applied to RGB training images from held-out target datasets to generate synthetic IR data. Evaluated methods include supervised GANs, ControlNet-based diffusion models, and foundation-model editing via LoRA. The resulting synthetic IR imagery is used to train RF-DETR vehicle detectors, which are evaluated on unseen IR target test splits across five aerial datasets, with Kust4K and VTUAV serving as target domains. Synthetic IR consistently outperforms RGB and grayscale baselines. Stable Diffusion 3.5 with ControlNet yields the best results, improving mAP from 50.8 to 60.1 on Kust4K and from 25.6 to 38.4 on VTUAV compared to models trained only on source-domain IR data. Increasing output diversity via multiple seeds (+1.1 mAP) and prompt variations (+3.3 mAP) provides additional gains on VTUAV. Although a performance gap to real target IR data remains, generative RGB-to-IR translation effectively mitigates IR data scarcity and improves cross-domain aerial vehicle detection.

cs.CV↗
arXiv2026

CuLifter: Lifting GPU Binaries to Typed IR

GPU compilers merge all data types into a single unified register file, erasing the type information that binary-analysis tools rely on. We show that type recovery from this untyped register file is the central challenge of GPU binary lifting. We present CuLifter, a SASS-to-LLVM IR lifting framework that recovers register types via constraint propagation with conflict detection, reconstructs explicit control flow, and aggregates multi-instruction patterns. Across eight benchmark suites spanning open-source applications, vendor libraries, and optimized ML runtimes, CuLifter successfully lifts all 11,977 kernels to valid LLVM IR. Among the testable set, we achieve more than 90% execution correctness, verified via the CPU backend. An ablation study confirms that type recovery is the only step required to produce compilable IR: disabling it causes 86.9% of kernels to execute incorrectly.

cs.AR↗
arXiv2026

CLEAR-IR: Clarity-Enhanced Active Reconstruction of Infrared Imagery

This paper presents a novel approach for enabling robust robotic perception in dark environments using infrared (IR) stream. IR stream is less susceptible to noise than RGB in low-light conditions. However, it is dominated by active emitter patterns that hinder high-level tasks such as object detection, tracking and localisation. To address this, a Deep Multi-scale Aware Overcomplete (DeepMAO) inspired architecture is proposed that reconstructs clean IR images from emitter populated input, improving both image quality and downstream robotic performance. This approach outperforms existing enhancement techniques and enables reliable operation of vision driven robotic systems across illumination conditions from well-lit to extreme low-light scenes. The results outline the ability of this work to be able to mimic RGB styling from the scene and its applicability on robotics tasks that were trained on RGB images, opening the possibility of doing these tasks in extreme low-light without on-board lighting.

cs.RO↗