10 papers
AdaRepair-Mem: Adaptive Experience Orchestration for Repository-Level Program Repair
Z. C. Luo, J. C. Guo, W. J. He +10
Recent memory-augmented repository-level program repair methods reuse historical repair experiences to improve LLM-based issue resolution. However, our analysis reveals three limit…
R^3: Advertisement Compliance Rectification via Group-Relative Experience Extractor and Curriculum Reinforcement
Yuan Chen, Zhenyu Hu, Mengge Xue +5
Rigorous content moderation is crucial for online advertising but leads to millions of daily rejections. This scale renders manual rectification infeasible, particularly for video…
Decoding Multimodal Cues: Unveiling the Implicit Meaning Behind Hateful Videos
Junyu Lu, Deyi Ji, Liqun Liu +9
Hateful videos have become prevalent on online platforms, highlighting an urgent need for effective detection. However, existing studies primarily focus on binary classification an…
ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring
Deyi Ji, Junyu Lu, Xuanyi Liu +7
Online advertising governance faces significant challenges due to the non-stationary nature of regulatory policies, where emerging mandates (e.g., restrictions on education or aest…
Disentangling to Re-couple: Resolving the Similarity-Controllability Paradox in Subject-Driven Text-to-Image Generation
Shuang Li, Chao Deng, Hang Chen +8
Subject-Driven Text-to-Image (T2I) Generation aims to preserve a subject's identity while editing its context based on a text prompt. A core challenge in this task is the "similari…
OneRanker: Unified Generation and Ranking with One Model in Industrial Advertising Recommendation
Dekai Sun, Yiming Liu, Jiafan Zhou +6
The end-to-end generative paradigm is revolutionizing advertising recommendation systems, driving a shift from traditional cascaded architectures towards unified modeling. However,…
Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA
Wenwei Li, Ming Xu, Tianle Xia +7
Industrial advertising question answering (QA) is a high-stakes task in which hallucinated content, particularly fabricated URLs, can lead to financial loss, compliance violations,…
Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training
Tianle Xia, Ming Xu, Lingxiang Hu +7
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by incorporating external knowledge, yet traditional single-round retrieval struggles with complex multi-…
An Efficient Embedding Based Ad Retrieval with GPU-Powered Feature Interaction
Yifan Lei, Jiahua Luo, Tingyu Jiang +7
In large-scale advertising recommendation systems, retrieval serves as a critical component, aiming to efficiently select a subset of candidate ads relevant to user behaviors from…
InpaintDPO: Mitigating Spatial Relationship Hallucinations in Foreground-conditioned Inpainting via Diverse Preference Optimization
Qirui Li, Yizhe Tang, Ran Yi +5
Foreground-conditioned inpainting, which aims at generating a harmonious background for a given foreground subject based on the text prompt, is an important subfield in controllabl…