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🔹 Title: Vision Foundation Models as Effective Visual Tokenizers for Autoregressive Image Generation 🔹 Publication Date: Published on Jul 11 🔹 Abstract: A novel image tokenizer built on pre-trained vision foundation models improves image reconstruction, generation quality, and token efficiency, enhancing autoregressive generation and class-conditional synthesis. AI-generated summary Leveraging the powerful representations of pre-trained vision foundation models -- traditionally used for visual comprehension -- we explore a novel direction: building an image tokenizer directly atop such models, a largely underexplored area. Specifically, we employ a frozen vision foundation model as the encoder of our tokenizer. To enhance its effectiveness, we introduce two key components: (1) a region-adaptive quantization framework that reduces redundancy in the pre-trained features on regular 2D grids, and (2) a semantic reconstruction objective that aligns the tokenizer's outputs with the foundation model's representations to preserve semantic fidelity. Based on these designs, our proposed image tokenizer , VFMTok , achieves substantial improvements in image reconstruction and generation quality, while also enhancing token efficiency. It further boosts autoregressive (AR) generation -- achieving a gFID of 2.07 on ImageNet benchmarks, while accelerating model convergence by three times, and enabling high-fidelity class-conditional synthesis without the need for classifier-free guidance (CFG). The code will be released publicly to benefit the community. 🔹 Links: • arXiv Page: https://arxiv.org/abs/2507.08441 • PDF: https://arxiv.org/pdf/2507.08441 🔹 Datasets citing this paper: No datasets found 🔹 Spaces citing this paper: No spaces found ================================== For more data science resources: ✓ https://t.me/DataScienceT

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This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

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🔹 Title: A Survey of Context Engineering for Large Language Models 🔹 Publication Date: Published on Jul 17 🔹 Abstract: Context Engineering systematically optimizes information payloads for Large Language Models, addressing gaps in generating sophisticated, long-form outputs. AI-generated summary The performance of Large Language Models (LLMs) is fundamentally determined by the contextual information provided during inference. This survey introduces Context Engineering , a formal discipline that transcends simple prompt design to encompass the systematic optimization of information payloads for LLMs. We present a comprehensive taxonomy decomposing Context Engineering into its foundational components and the sophisticated implementations that integrate them into intelligent systems. We first examine the foundational components: context retrieval and generation, context processing and context management . We then explore how these components are architecturally integrated to create sophisticated system implementations: retrieval-augmented generation (RAG), memory systems and tool-integrated reasoning , and multi-agent systems . Through this systematic analysis of over 1300 research papers, our survey not only establishes a technical roadmap for the field but also reveals a critical research gap: a fundamental asymmetry exists between model capabilities. While current models, augmented by advanced context engineering , demonstrate remarkable proficiency in understanding complex contexts, they exhibit pronounced limitations in generating equally sophisticated, long-form outputs. Addressing this gap is a defining priority for future research. Ultimately, this survey provides a unified framework for both researchers and engineers advancing context-aware AI. 🔹 Links: • arXiv Page: https://huggingface.co/collections/Maxwell-Jia/daily-arxiv-668d5e8d30bab29956b66b8d • PDF: https://arxiv.org/pdf/2507.13334 • Github: https://github.com/Meirtz/Awesome-Context-Engineering 🔹 Datasets citing this paper: No datasets found 🔹 Spaces citing this paper: No spaces found ================================== For more data science resources: ✓ https://t.me/DataScienceT

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🔹 Title: Perception-Aware Policy Optimization for Multimodal Reasoning 🔹 Publication Date: Published on Jul 8 🔹 Abstract: Perception-Aware Policy Optimization (PAPO) enhances reinforcement learning with verifiable rewards for multimodal reasoning by integrating implicit perception loss, improving visual perception and reasoning. AI-generated summary Reinforcement Learning with Verifiable Rewards (RLVR) has proven to be a highly effective strategy for endowing Large Language Models (LLMs) with robust multi-step reasoning abilities. However, its design and optimizations remain tailored to purely textual domains, resulting in suboptimal performance when applied to multimodal reasoning tasks . In particular, we observe that a major source of error in current multimodal reasoning lies in the perception of visual inputs. To address this bottleneck, we propose Perception-Aware Policy Optimization (PAPO), a simple yet effective extension of GRPO that encourages the model to learn to perceive while learning to reason, entirely from internal supervision signals. Notably, PAPO does not rely on additional data curation, external reward models, or proprietary models. Specifically, we introduce the Implicit Perception Loss in the form of a KL divergence term to the GRPO objective, which, despite its simplicity, yields significant overall improvements (4.4%) on diverse multimodal benchmarks. The improvements are more pronounced, approaching 8.0%, on tasks with high vision dependency. We also observe a substantial reduction (30.5%) in perception errors, indicating improved perceptual capabilities with PAPO. We conduct comprehensive analysis of PAPO and identify a unique loss hacking issue, which we rigorously analyze and mitigate through a Double Entropy Loss . Overall, our work introduces a deeper integration of perception-aware supervision into RLVR learning objectives and lays the groundwork for a new RL framework that encourages visually grounded reasoning . Project page: https://mikewangwzhl.github.io/PAPO. 🔹 Links: • arXiv Page: https://arxiv.org/abs/2507.06448 • PDF: https://arxiv.org/pdf/2507.06448 • Project Page: https://mikewangwzhl.github.io/PAPO • Github: https://mikewangwzhl.github.io/PAPO/ 🔹 Datasets citing this paper: • https://huggingface.co/datasets/PAPOGalaxy/PAPO_ViRL39K_train • https://huggingface.co/datasets/PAPOGalaxy/PAPO_MMK12_test 🔹 Spaces citing this paper: No spaces found ================================== For more data science resources: ✓ https://t.me/DataScienceT

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