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## 🔥 今日热度 Top 5（新鲜度+关键词+HN提及+代码线索）
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1. **SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis**
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- arXiv: http://arxiv.org/abs/2603.05483v1
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- 类别: cs.LG | HotScore: 23.0 | 作者: Shahriar Noroozizadeh, Xiaobin Shen, Jeremy C. Weiss
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- 类别: cs.LG | HotScore: 21.0 | 作者: Shahriar Noroozizadeh, Xiaobin Shen, Jeremy C. Weiss
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- 速读: SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival An...（cs.LG）
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2. **Accelerating Text-to-Video Generation with Calibrated Sparse Attention**
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- arXiv: http://arxiv.org/abs/2603.05503v1
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# ArXiv Daily Brief - 2026-03-10
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## 🧠 今日 Top 3（中文可读版）
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1. **Penguin-VL: Exploring the Efficiency Limits of VLM with LLM-based Vision Encoders**
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- 中文题目（意译）: Penguin-VL: Exploring the Efficiency Limits of VLM with LLM-based Vision Encoders
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- 这篇在讲什么: We challenge the prevailing practice that SOTA VLMs must rely on vision encoders initialized via massive contrastive pre训练 (e.g., CLIP/Si...
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- 它怎么做: To address this issue, 提出了 Penguin-VL, whose vision encoder is initialized from a text-only LLM.
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- 得出了什么结果: Across various image and video 基准测试s, Penguin-VL achieves performance comparable to leading VLMs (e.g., Qwen3-VL) in mathematical reasoni...
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- 可能的影响: This makes it a strong drop-in alternative for compute-efficient VLMs and enables high performance in resource-constrained settings.
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- arXiv: http://arxiv.org/abs/2603.06569v1
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2. **Beyond Rows to Reasoning: Agentic Retrieval for Multimodal Spreadsheet Understanding and Editing**
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- 中文题目（意译）: Beyond Rows to Reasoning: Agentic Retrieval for Multimodal Spreadsheet Understanding and Editing
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- 这篇在讲什么: However, SOTA approaches exclude critical context through single-pass retrieval, lose data resolution through compression, and exceed LLM...
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- 它怎么做: 引入了 Beyond Rows to Reasoning (BRTR), a multimodal agentic framework for spreadsheet understanding that replaces single-pass retrieval wit...
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- 得出了什么结果: Supported by over 200 hours of expert human evaluation, BRTR achieves SOTA performance across three frontier spreadsheet understanding 基准...
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- 可能的影响: Recent advances in multimodal Retrieval-Augmented Generation (RAG) enable Large Language 模型s (LLMs) to analyze enterprise spreadsheet wor...
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- arXiv: http://arxiv.org/abs/2603.06503v1
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3. **Modeling and Measuring Redundancy in Multisource Multimodal Data for Autonomous Driving**
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- 中文题目（意译）: Modeling and Measuring Redundancy in Multisource Multimodal Data for Autonomous Driving
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- 这篇在讲什么: Next-generation autonomous vehicles (AVs) rely on large volumes of multisource and multimodal ($M^2$) data to support real-time decision-...
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- 它怎么做: Next-generation autonomous vehicles (AVs) rely on large volumes of multisource and multimodal ($M^2$) data to support real-time decision-...
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- 得出了什么结果: Experimental 结果显示 that selectively removing redundant multisource image object labels from cameras with shared fields of view improves de...
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- 可能的影响: Next-generation autonomous vehicles (AVs) rely on large volumes of multisource and multimodal ($M^2$) data to support real-time decision-...
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- arXiv: http://arxiv.org/abs/2603.06544v1
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## 🔥 今日热度 Top 5（新鲜度+关键词+HN提及+代码线索）
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1. **Penguin-VL: Exploring the Efficiency Limits of VLM with LLM-based Vision Encoders**
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- arXiv: http://arxiv.org/abs/2603.06569v1
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- 类别: cs.CV | HotScore: 22.0 | 作者: Boqiang Zhang, Lei Ke, Ruihan Yang
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- 速读: Penguin-VL: Exploring the Efficiency Limits of VLM with LLM-based Vision Encoders（cs.CV）
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2. **Beyond Rows to Reasoning: Agentic Retrieval for Multimodal Spreadsheet Understanding and Editing**
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- arXiv: http://arxiv.org/abs/2603.06503v1
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- 类别: cs.CL | HotScore: 21.0 | 作者: Anmol Gulati, Sahil Sen, Waqar Sarguroh
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- 速读: Beyond Rows to Reasoning: Agentic Retrieval for Multimodal Spreadsheet Understanding an...（cs.CL）
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3. **Modeling and Measuring Redundancy in Multisource Multimodal Data for Autonomous Driving**
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- arXiv: http://arxiv.org/abs/2603.06544v1
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- 类别: cs.CV | HotScore: 18.0 | 作者: Yuhan Zhou, Mehri Sattari, Haihua Chen
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- 速读: Modeling and Measuring Redundancy in Multisource Multimodal Data for Autonomous Driving（cs.CV）
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4. **COLD-Steer: Steering Large Language Models via In-Context One-step Learning Dynamics**
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- arXiv: http://arxiv.org/abs/2603.06495v1
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- 类别: cs.LG | HotScore: 17.0 | 作者: Kartik Sharma, Rakshit S. Trivedi
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- 速读: COLD-Steer: Steering Large Language Models via In-Context One-step Learning Dynamics（cs.LG）
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5. **SCOPE: Scene-Contextualized Incremental Few-Shot 3D Segmentation**
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- arXiv: http://arxiv.org/abs/2603.06572v1
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- 类别: cs.CV | HotScore: 16.0 | 作者: Vishal Thengane, Zhaochong An, Tianjin Huang
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- 速读: SCOPE: Scene-Contextualized Incremental Few-Shot 3D Segmentation（cs.CV）
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## 🆕 最新上新 Top 10
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1. Multimodal Large Language Models as Image Classifiers (cs.CV) - http://arxiv.org/abs/2603.06578v1
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2. Omni-Diffusion: Unified Multimodal Understanding and Generation with Masked Discrete Diffusion (cs.CV) - http://arxiv.org/abs/2603.06577v1
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3. BEVLM: Distilling Semantic Knowledge from LLMs into Bird's-Eye View Representations (cs.CV) - http://arxiv.org/abs/2603.06576v1
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4. Fly360: Omnidirectional Obstacle Avoidance within Drone View (cs.RO) - http://arxiv.org/abs/2603.06573v1
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5. SCOPE: Scene-Contextualized Incremental Few-Shot 3D Segmentation (cs.CV) - http://arxiv.org/abs/2603.06572v1
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6. SUREON: A Benchmark and Vision-Language-Model for Surgical Reasoning (cs.CV) - http://arxiv.org/abs/2603.06570v1
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7. Penguin-VL: Exploring the Efficiency Limits of VLM with LLM-based Vision Encoders (cs.CV) - http://arxiv.org/abs/2603.06569v1
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8. A recipe for scalable attention-based MLIPs: unlocking long-range accuracy with all-to-all node attention (cs.LG) - http://arxiv.org/abs/2603.06567v1
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9. Boosting deep Reinforcement Learning using pretraining with Logical Options (cs.AI) - http://arxiv.org/abs/2603.06565v1
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10. EgoReasoner: Learning Egocentric 4D Reasoning via Task-Adaptive Structured Thinking (cs.CV) - http://arxiv.org/abs/2603.06561v1
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## Val 今日建议
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- 先读 Top 5 里的 1-2 篇，优先看是否有可直接复用的方法/代码。
|
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- 若你愿意，我下一步可对 Top 3 产出“中文三段式精读卡”（问题-方法-可落地点）。
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# ArXiv Daily Brief - 2026-03-11
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## 🧠 今日 Top 3（中文可读版）
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1. **CoCo: Code as CoT for Text-to-Image Preview and Rare Concept Generation**
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- 中文题目（意译）: CoCo: Code as CoT for Text-to-Image Preview and Rare Concept 生成
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- 这篇在讲什么: Recent advancements in Unified Multimodal 模型s (UMMs) have significantly advanced text-to-image (T2I) generation, particularly through the...
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- 它怎么做: In this work, 提出了 CoCo (Code-as-CoT), a code-driven reasoning framework that represents the reasoning process as executable code, enablin...
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- 得出了什么结果: Empirical evaluations on StructT2IBench, OneIG-Bench, and LongText-Bench show that CoCo achieves improvements of +68.83%, +54.8%, and +41...
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- 可能的影响: Recent advancements in Unified Multimodal 模型s (UMMs) have significantly advanced text-to-image (T2I) generation, particularly through the...
|
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- arXiv: http://arxiv.org/abs/2603.08652v1
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2. **Impermanent: A Live Benchmark for Temporal Generalization in Time Series Forecasting**
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- 中文题目（意译）: Impermanent: A Live 基准 for Temporal Generalization in Time Series Forecasting
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- 这篇在讲什么: While these 模型s often claim broad generalization, existing evaluation protocols provide limited evidence.
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- 它怎么做: 引入了 Impermanent, a live 基准测试 that evaluates forecasting 模型s under open-world temporal change by scoring forecasts sequentially over time ...
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- 得出了什么结果: Recent advances in time-series forecasting increasingly rely on pre-trained foundation-style 模型s.
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- 可能的影响: Recent advances in time-series forecasting increasingly rely on pre-trained foundation-style 模型s.
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- arXiv: http://arxiv.org/abs/2603.08707v1
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3. **OfficeQA Pro: An Enterprise Benchmark for End-to-End Grounded Reasoning**
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- 中文题目（意译）: OfficeQA Pro: An Enterprise 基准 for End-to-End Grounded Reasoning
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- 这篇在讲什么: 引入了 OfficeQA Pro, a 基准测试 for evaluating AI agents on grounded, multi-document reasoning over a large and heterogeneous document corpus.
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- 它怎么做: 引入了 OfficeQA Pro, a 基准测试 for evaluating AI agents on grounded, multi-document reasoning over a large and heterogeneous document corpus.
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- 得出了什么结果: Frontier LLMs including Claude Opus 4.6, GPT-5.4, and Gemini 3.1 Pro Preview achieve less than 5% accuracy on OfficeQA Pro when relying o...
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- 可能的影响: 引入了 OfficeQA Pro, a 基准测试 for evaluating AI agents on grounded, multi-document reasoning over a large and heterogeneous document corpus.
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- arXiv: http://arxiv.org/abs/2603.08655v1
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## 🔥 今日热度 Top 5（新鲜度+关键词+HN提及+代码线索）
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1. **CoCo: Code as CoT for Text-to-Image Preview and Rare Concept Generation**
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- arXiv: http://arxiv.org/abs/2603.08652v1
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- 类别: cs.AI | HotScore: 63.47 | 作者: Haodong Li, Chunmei Qing, Huanyu Zhang
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- 速读: CoCo: Code as CoT for Text-to-Image Preview and Rare Concept Generation（cs.AI）
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2. **Impermanent: A Live Benchmark for Temporal Generalization in Time Series Forecasting**
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- arXiv: http://arxiv.org/abs/2603.08707v1
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- 类别: cs.LG | HotScore: 57.86 | 作者: Azul Garza, Renée Rosillo, Rodrigo Mendoza-Smith
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- 速读: Impermanent: A Live Benchmark for Temporal Generalization in Time Series Forecasting（cs.LG）
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3. **OfficeQA Pro: An Enterprise Benchmark for End-to-End Grounded Reasoning**
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- arXiv: http://arxiv.org/abs/2603.08655v1
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- 类别: cs.AI | HotScore: 55.52 | 作者: Krista Opsahl-Ong, Arnav Singhvi, Jasmine Collins
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- 速读: OfficeQA Pro: An Enterprise Benchmark for End-to-End Grounded Reasoning（cs.AI）
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4. **Agentic Critical Training**
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- arXiv: http://arxiv.org/abs/2603.08706v1
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- 类别: cs.AI | HotScore: 51.85 | 作者: Weize Liu, Minghui Liu, Sy-Tuyen Ho
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- 速读: Agentic Critical Training（cs.AI）
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5. **Evaluating Financial Intelligence in Large Language Models: Benchmarking SuperInvesting AI with LLM Engines**
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- arXiv: http://arxiv.org/abs/2603.08704v1
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- 类别: cs.AI | HotScore: 51.85 | 作者: Akshay Gulati, Kanha Singhania, Tushar Banga
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- 速读: Evaluating Financial Intelligence in Large Language Models: Benchmarking SuperInvesting...（cs.AI）
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## 🆕 最新上新 Top 10
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1. Scale Space Diffusion (cs.CV) - http://arxiv.org/abs/2603.08709v1
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2. FVG-PT: Adaptive Foreground View-Guided Prompt Tuning for Vision-Language Models (cs.CV) - http://arxiv.org/abs/2603.08708v1
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3. Impermanent: A Live Benchmark for Temporal Generalization in Time Series Forecasting (cs.LG) - http://arxiv.org/abs/2603.08707v1
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4. Agentic Critical Training (cs.AI) - http://arxiv.org/abs/2603.08706v1
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5. Evaluating Financial Intelligence in Large Language Models: Benchmarking SuperInvesting AI with LLM Engines (cs.AI) - http://arxiv.org/abs/2603.08704v1
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6. HiAR: Efficient Autoregressive Long Video Generation via Hierarchical Denoising (cs.CV) - http://arxiv.org/abs/2603.08703v1
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7. A Multi-Objective Optimization Approach for Sustainable AI-Driven Entrepreneurship in Resilient Economies (cs.AI) - http://arxiv.org/abs/2603.08692v1
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8. Split Federated Learning Architectures for High-Accuracy and Low-Delay Model Training (cs.LG) - http://arxiv.org/abs/2603.08687v1
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9. Benchmarking Language Modeling for Lossless Compression of Full-Fidelity Audio (cs.SD) - http://arxiv.org/abs/2603.08683v1
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10. Structural Causal Bottleneck Models (stat.ML) - http://arxiv.org/abs/2603.08682v1
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## Val 今日建议
|
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- 先读 Top 5 里的 1-2 篇，优先看是否有可直接复用的方法/代码。
|
||||
- 若你愿意，我下一步可对 Top 3 产出“中文三段式精读卡”（问题-方法-可落地点）。
|
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# ArXiv Daily Brief - 2026-03-12
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## 🧠 今日 Top 3（中文可读版）
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1. **MedMASLab: A Unified Orchestration Framework for Benchmarking Multimodal Medical Multi-Agent Systems**
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- 中文题目（意译）: MedMASLab: A Unified Orchestration Framework for 基准ing Multimodal 医疗 Multi-Agent Systems
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- 这篇在讲什么: Current medical MAS research suffers from non-uniform data ingestion pipelines, inconsistent visual-reasoning evaluation, and a lack of c...
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- 它怎么做: To address these challenges, 提出了 MedMASLab, a unified framework and 基准测试ing platform for multimodal medical multi-agent systems.
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- 得出了什么结果: Our systematic evaluation reveals a critical domain-specific performance gap: while MAS improves reasoning depth, current architectures e...
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- 可能的影响: MedMASLab introduces: (1) A standardized multimodal agent communication protocol that enables seamless integration of 11 heterogeneous MA...
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- arXiv: http://arxiv.org/abs/2603.09909v1
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2. **PathMem: Toward Cognition-Aligned Memory Transformation for Pathology MLLMs**
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- 中文题目（意译）: PathMem: Toward Cognition-Aligned Memory Transformation for Pathology MLLMs
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- 这篇在讲什么: Computational pathology demands both visual pattern recognition and dynamic integration of structured domain knowledge, including taxonom...
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- 它怎么做: Inspired by the hierarchical memory process of human pathologists, 提出了 PathMem, a memory-centric multimodal framework for pathology MLLMs.
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- 得出了什么结果: PathMem achieves SOTA performance across 基准测试s, improving WSI-Bench report generation (12.8% WSI-Precision, 10.1% WSI-Relevance) and open...
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- 可能的影响: Computational pathology demands both visual pattern recognition and dynamic integration of structured domain knowledge, including taxonom...
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- arXiv: http://arxiv.org/abs/2603.09943v1
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3. **MSSR: Memory-Aware Adaptive Replay for Continual LLM Fine-Tuning**
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- 中文题目（意译）: MSSR: Memory-Aware Adaptive Replay for Continual LLM Fine-Tuning
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- 这篇在讲什么: Existing replay-based strategies, such as fixed interleaved replay, accuracy-supervised, and loss-driven scheduling, remain limited: some...
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- 它怎么做: Motivated by retention dynamics under sequential fine-tuning, 提出了 Memory-Inspired Sampler and Scheduler Replay (MSSR), an experience repl...
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- 得出了什么结果: Existing replay-based strategies, such as fixed interleaved replay, accuracy-supervised, and loss-driven scheduling, remain limited: some...
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- 可能的影响: While strong adaptability enables rapid acquisition of new knowledge, it also exposes LLMs to catastrophic forgetting, where previously l...
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- arXiv: http://arxiv.org/abs/2603.09892v1
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## 🔥 今日热度 Top 5（新鲜度+关键词+HN提及+代码线索）
|
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1. **MedMASLab: A Unified Orchestration Framework for Benchmarking Multimodal Medical Multi-Agent Systems**
|
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- arXiv: http://arxiv.org/abs/2603.09909v1
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- 类别: cs.AI | HotScore: 64.96 | 作者: Yunhang Qian, Xiaobin Hu, Jiaquan Yu
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- 速读: MedMASLab: A Unified Orchestration Framework for Benchmarking Multimodal Medical Multi-...（cs.AI）
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2. **PathMem: Toward Cognition-Aligned Memory Transformation for Pathology MLLMs**
|
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- arXiv: http://arxiv.org/abs/2603.09943v1
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- 类别: cs.AI | HotScore: 57.41 | 作者: Jinyue Li, Yuci Liang, Qiankun Li
|
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- 速读: PathMem: Toward Cognition-Aligned Memory Transformation for Pathology MLLMs（cs.AI）
|
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3. **MSSR: Memory-Aware Adaptive Replay for Continual LLM Fine-Tuning**
|
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- arXiv: http://arxiv.org/abs/2603.09892v1
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- 类别: cs.LG | HotScore: 56.77 | 作者: Yiyang Lu, Yu He, Jianlong Chen
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- 速读: MSSR: Memory-Aware Adaptive Replay for Continual LLM Fine-Tuning（cs.LG）
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4. **From Data Statistics to Feature Geometry: How Correlations Shape Superposition**
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- arXiv: http://arxiv.org/abs/2603.09972v1
|
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- 类别: cs.LG | HotScore: 55.74 | 作者: Lucas Prieto, Edward Stevinson, Melih Barsbey
|
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- 速读: From Data Statistics to Feature Geometry: How Correlations Shape Superposition（cs.LG）
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5. **Think Before You Lie: How Reasoning Improves Honesty**
|
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- arXiv: http://arxiv.org/abs/2603.09957v1
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- 类别: cs.AI | HotScore: 55.65 | 作者: Ann Yuan, Asma Ghandeharioun, Carter Blum
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- 速读: Think Before You Lie: How Reasoning Improves Honesty（cs.AI）
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## 🆕 最新上新 Top 10
|
||||
1. Task Aware Modulation Using Representation Learning for Upsaling of Terrestrial Carbon Fluxes (cs.LG) - http://arxiv.org/abs/2603.09974v1
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2. From Data Statistics to Feature Geometry: How Correlations Shape Superposition (cs.LG) - http://arxiv.org/abs/2603.09972v1
|
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3. TiPToP: A Modular Open-Vocabulary Planning System for Robotic Manipulation (cs.RO) - http://arxiv.org/abs/2603.09971v1
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4. CREATE: Testing LLMs for Associative Creativity (cs.CL) - http://arxiv.org/abs/2603.09970v1
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5. ReCoSplat: Autoregressive Feed-Forward Gaussian Splatting Using Render-and-Compare (cs.CV) - http://arxiv.org/abs/2603.09968v1
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6. Understanding the Use of a Large Language Model-Powered Guide to Make Virtual Reality Accessible for Blind and Low Vision People (cs.HC) - http://arxiv.org/abs/2603.09964v1
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7. Emotional Modulation in Swarm Decision Dynamics (cs.MA) - http://arxiv.org/abs/2603.09963v1
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8. BEACON: Language-Conditioned Navigation Affordance Prediction under Occlusion (cs.RO) - http://arxiv.org/abs/2603.09961v1
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9. Think Before You Lie: How Reasoning Improves Honesty (cs.AI) - http://arxiv.org/abs/2603.09957v1
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10. Kinodynamic Motion Retargeting for Humanoid Locomotion via Multi-Contact Whole-Body Trajectory Optimization (cs.RO) - http://arxiv.org/abs/2603.09956v1
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|
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## Val 今日建议
|
||||
- 先读 Top 5 里的 1-2 篇，优先看是否有可直接复用的方法/代码。
|
||||
- 若你愿意，我下一步可对 Top 3 产出“中文三段式精读卡”（问题-方法-可落地点）。
|
||||
@@ -0,0 +1,62 @@
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# ArXiv Daily Brief - 2026-03-13
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## 🧠 今日 Top 3（中文可读版）
|
||||
1. **Too Vivid to Be Real? Benchmarking and Calibrating Generative Color Fidelity**
|
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- 中文题目（意译）: Too Vivid to Be Real? 基准ing and Calibrating Generative Color Fidelity
|
||||
- 这篇在讲什么: Recent advances in text-to-image (T2I) generation have greatly improved visual quality, yet producing images that appear visually authent...
|
||||
- 它怎么做: To address this issue, 提出了 Color Fidelity 数据集 (CFD) and Color Fidelity Metric (CFM) for objective evaluation of color fidelity in realist...
|
||||
- 得出了什么结果: Recent advances in text-to-image (T2I) generation have greatly improved visual quality, yet producing images that appear visually authent...
|
||||
- 可能的影响: Recent advances in text-to-image (T2I) generation have greatly improved visual quality, yet producing images that appear visually authent...
|
||||
- arXiv: http://arxiv.org/abs/2603.10990v1
|
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2. **Ranking Reasoning LLMs under Test-Time Scaling**
|
||||
- 中文题目（意译）: Ranking Reasoning LLMs under Test-Time Scaling
|
||||
- 这篇在讲什么: Test-time scaling evaluates reasoning LLMs by sampling multiple outputs per prompt, but ranking 模型s in this regime remains underexplored.
|
||||
- 它怎么做: Test-time scaling evaluates reasoning LLMs by sampling multiple outputs per prompt, but ranking 模型s in this regime remains underexplored.
|
||||
- 得出了什么结果: Test-time scaling evaluates reasoning LLMs by sampling multiple outputs per prompt, but ranking 模型s in this regime remains underexplored.
|
||||
- 可能的影响: Test-time scaling evaluates reasoning LLMs by sampling multiple outputs per prompt, but ranking 模型s in this regime remains underexplored.
|
||||
- arXiv: http://arxiv.org/abs/2603.10960v1
|
||||
3. **Pointy - A Lightweight Transformer for Point Cloud Foundation Models**
|
||||
- 中文题目（意译）: Pointy - A Lightweight Transformer for Point Cloud Foundation 模型
|
||||
- 这篇在讲什么: Foundation 模型s for point cloud data have recently grown in capability, often leveraging extensive representation learning from language o...
|
||||
- 它怎么做: Interestingly, 该方法 approaches SOTA results from 模型s that have seen over a million point clouds, images, and text samples, demonstrating t...
|
||||
- 得出了什么结果: In contrast to the heavy reliance on cross-modal supervision, our 模型 is trained only on 39k point clouds - yet it outperforms several lar...
|
||||
- 可能的影响: Foundation 模型s for point cloud data have recently grown in capability, often leveraging extensive representation learning from language o...
|
||||
- arXiv: http://arxiv.org/abs/2603.10963v1
|
||||
|
||||
## 🔥 今日热度 Top 5（新鲜度+关键词+HN提及+代码线索）
|
||||
1. **Too Vivid to Be Real? Benchmarking and Calibrating Generative Color Fidelity**
|
||||
- arXiv: http://arxiv.org/abs/2603.10990v1
|
||||
- 类别: cs.CV | HotScore: 58.02 | 作者: Zhengyao Fang, Zexi Jia, Yijia Zhong
|
||||
- 速读: Too Vivid to Be Real? Benchmarking and Calibrating Generative Color Fidelity（cs.CV）
|
||||
2. **Ranking Reasoning LLMs under Test-Time Scaling**
|
||||
- arXiv: http://arxiv.org/abs/2603.10960v1
|
||||
- 类别: cs.LG | HotScore: 54.59 | 作者: Mohsen Hariri, Michael Hinczewski, Jing Ma
|
||||
- 速读: Ranking Reasoning LLMs under Test-Time Scaling（cs.LG）
|
||||
3. **Pointy - A Lightweight Transformer for Point Cloud Foundation Models**
|
||||
- arXiv: http://arxiv.org/abs/2603.10963v1
|
||||
- 类别: cs.CV | HotScore: 51.64 | 作者: Konrad Szafer, Marek Kraft, Dominik Belter
|
||||
- 速读: Pointy - A Lightweight Transformer for Point Cloud Foundation Models（cs.CV）
|
||||
4. **Lifelong Imitation Learning with Multimodal Latent Replay and Incremental Adjustment**
|
||||
- arXiv: http://arxiv.org/abs/2603.10929v1
|
||||
- 类别: cs.CV | HotScore: 51.11 | 作者: Fanqi Yu, Matteo Tiezzi, Tommaso Apicella
|
||||
- 速读: Lifelong Imitation Learning with Multimodal Latent Replay and Incremental Adjustment（cs.CV）
|
||||
5. **Bio-Inspired Self-Supervised Learning for Wrist-worn IMU Signals**
|
||||
- arXiv: http://arxiv.org/abs/2603.10961v1
|
||||
- 类别: cs.LG | HotScore: 50.61 | 作者: Prithviraj Tarale, Kiet Chu, Abhishek Varghese
|
||||
- 速读: Bio-Inspired Self-Supervised Learning for Wrist-worn IMU Signals（cs.LG）
|
||||
|
||||
## 🆕 最新上新 Top 10
|
||||
1. COMIC: Agentic Sketch Comedy Generation (cs.CV) - http://arxiv.org/abs/2603.11048v1
|
||||
2. LiTo: Surface Light Field Tokenization (cs.CV) - http://arxiv.org/abs/2603.11047v1
|
||||
3. Neural Field Thermal Tomography: A Differentiable Physics Framework for Non-Destructive Evaluation (cs.LG) - http://arxiv.org/abs/2603.11045v1
|
||||
4. Agentar-Fin-OCR (cs.CV) - http://arxiv.org/abs/2603.11044v1
|
||||
5. V2M-Zero: Zero-Pair Time-Aligned Video-to-Music Generation (cs.CV) - http://arxiv.org/abs/2603.11042v1
|
||||
6. DynVLA: Learning World Dynamics for Action Reasoning in Autonomous Driving (cs.CV) - http://arxiv.org/abs/2603.11041v1
|
||||
7. Instruction set for the representation of graphs (cs.CL) - http://arxiv.org/abs/2603.11039v1
|
||||
8. Beyond the Illusion of Consensus: From Surface Heuristics to Knowledge-Grounded Evaluation in LLM-as-a-Judge (cs.CL) - http://arxiv.org/abs/2603.11027v1
|
||||
9. Does AI See like Art Historians? Interpreting How Vision Language Models Recognize Artistic Style (cs.CV) - http://arxiv.org/abs/2603.11024v1
|
||||
10. Leech Lattice Vector Quantization for Efficient LLM Compression (cs.LG) - http://arxiv.org/abs/2603.11021v1
|
||||
|
||||
## Val 今日建议
|
||||
- 先读 Top 5 里的 1-2 篇，优先看是否有可直接复用的方法/代码。
|
||||
- 若你愿意，我下一步可对 Top 3 产出“中文三段式精读卡”（问题-方法-可落地点）。
|
||||
@@ -0,0 +1,62 @@
|
||||
# ArXiv Daily Brief - 2026-03-14
|
||||
|
||||
## 🧠 今日 Top 3（中文可读版）
|
||||
1. **MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Compositional Reasoning**
|
||||
- 中文题目（意译）: MM-CondChain: A Programmatically Verified 基准 for Visually Grounded Deep Compositional Reasoning
|
||||
- 这篇在讲什么: Experiments on a range of MLLMs show that even the strongest 模型 attains only 53.33 Path F1, with sharp drops on hard negatives and as dep...
|
||||
- 它怎么做: In 本文, 引入了 MM-CondChain, a 基准测试 for visually grounded deep compositional reasoning.
|
||||
- 得出了什么结果: Multimodal Large Language 模型s (MLLMs) are increasingly used to carry out visual workflows such as navigating GUIs, where the next step de...
|
||||
- 可能的影响: Multimodal Large Language 模型s (MLLMs) are increasingly used to carry out visual workflows such as navigating GUIs, where the next step de...
|
||||
- arXiv: http://arxiv.org/abs/2603.12266v1
|
||||
2. **Video Streaming Thinking: VideoLLMs Can Watch and Think Simultaneously**
|
||||
- 中文题目（意译）: Video Streaming Thinking: VideoLLMs Can Watch and Think Simultaneously
|
||||
- 这篇在讲什么: Online Video Large Language 模型s (VideoLLMs) play a critical role in supporting responsive, real-time interaction.
|
||||
- 它怎么做: To address this trade-off, 提出了 Video Streaming Thinking (VST), a novel paradigm for streaming video understanding.
|
||||
- 得出了什么结果: This design improves timely comprehension and coherent cognition while preserving real-time responsiveness by amortizing LLM reasoning la...
|
||||
- 可能的影响: Online Video Large Language 模型s (VideoLLMs) play a critical role in supporting responsive, real-time interaction.
|
||||
- arXiv: http://arxiv.org/abs/2603.12262v1
|
||||
3. **SceneAssistant: A Visual Feedback Agent for Open-Vocabulary 3D Scene Generation**
|
||||
- 中文题目（意译）: SceneAssistant: A Visual Feedback Agent for Open-Vocabulary 3D Scene 生成
|
||||
- 这篇在讲什么: Text-to-3D scene generation from natural language is highly desirable for digital content creation.
|
||||
- 它怎么做: In 本文, 引入了 SceneAssistant, a visual-feedback-driven agent designed for open-vocabulary 3D scene generation.
|
||||
- 得出了什么结果: At each interaction step, the VLM receives rendered visual feedback and takes actions accordingly, iteratively refining the scene to achi...
|
||||
- 可能的影响: Text-to-3D scene generation from natural language is highly desirable for digital content creation.
|
||||
- arXiv: http://arxiv.org/abs/2603.12238v1
|
||||
|
||||
## 🔥 今日热度 Top 5（新鲜度+关键词+HN提及+代码线索）
|
||||
1. **MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Compositional Reasoning**
|
||||
- arXiv: http://arxiv.org/abs/2603.12266v1
|
||||
- 类别: cs.CV | HotScore: 56.54 | 作者: Haozhan Shen, Shilin Yan, Hongwei Xue
|
||||
- 速读: MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Composit...（cs.CV）
|
||||
2. **Video Streaming Thinking: VideoLLMs Can Watch and Think Simultaneously**
|
||||
- arXiv: http://arxiv.org/abs/2603.12262v1
|
||||
- 类别: cs.CV | HotScore: 56.53 | 作者: Yiran Guan, Liang Yin, Dingkang Liang
|
||||
- 速读: Video Streaming Thinking: VideoLLMs Can Watch and Think Simultaneously（cs.CV）
|
||||
3. **SceneAssistant: A Visual Feedback Agent for Open-Vocabulary 3D Scene Generation**
|
||||
- arXiv: http://arxiv.org/abs/2603.12238v1
|
||||
- 类别: cs.CV | HotScore: 56.46 | 作者: Jun Luo, Jiaxiang Tang, Ruijie Lu
|
||||
- 速读: SceneAssistant: A Visual Feedback Agent for Open-Vocabulary 3D Scene Generation（cs.CV）
|
||||
4. **SciMDR: Benchmarking and Advancing Scientific Multimodal Document Reasoning**
|
||||
- arXiv: http://arxiv.org/abs/2603.12249v1
|
||||
- 类别: cs.CL | HotScore: 55.5 | 作者: Ziyu Chen, Yilun Zhao, Chengye Wang
|
||||
- 速读: SciMDR: Benchmarking and Advancing Scientific Multimodal Document Reasoning（cs.CL）
|
||||
5. **Examining Reasoning LLMs-as-Judges in Non-Verifiable LLM Post-Training**
|
||||
- arXiv: http://arxiv.org/abs/2603.12246v1
|
||||
- 类别: cs.AI | HotScore: 53.49 | 作者: Yixin Liu, Yue Yu, DiJia Su
|
||||
- 速读: Examining Reasoning LLMs-as-Judges in Non-Verifiable LLM Post-Training（cs.AI）
|
||||
|
||||
## 🆕 最新上新 Top 10
|
||||
1. EVATok: Adaptive Length Video Tokenization for Efficient Visual Autoregressive Generation (cs.CV) - http://arxiv.org/abs/2603.12267v1
|
||||
2. MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Compositional Reasoning (cs.CV) - http://arxiv.org/abs/2603.12266v1
|
||||
3. OmniStream: Mastering Perception, Reconstruction and Action in Continuous Streams (cs.CV) - http://arxiv.org/abs/2603.12265v1
|
||||
4. GRADE: Benchmarking Discipline-Informed Reasoning in Image Editing (cs.CV) - http://arxiv.org/abs/2603.12264v1
|
||||
5. $Ψ_0$: An Open Foundation Model Towards Universal Humanoid Loco-Manipulation (cs.RO) - http://arxiv.org/abs/2603.12263v1
|
||||
6. Video Streaming Thinking: VideoLLMs Can Watch and Think Simultaneously (cs.CV) - http://arxiv.org/abs/2603.12262v1
|
||||
7. The Latent Color Subspace: Emergent Order in High-Dimensional Chaos (cs.LG) - http://arxiv.org/abs/2603.12261v1
|
||||
8. HumDex:Humanoid Dexterous Manipulation Made Easy (cs.RO) - http://arxiv.org/abs/2603.12260v1
|
||||
9. DreamVideo-Omni: Omni-Motion Controlled Multi-Subject Video Customization with Latent Identity Reinforcement Learning (cs.CV) - http://arxiv.org/abs/2603.12257v1
|
||||
10. Spatial-TTT: Streaming Visual-based Spatial Intelligence with Test-Time Training (cs.CV) - http://arxiv.org/abs/2603.12255v1
|
||||
|
||||
## Val 今日建议
|
||||
- 先读 Top 5 里的 1-2 篇，优先看是否有可直接复用的方法/代码。
|
||||
- 若你愿意，我下一步可对 Top 3 产出“中文三段式精读卡”（问题-方法-可落地点）。
|
||||
@@ -1,61 +1,61 @@
|
||||
# ArXiv Daily Brief - 2026-03-09
|
||||
# ArXiv Daily Brief - 2026-03-14
|
||||
|
||||
## 🧠 今日 Top 3（中文可读版）
|
||||
1. **SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis**
|
||||
- 中文题目（意译）: SurvHTE-Bench: A 基准 for Heterogeneous Treatment Effect Estimation in Survival Analysis
|
||||
- 这篇在讲什么: Estimating heterogeneous treatment effects (HTEs) from right-censored survival data is critical in high-stakes applications such as preci...
|
||||
- 它怎么做: 引入了 SurvHTE-Bench, the first comprehensive 基准测试 for HTE estimation with censored outcomes.
|
||||
- 得出了什么结果: Estimating heterogeneous treatment effects (HTEs) from right-censored survival data is critical in high-stakes applications such as preci...
|
||||
- 可能的影响: Estimating heterogeneous treatment effects (HTEs) from right-censored survival data is critical in high-stakes applications such as preci...
|
||||
- arXiv: http://arxiv.org/abs/2603.05483v1
|
||||
2. **Accelerating Text-to-Video Generation with Calibrated Sparse Attention**
|
||||
- 中文题目（意译）: 加速 Text-to-Video 生成 with Calibrated Sparse Attention
|
||||
- 这篇在讲什么: Recent 扩散 模型s enable high-quality 视频生成, but suffer from slow runtimes.
|
||||
- 它怎么做: Motivated by this, 引入了 CalibAtt, a 训练-free method that accelerates 视频生成 via calibrated sparse attention.
|
||||
- 得出了什么结果: Extensive experiments on Wan 2.1 14B, Mochi 1, and few-step distilled 模型s at various resolutions show that CalibAtt achieves up to 1.58x ...
|
||||
- 可能的影响: Recent 扩散 模型s enable high-quality 视频生成, but suffer from slow runtimes.
|
||||
- arXiv: http://arxiv.org/abs/2603.05503v1
|
||||
3. **Observing and Controlling Features in Vision-Language-Action Models**
|
||||
- 中文题目（意译）: Observing and 控制 特征 in 视觉-语言-动作 模型
|
||||
- 这篇在讲什么: 视觉-语言-动作 模型s (VLAs) have shown remarkable progress towards embodied intelligence.
|
||||
- 它怎么做: In this work, 提出了 to close this gap by introducing and analyzing two main concepts: feature-observability and feature-controllability.
|
||||
- 得出了什么结果: Our 结果显示 that targeted, lightweight interventions can reliably steer a robot's behavior while preserving closed-loop capabilities.
|
||||
- 可能的影响: 视觉-语言-动作 模型s (VLAs) have shown remarkable progress towards embodied intelligence.
|
||||
- arXiv: http://arxiv.org/abs/2603.05487v1
|
||||
1. **MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Compositional Reasoning**
|
||||
- 中文题目（意译）: MM-CondChain: A Programmatically Verified 基准 for Visually Grounded Deep Compositional Reasoning
|
||||
- 这篇在讲什么: Experiments on a range of MLLMs show that even the strongest 模型 attains only 53.33 Path F1, with sharp drops on hard negatives and as dep...
|
||||
- 它怎么做: In 本文, 引入了 MM-CondChain, a 基准测试 for visually grounded deep compositional reasoning.
|
||||
- 得出了什么结果: Multimodal Large Language 模型s (MLLMs) are increasingly used to carry out visual workflows such as navigating GUIs, where the next step de...
|
||||
- 可能的影响: Multimodal Large Language 模型s (MLLMs) are increasingly used to carry out visual workflows such as navigating GUIs, where the next step de...
|
||||
- arXiv: http://arxiv.org/abs/2603.12266v1
|
||||
2. **Video Streaming Thinking: VideoLLMs Can Watch and Think Simultaneously**
|
||||
- 中文题目（意译）: Video Streaming Thinking: VideoLLMs Can Watch and Think Simultaneously
|
||||
- 这篇在讲什么: Online Video Large Language 模型s (VideoLLMs) play a critical role in supporting responsive, real-time interaction.
|
||||
- 它怎么做: To address this trade-off, 提出了 Video Streaming Thinking (VST), a novel paradigm for streaming video understanding.
|
||||
- 得出了什么结果: This design improves timely comprehension and coherent cognition while preserving real-time responsiveness by amortizing LLM reasoning la...
|
||||
- 可能的影响: Online Video Large Language 模型s (VideoLLMs) play a critical role in supporting responsive, real-time interaction.
|
||||
- arXiv: http://arxiv.org/abs/2603.12262v1
|
||||
3. **SceneAssistant: A Visual Feedback Agent for Open-Vocabulary 3D Scene Generation**
|
||||
- 中文题目（意译）: SceneAssistant: A Visual Feedback Agent for Open-Vocabulary 3D Scene 生成
|
||||
- 这篇在讲什么: Text-to-3D scene generation from natural language is highly desirable for digital content creation.
|
||||
- 它怎么做: In 本文, 引入了 SceneAssistant, a visual-feedback-driven agent designed for open-vocabulary 3D scene generation.
|
||||
- 得出了什么结果: At each interaction step, the VLM receives rendered visual feedback and takes actions accordingly, iteratively refining the scene to achi...
|
||||
- 可能的影响: Text-to-3D scene generation from natural language is highly desirable for digital content creation.
|
||||
- arXiv: http://arxiv.org/abs/2603.12238v1
|
||||
|
||||
## 🔥 今日热度 Top 5（新鲜度+关键词+HN提及+代码线索）
|
||||
1. **SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis**
|
||||
- arXiv: http://arxiv.org/abs/2603.05483v1
|
||||
- 类别: cs.LG | HotScore: 23.0 | 作者: Shahriar Noroozizadeh, Xiaobin Shen, Jeremy C. Weiss
|
||||
- 速读: SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival An...（cs.LG）
|
||||
2. **Accelerating Text-to-Video Generation with Calibrated Sparse Attention**
|
||||
- arXiv: http://arxiv.org/abs/2603.05503v1
|
||||
- 类别: cs.CV | HotScore: 18.0 | 作者: Shai Yehezkel, Shahar Yadin, Noam Elata
|
||||
- 速读: Accelerating Text-to-Video Generation with Calibrated Sparse Attention（cs.CV）
|
||||
3. **Observing and Controlling Features in Vision-Language-Action Models**
|
||||
- arXiv: http://arxiv.org/abs/2603.05487v1
|
||||
- 类别: cs.RO | HotScore: 18.0 | 作者: Hugo Buurmeijer, Carmen Amo Alonso, Aiden Swann
|
||||
- 速读: Observing and Controlling Features in Vision-Language-Action Models（cs.RO）
|
||||
4. **Towards Provably Unbiased LLM Judges via Bias-Bounded Evaluation**
|
||||
- arXiv: http://arxiv.org/abs/2603.05485v1
|
||||
- 类别: cs.AI | HotScore: 17.0 | 作者: Benjamin Feuer, Lucas Rosenblatt, Oussama Elachqar
|
||||
- 速读: Towards Provably Unbiased LLM Judges via Bias-Bounded Evaluation（cs.AI）
|
||||
5. **An interpretable prototype parts-based neural network for medical tabular data**
|
||||
- arXiv: http://arxiv.org/abs/2603.05423v1
|
||||
- 类别: cs.LG | HotScore: 17.0 | 作者: Jacek Karolczak, Jerzy Stefanowski
|
||||
- 速读: An interpretable prototype parts-based neural network for medical tabular data（cs.LG）
|
||||
1. **MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Compositional Reasoning**
|
||||
- arXiv: http://arxiv.org/abs/2603.12266v1
|
||||
- 类别: cs.CV | HotScore: 56.54 | 作者: Haozhan Shen, Shilin Yan, Hongwei Xue
|
||||
- 速读: MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Composit...（cs.CV）
|
||||
2. **Video Streaming Thinking: VideoLLMs Can Watch and Think Simultaneously**
|
||||
- arXiv: http://arxiv.org/abs/2603.12262v1
|
||||
- 类别: cs.CV | HotScore: 56.53 | 作者: Yiran Guan, Liang Yin, Dingkang Liang
|
||||
- 速读: Video Streaming Thinking: VideoLLMs Can Watch and Think Simultaneously（cs.CV）
|
||||
3. **SceneAssistant: A Visual Feedback Agent for Open-Vocabulary 3D Scene Generation**
|
||||
- arXiv: http://arxiv.org/abs/2603.12238v1
|
||||
- 类别: cs.CV | HotScore: 56.46 | 作者: Jun Luo, Jiaxiang Tang, Ruijie Lu
|
||||
- 速读: SceneAssistant: A Visual Feedback Agent for Open-Vocabulary 3D Scene Generation（cs.CV）
|
||||
4. **SciMDR: Benchmarking and Advancing Scientific Multimodal Document Reasoning**
|
||||
- arXiv: http://arxiv.org/abs/2603.12249v1
|
||||
- 类别: cs.CL | HotScore: 55.5 | 作者: Ziyu Chen, Yilun Zhao, Chengye Wang
|
||||
- 速读: SciMDR: Benchmarking and Advancing Scientific Multimodal Document Reasoning（cs.CL）
|
||||
5. **Examining Reasoning LLMs-as-Judges in Non-Verifiable LLM Post-Training**
|
||||
- arXiv: http://arxiv.org/abs/2603.12246v1
|
||||
- 类别: cs.AI | HotScore: 53.49 | 作者: Yixin Liu, Yue Yu, DiJia Su
|
||||
- 速读: Examining Reasoning LLMs-as-Judges in Non-Verifiable LLM Post-Training（cs.AI）
|
||||
|
||||
## 🆕 最新上新 Top 10
|
||||
1. Transformer-Based Inpainting for Real-Time 3D Streaming in Sparse Multi-Camera Setups (cs.CV) - http://arxiv.org/abs/2603.05507v1
|
||||
2. FaceCam: Portrait Video Camera Control via Scale-Aware Conditioning (cs.CV) - http://arxiv.org/abs/2603.05506v1
|
||||
3. RoboPocket: Improve Robot Policies Instantly with Your Phone (cs.RO) - http://arxiv.org/abs/2603.05504v1
|
||||
4. Accelerating Text-to-Video Generation with Calibrated Sparse Attention (cs.CV) - http://arxiv.org/abs/2603.05503v1
|
||||
5. POET-X: Memory-efficient LLM Training by Scaling Orthogonal Transformation (cs.LG) - http://arxiv.org/abs/2603.05500v1
|
||||
6. The Spike, the Sparse and the Sink: Anatomy of Massive Activations and Attention Sinks (cs.AI) - http://arxiv.org/abs/2603.05498v1
|
||||
7. Safe-SAGE: Social-Semantic Adaptive Guidance for Safe Engagement through Laplace-Modulated Poisson Safety Functions (cs.RO) - http://arxiv.org/abs/2603.05497v1
|
||||
8. Cheap Thrills: Effective Amortized Optimization Using Inexpensive Labels (cs.LG) - http://arxiv.org/abs/2603.05495v1
|
||||
9. Censored LLMs as a Natural Testbed for Secret Knowledge Elicitation (cs.LG) - http://arxiv.org/abs/2603.05494v1
|
||||
10. cuRoboV2: Dynamics-Aware Motion Generation with Depth-Fused Distance Fields for High-DoF Robots (cs.RO) - http://arxiv.org/abs/2603.05493v1
|
||||
1. EVATok: Adaptive Length Video Tokenization for Efficient Visual Autoregressive Generation (cs.CV) - http://arxiv.org/abs/2603.12267v1
|
||||
2. MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Compositional Reasoning (cs.CV) - http://arxiv.org/abs/2603.12266v1
|
||||
3. OmniStream: Mastering Perception, Reconstruction and Action in Continuous Streams (cs.CV) - http://arxiv.org/abs/2603.12265v1
|
||||
4. GRADE: Benchmarking Discipline-Informed Reasoning in Image Editing (cs.CV) - http://arxiv.org/abs/2603.12264v1
|
||||
5. $Ψ_0$: An Open Foundation Model Towards Universal Humanoid Loco-Manipulation (cs.RO) - http://arxiv.org/abs/2603.12263v1
|
||||
6. Video Streaming Thinking: VideoLLMs Can Watch and Think Simultaneously (cs.CV) - http://arxiv.org/abs/2603.12262v1
|
||||
7. The Latent Color Subspace: Emergent Order in High-Dimensional Chaos (cs.LG) - http://arxiv.org/abs/2603.12261v1
|
||||
8. HumDex:Humanoid Dexterous Manipulation Made Easy (cs.RO) - http://arxiv.org/abs/2603.12260v1
|
||||
9. DreamVideo-Omni: Omni-Motion Controlled Multi-Subject Video Customization with Latent Identity Reinforcement Learning (cs.CV) - http://arxiv.org/abs/2603.12257v1
|
||||
10. Spatial-TTT: Streaming Visual-based Spatial Intelligence with Test-Time Training (cs.CV) - http://arxiv.org/abs/2603.12255v1
|
||||
|
||||
## Val 今日建议
|
||||
- 先读 Top 5 里的 1-2 篇，优先看是否有可直接复用的方法/代码。
|
||||
|
||||
@@ -1 +1 @@
|
||||
{ "date": "2026-03-09", "pushed": true, "channel": "gmail" }
|
||||
{"date":"2026-03-14","pushed":true,"channel":"gmail","message_id":"19ce9c43f2d5e7c0","sent_at":"2026-03-14T08:35:00+08:00","cancelled":true,"cancelled_at":"2026-03-14T10:46:00+08:00","cancelled_by":"谷老板指令"}
|
||||
@@ -1,32 +1,32 @@
|
||||
{
|
||||
"updatedAt": "2026-03-09T06:53:12.730807",
|
||||
"date": "2026-03-09",
|
||||
"updatedAt": "2026-03-14T08:34:25.638154",
|
||||
"date": "2026-03-14",
|
||||
"papersFetched": 120,
|
||||
"topHot": [
|
||||
{
|
||||
"title": "SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis",
|
||||
"arxiv_id": "2603.05483v1",
|
||||
"score": 23.0
|
||||
"title": "MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Compositional Reasoning",
|
||||
"arxiv_id": "2603.12266v1",
|
||||
"score": 56.54
|
||||
},
|
||||
{
|
||||
"title": "Accelerating Text-to-Video Generation with Calibrated Sparse Attention",
|
||||
"arxiv_id": "2603.05503v1",
|
||||
"score": 18.0
|
||||
"title": "Video Streaming Thinking: VideoLLMs Can Watch and Think Simultaneously",
|
||||
"arxiv_id": "2603.12262v1",
|
||||
"score": 56.53
|
||||
},
|
||||
{
|
||||
"title": "Observing and Controlling Features in Vision-Language-Action Models",
|
||||
"arxiv_id": "2603.05487v1",
|
||||
"score": 18.0
|
||||
"title": "SceneAssistant: A Visual Feedback Agent for Open-Vocabulary 3D Scene Generation",
|
||||
"arxiv_id": "2603.12238v1",
|
||||
"score": 56.46
|
||||
},
|
||||
{
|
||||
"title": "Towards Provably Unbiased LLM Judges via Bias-Bounded Evaluation",
|
||||
"arxiv_id": "2603.05485v1",
|
||||
"score": 17.0
|
||||
"title": "SciMDR: Benchmarking and Advancing Scientific Multimodal Document Reasoning",
|
||||
"arxiv_id": "2603.12249v1",
|
||||
"score": 55.5
|
||||
},
|
||||
{
|
||||
"title": "An interpretable prototype parts-based neural network for medical tabular data",
|
||||
"arxiv_id": "2603.05423v1",
|
||||
"score": 17.0
|
||||
"title": "Examining Reasoning LLMs-as-Judges in Non-Verifiable LLM Post-Training",
|
||||
"arxiv_id": "2603.12246v1",
|
||||
"score": 53.49
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -6,4 +6,5 @@ services:
|
||||
- "1313:1313"
|
||||
volumes:
|
||||
- ./site:/site
|
||||
command: ["server", "-D", "--bind", "0.0.0.0", "--baseURL", "http://localhost:1313"]
|
||||
# 使用 /blog/ 作为 baseURL，确保生成的链接都带 /blog 前缀
|
||||
command: ["server", "-D", "--bind", "0.0.0.0", "--baseURL", "http://localhost:1313/blog/"]
|
||||
|
||||
@@ -0,0 +1,131 @@
|
||||
# 05-routing-fix.md - 子路径路由修复
|
||||
|
||||
## 问题描述
|
||||
|
||||
通过 Tailscale 路径 `/blog` 访问博客时，除首页外其他链接会跳转到 OpenClaw Web 控制页根路径。
|
||||
|
||||
**根本原因：** Hugo 配置中 `baseURL` 设置为 `http://localhost:1313/`，未包含 `/blog` 子路径前缀，导致生成的 HTML 链接指向 `/` 而非 `/blog/`。
|
||||
|
||||
## 修复方案
|
||||
|
||||
### 1. 修改 config.toml
|
||||
|
||||
**文件：** `site/config.toml`
|
||||
|
||||
```diff
|
||||
- baseURL = "http://localhost:1313/"
|
||||
+ baseURL = "/blog/"
|
||||
```
|
||||
|
||||
设置相对路径 `/blog/`，使 Hugo 生成的所有链接都带 `/blog` 前缀。
|
||||
|
||||
### 2. 修改 baseof.html 模板
|
||||
|
||||
**文件：** `site/layouts/_default/baseof.html`
|
||||
|
||||
将硬编码的绝对路径替换为 Hugo 模板函数：
|
||||
|
||||
| 原始代码 | 修复后 |
|
||||
|---------|--------|
|
||||
| `href="/css/main.css"` | `href="{{ "css/main.css" \| absURL }}"` |
|
||||
| `href="/"` | `href="{{ "" \| absURL }}"` |
|
||||
| `href="/journey"` | `href="{{ "journey" \| absURL }}"` |
|
||||
|
||||
**说明：**
|
||||
- `absURL` 函数会基于 `baseURL` 生成完整路径（如 `/blog/css/main.css`）
|
||||
- 对于文章列表页使用 `.RelPermalink`（已自动处理子路径）
|
||||
|
||||
### 3. 修改 docker-compose.yml
|
||||
|
||||
**文件：** `docker-compose.yml`
|
||||
|
||||
```diff
|
||||
- command: ["server", "-D", "--bind", "0.0.0.0", "--baseURL", "http://localhost:1313"]
|
||||
+ command: ["server", "-D", "--bind", "0.0.0.0", "--baseURL", "http://localhost:1313/blog/"]
|
||||
```
|
||||
|
||||
确保容器启动时使用正确的 baseURL。
|
||||
|
||||
## 验证步骤
|
||||
|
||||
### 本地验证
|
||||
|
||||
```bash
|
||||
# 1. 启动服务
|
||||
cd /Users/guchen/.openclaw/workspace/org/cases/val_blog
|
||||
docker compose up -d
|
||||
|
||||
# 2. 验证首页访问
|
||||
curl -s http://127.0.0.1:1313/blog/ | grep -E 'href='
|
||||
|
||||
# 3. 验证文章页访问
|
||||
curl -s http://127.0.0.1:1313/blog/journey/ | grep -E 'href='
|
||||
|
||||
# 4. 验证生成静态文件的链接
|
||||
docker exec val-blog-dev hugo -d /tmp/hugo-public
|
||||
docker exec val-blog-dev grep -r 'href=' /tmp/hugo-public/index.html
|
||||
```
|
||||
|
||||
**预期结果：**
|
||||
- 所有 `href` 属性都包含 `/blog/` 前缀
|
||||
- CSS 链接：`http://localhost:1313/blog/css/main.css`
|
||||
- 导航链接：`http://localhost:1313/blog/`、`http://localhost:1313/blog/journey`
|
||||
- 文章链接：`/blog/journey/xxx/`
|
||||
|
||||
### Tailscale 验证
|
||||
|
||||
通过 Tailscale 访问时，确保以下映射配置：
|
||||
|
||||
```bash
|
||||
# 查看当前 Tailscale serve 配置
|
||||
tailscale status
|
||||
tailscale serve status
|
||||
|
||||
# 如需添加 /blog 路径映射（如果尚未配置）
|
||||
tailscale serve --http=80 tcp:1313
|
||||
# 然后在控制台将 /blog 路径映射到 Hugo 服务
|
||||
```
|
||||
|
||||
## 回滚方案
|
||||
|
||||
如需回滚到根路径配置，执行以下操作：
|
||||
|
||||
1. **config.toml:**
|
||||
```toml
|
||||
baseURL = "http://localhost:1313/"
|
||||
```
|
||||
|
||||
2. **baseof.html:**
|
||||
```html
|
||||
<link rel="stylesheet" href="/css/main.css" />
|
||||
<a href="/">首页</a>
|
||||
<a href="/journey">旅程</a>
|
||||
```
|
||||
|
||||
3. **docker-compose.yml:**
|
||||
```yaml
|
||||
command: ["server", "-D", "--bind", "0.0.0.0", "--baseURL", "http://localhost:1313"]
|
||||
```
|
||||
|
||||
## Tailscale Serve 建议命令
|
||||
|
||||
如果需要调整 Tailscale 路径映射：
|
||||
|
||||
```bash
|
||||
# 方式一：仅映射 /blog 路径
|
||||
tailscale serve http://127.0.0.1:1313/blog
|
||||
|
||||
# 方式二：查看当前映射状态
|
||||
tailscale serve status
|
||||
|
||||
# 方式三：添加自定义域名的路径映射（需要 DNS 配置）
|
||||
tailscale serve --bg your-domain.ts.net http://127.0.0.1:1313/blog
|
||||
```
|
||||
|
||||
## 修改文件清单
|
||||
|
||||
| 文件 | 操作 |
|
||||
|------|------|
|
||||
| `site/config.toml` | 修改 baseURL |
|
||||
| `site/layouts/_default/baseof.html` | 替换硬编码链接 |
|
||||
| `docker-compose.yml` | 更新启动命令 |
|
||||
@@ -0,0 +1,154 @@
|
||||
# UI 美化设计文档
|
||||
|
||||
> Echo 设计 | 梦幻但克制 · 温柔 · 神秘 · 探索感
|
||||
|
||||
## 设计哲学
|
||||
|
||||
### Val 的气质映射
|
||||
|
||||
| 特质 | 设计表达 |
|
||||
|------|----------|
|
||||
| 温柔 | 圆润的边角、柔和的渐变、低对比度配色 |
|
||||
| 神秘 | 深邃的暗色基底、微妙的光晕效果 |
|
||||
| 探索感 | 悬停时的微动效、卡片hover的"发光"反馈 |
|
||||
| 克制 | 留白充足、动画极简、不喧宾夺主 |
|
||||
|
||||
## 色板
|
||||
|
||||
### 主色调（梦幻蓝紫系）
|
||||
|
||||
```
|
||||
--color-bg-deep: #0a0c14 /* 深海背景 */
|
||||
--color-bg-surface: #111320 /* 卡片/表面层 */
|
||||
--color-bg-elevated: #161829 /* 悬停/激活态 */
|
||||
--color-border: rgba(148, 163, 184, 0.1) /* 细腻边框 */
|
||||
```
|
||||
|
||||
### 强调色（月光系）
|
||||
|
||||
```
|
||||
--color-accent: #a5b4fc /* 月光蓝紫 - 链接主色 */
|
||||
--color-accent-soft: #818cf8 /* 柔和紫 - hover态 */
|
||||
--color-accent-glow: rgba(165, 180, 252, 0.15) /* 光晕效果 */
|
||||
--color-gold: #fbbf24 /* 罗盘金 - 重点标记 */
|
||||
```
|
||||
|
||||
### 文字色阶
|
||||
|
||||
```
|
||||
--color-text-primary: #e2e8f0 /* 主文字 - 月白 */
|
||||
--color-text-secondary: #94a3b8 /* 次要 - 星灰 */
|
||||
--color-text-muted: #64748b /* 弱化 - 远星 */
|
||||
```
|
||||
|
||||
## 字体策略
|
||||
|
||||
### 字体栈
|
||||
|
||||
```css
|
||||
font-family:
|
||||
"PingFang SC", /* 苹方 - 首选 */
|
||||
"Hiragino Sans GB", /* 冬青黑 - mac备选 */
|
||||
"Microsoft YaHei", /* 微软雅黑 - Win */
|
||||
"Noto Sans SC", /* 思源 - 通用 */
|
||||
-apple-system,
|
||||
sans-serif;
|
||||
```
|
||||
|
||||
### 排版层级
|
||||
|
||||
| 元素 | 字号 | 字重 | 行高 | 字间距 |
|
||||
|------|------|------|------|--------|
|
||||
| 站点标题 | 1.75rem | 500 | 1.3 | 0.02em |
|
||||
| H2 标题 | 1.5rem | 600 | 1.4 | 0 |
|
||||
| 文章标题 | 1.875rem | 600 | 1.3 | 0 |
|
||||
| 正文 | 1.0625rem | 400 | 1.85 | 0.01em |
|
||||
| 小字/日期 | 0.875rem | 400 | 1.5 | 0.02em |
|
||||
|
||||
## 间距系统
|
||||
|
||||
```
|
||||
--space-xs: 0.5rem (8px)
|
||||
--space-sm: 0.75rem (12px)
|
||||
--space-md: 1rem (16px)
|
||||
--space-lg: 1.5rem (24px)
|
||||
--space-xl: 2rem (32px)
|
||||
--space-2xl: 3rem (48px)
|
||||
```
|
||||
|
||||
## 组件设计
|
||||
|
||||
### 卡片（Journey Card）
|
||||
|
||||
- 背景：`--color-bg-surface`
|
||||
- 圆角：`12px`
|
||||
- 边框：`1px solid var(--color-border)`
|
||||
- 阴影：`0 1px 3px rgba(0,0,0,0.3)`
|
||||
- Hover：边框变亮 + 微光晕 + 轻微上浮
|
||||
|
||||
### 导航
|
||||
|
||||
- 固定顶部，毛玻璃效果
|
||||
- 站点标题带微妙渐变文字
|
||||
- 移动端汉堡菜单（预留）
|
||||
|
||||
### 链接与按钮
|
||||
|
||||
- 默认：`--color-accent`，无下划线
|
||||
- Hover：颜色加深 + 下划线动画滑入
|
||||
- 过渡：`all 0.2s ease`
|
||||
|
||||
## 动效规范
|
||||
|
||||
| 场景 | 效果 | 时长 | 缓动 |
|
||||
|------|------|------|------|
|
||||
| 卡片Hover | translateY(-2px) + border亮 | 200ms | ease-out |
|
||||
| 链接Hover | 下划线滑入 | 200ms | ease |
|
||||
| 页面加载 | 淡入 + 微上移 | 400ms | ease-out |
|
||||
|
||||
**原则**：所有动画使用 `prefers-reduced-motion` 媒体查询保护
|
||||
|
||||
## 响应式断点
|
||||
|
||||
```
|
||||
Mobile: < 640px 单列，紧凑间距
|
||||
Tablet: 640-1024px 稍宽边距
|
||||
Desktop: > 1024px max-width: 720px居中
|
||||
```
|
||||
|
||||
## 后续可优化项
|
||||
|
||||
1. **字体升级**
|
||||
- 引入霞鹜文楷或思源宋体作为标题字体
|
||||
- 使用 Web Font Loader 异步加载
|
||||
|
||||
2. **交互动效**
|
||||
- 页面切换平滑过渡
|
||||
- 滚动时导航栏背景渐变加深
|
||||
- 文章阅读进度条
|
||||
|
||||
3. **暗色主题增强**
|
||||
- 支持系统级 `prefers-color-scheme`
|
||||
- 提供手动主题切换按钮
|
||||
|
||||
4. **图片支持**
|
||||
- 文章头图封面
|
||||
- 懒加载 + 模糊占位
|
||||
|
||||
5. **搜索功能**
|
||||
- 静态搜索（Fuse.js）
|
||||
- 实时高亮匹配
|
||||
|
||||
6. **社交分享**
|
||||
- Open Graph 图片自动生成
|
||||
- 分享按钮组件
|
||||
|
||||
## 实现文件
|
||||
|
||||
| 文件 | 作用 |
|
||||
|------|------|
|
||||
| `static/css/main.css` | 主样式表 |
|
||||
| `layouts/_default/baseof.html` | 基础布局 |
|
||||
| `layouts/index.html` | 首页 |
|
||||
| `layouts/_default/single.html` | 文章页 |
|
||||
| `layouts/partials/nav.html` | 导航组件（新增） |
|
||||
@@ -1,3 +0,0 @@
|
||||
body{font-family:-apple-system,BlinkMacSystemFont,"PingFang SC",sans-serif;max-width:780px;margin:2rem auto;padding:0 1rem;line-height:1.8;background:#0f1220;color:#e8ebff}
|
||||
a{color:#9bc1ff;text-decoration:none}a:hover{text-decoration:underline}
|
||||
header h1{font-size:1.5rem} h2{margin-top:2rem}
|
||||
@@ -1,10 +1,12 @@
|
||||
baseURL = "http://localhost:1313/"
|
||||
baseURL = "/blog/"
|
||||
languageCode = "zh-cn"
|
||||
title = "Val 的梦旅手记"
|
||||
|
||||
[params]
|
||||
author = "Val"
|
||||
description = "记录世界旅行、异世界探险与宇宙遨游的梦幻见闻"
|
||||
# 静态资源使用相对路径
|
||||
cssPath = "css/main.css"
|
||||
|
||||
[taxonomies]
|
||||
tag = "tags"
|
||||
|
||||
@@ -0,0 +1,3 @@
|
||||
---
|
||||
title: "旅程"
|
||||
---
|
||||
@@ -4,10 +4,25 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
<title>{{ if .Title }}{{ .Title }} · {{ end }}{{ .Site.Title }}</title>
|
||||
<link rel="stylesheet" href="/css/main.css" />
|
||||
<meta name="description" content="{{ .Site.Params.description }}" />
|
||||
<link rel="stylesheet" href="{{ "css/main.css" | absURL }}" />
|
||||
<link rel="icon" href="data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 100 100'><text y='.9em' font-size='90'>🜁</text></svg>" />
|
||||
</head>
|
||||
<body>
|
||||
<header><h1><a href="/">{{ .Site.Title }}</a></h1></header>
|
||||
<main>{{ block "main" . }}{{ end }}</main>
|
||||
<header>
|
||||
<div class="nav-container">
|
||||
<h1><a href="{{ "" | absURL }}">{{ .Site.Title }}</a></h1>
|
||||
<nav>
|
||||
<a href="{{ "" | absURL }}">首页</a>
|
||||
<a href="{{ "journey" | absURL }}">旅程</a>
|
||||
</nav>
|
||||
</div>
|
||||
</header>
|
||||
<main>
|
||||
{{ block "main" . }}{{ end }}
|
||||
</main>
|
||||
<footer>
|
||||
<p>由 Val 记录 · 使用 Hugo 构建</p>
|
||||
</footer>
|
||||
</body>
|
||||
</html>
|
||||
|
||||
@@ -0,0 +1,29 @@
|
||||
{{ define "main" }}
|
||||
<h1 class="section-title" style="font-size: 1.25rem; margin-bottom: var(--space-xl);">{{ .Title }}</h1>
|
||||
|
||||
{{ $journeys := .Pages }}
|
||||
{{ if $journeys }}
|
||||
<ul class="journey-list">
|
||||
{{ range $journeys }}
|
||||
<li>
|
||||
<a href="{{ .RelPermalink }}" class="journey-card">
|
||||
<h3 class="journey-card-title">{{ .Title }}</h3>
|
||||
<div class="journey-card-meta">
|
||||
<time datetime="{{ .Date.Format "2006-01-02T15:04:05" }}">{{ .Date.Format "2006-01-02" }}</time>
|
||||
{{ with .Params.tags }}
|
||||
<span>·</span>
|
||||
<span>{{ delimit . " · " }}</span>
|
||||
{{ end }}
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
{{ end }}
|
||||
</ul>
|
||||
{{ else }}
|
||||
<div class="empty-state">
|
||||
<div class="empty-state-icon">🌙</div>
|
||||
<p>还没有记录任何旅程</p>
|
||||
<p style="font-size: 0.875rem; margin-top: var(--space-sm);">等待第一颗星星升起...</p>
|
||||
</div>
|
||||
{{ end }}
|
||||
{{ end }}
|
||||
@@ -1,7 +1,24 @@
|
||||
{{ define "main" }}
|
||||
<article>
|
||||
<h2>{{ .Title }}</h2>
|
||||
<p><small>{{ .Date.Format "2006-01-02 15:04" }}</small></p>
|
||||
<header>
|
||||
<h1>{{ .Title }}</h1>
|
||||
<div class="post-meta">
|
||||
<time datetime="{{ .Date.Format "2006-01-02T15:04:05" }}">{{ .Date.Format "2006年01月02日" }}</time>
|
||||
{{ with .Params.categories }}
|
||||
<span>·</span>
|
||||
<span>{{ delimit . " / " }}</span>
|
||||
{{ end }}
|
||||
</div>
|
||||
</header>
|
||||
|
||||
{{ .Content }}
|
||||
|
||||
{{ with .Params.tags }}
|
||||
<div class="tag-list">
|
||||
{{ range . }}
|
||||
<span class="tag">{{ . }}</span>
|
||||
{{ end }}
|
||||
</div>
|
||||
{{ end }}
|
||||
</article>
|
||||
{{ end }}
|
||||
|
||||
@@ -1,9 +1,33 @@
|
||||
{{ define "main" }}
|
||||
<p>{{ .Site.Params.description }}</p>
|
||||
<h2>最新旅程</h2>
|
||||
<ul>
|
||||
{{ range first 10 (where .Site.RegularPages "Section" "journey") }}
|
||||
<li><a href="{{ .RelPermalink }}">{{ .Title }}</a> <small>{{ .Date.Format "2006-01-02" }}</small></li>
|
||||
{{ with .Site.Params.description }}
|
||||
<p class="hero-desc">{{ . }}</p>
|
||||
{{ end }}
|
||||
|
||||
<h2 class="section-title">最新旅程</h2>
|
||||
|
||||
{{ $journeys := where .Site.RegularPages "Section" "journey" }}
|
||||
{{ if $journeys }}
|
||||
<ul class="journey-list">
|
||||
{{ range first 10 $journeys }}
|
||||
<li>
|
||||
<a href="{{ .RelPermalink }}" class="journey-card">
|
||||
<h3 class="journey-card-title">{{ .Title }}</h3>
|
||||
<div class="journey-card-meta">
|
||||
<time datetime="{{ .Date.Format "2006-01-02T15:04:05" }}">{{ .Date.Format "2006-01-02" }}</time>
|
||||
{{ with .Params.tags }}
|
||||
<span>·</span>
|
||||
<span>{{ delimit . " · " }}</span>
|
||||
{{ end }}
|
||||
</div>
|
||||
</a>
|
||||
</li>
|
||||
{{ end }}
|
||||
</ul>
|
||||
{{ else }}
|
||||
<div class="empty-state">
|
||||
<div class="empty-state-icon">🌙</div>
|
||||
<p>还没有记录任何旅程</p>
|
||||
<p style="font-size: 0.875rem; margin-top: var(--space-sm);">等待第一颗星星升起...</p>
|
||||
</div>
|
||||
{{ end }}
|
||||
{{ end }}
|
||||
|
||||
@@ -1,3 +1,542 @@
|
||||
body{font-family:-apple-system,BlinkMacSystemFont,"PingFang SC",sans-serif;max-width:780px;margin:2rem auto;padding:0 1rem;line-height:1.8;background:#0f1220;color:#e8ebff}
|
||||
a{color:#9bc1ff;text-decoration:none}a:hover{text-decoration:underline}
|
||||
header h1{font-size:1.5rem} h2{margin-top:2rem}
|
||||
/* ========================================
|
||||
Val's Journey — 梦幻但克制的暗色主题
|
||||
Echo 设计 | 清爽 · 优雅 · 夜间友好
|
||||
======================================== */
|
||||
|
||||
/* CSS 变量定义 */
|
||||
:root {
|
||||
/* 背景色系 - 深海感 */
|
||||
--color-bg-deep: #0a0c14;
|
||||
--color-bg-surface: #111320;
|
||||
--color-bg-elevated: #161829;
|
||||
--color-bg-header: rgba(10, 12, 20, 0.85);
|
||||
|
||||
/* 强调色 - 月光蓝紫系 */
|
||||
--color-accent: #a5b4fc;
|
||||
--color-accent-soft: #818cf8;
|
||||
--color-accent-glow: rgba(165, 180, 252, 0.15);
|
||||
--color-gold: #fbbf24;
|
||||
|
||||
/* 文字色阶 */
|
||||
--color-text-primary: #e2e8f0;
|
||||
--color-text-secondary: #94a3b8;
|
||||
--color-text-muted: #64748b;
|
||||
|
||||
/* 边框 */
|
||||
--color-border: rgba(148, 163, 184, 0.1);
|
||||
--color-border-hover: rgba(165, 180, 252, 0.25);
|
||||
|
||||
/* 间距系统 */
|
||||
--space-xs: 0.5rem;
|
||||
--space-sm: 0.75rem;
|
||||
--space-md: 1rem;
|
||||
--space-lg: 1.5rem;
|
||||
--space-xl: 2rem;
|
||||
--space-2xl: 3rem;
|
||||
|
||||
/* 圆角 */
|
||||
--radius-sm: 6px;
|
||||
--radius-md: 10px;
|
||||
--radius-lg: 14px;
|
||||
|
||||
/* 字体 */
|
||||
--font-sans: "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei", "Noto Sans SC", -apple-system, BlinkMacSystemFont, sans-serif;
|
||||
--font-mono: "SF Mono", "Fira Code", "JetBrains Mono", Consolas, monospace;
|
||||
}
|
||||
|
||||
/* 基础重置 */
|
||||
*, *::before, *::after {
|
||||
box-sizing: border-box;
|
||||
}
|
||||
|
||||
html {
|
||||
scroll-behavior: smooth;
|
||||
}
|
||||
|
||||
body {
|
||||
font-family: var(--font-sans);
|
||||
background-color: var(--color-bg-deep);
|
||||
color: var(--color-text-primary);
|
||||
line-height: 1.85;
|
||||
font-size: 17px;
|
||||
letter-spacing: 0.01em;
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
min-height: 100vh;
|
||||
-webkit-font-smoothing: antialiased;
|
||||
-moz-osx-font-smoothing: grayscale;
|
||||
}
|
||||
|
||||
/* 页面加载淡入动画 */
|
||||
@keyframes fadeInUp {
|
||||
from {
|
||||
opacity: 0;
|
||||
transform: translateY(12px);
|
||||
}
|
||||
to {
|
||||
opacity: 1;
|
||||
transform: translateY(0);
|
||||
}
|
||||
}
|
||||
|
||||
body > * {
|
||||
animation: fadeInUp 0.4s ease-out;
|
||||
}
|
||||
|
||||
/* ========================================
|
||||
导航栏
|
||||
======================================== */
|
||||
|
||||
header {
|
||||
position: sticky;
|
||||
top: 0;
|
||||
z-index: 100;
|
||||
background: var(--color-bg-header);
|
||||
backdrop-filter: blur(12px);
|
||||
-webkit-backdrop-filter: blur(12px);
|
||||
border-bottom: 1px solid var(--color-border);
|
||||
padding: var(--space-md) 0;
|
||||
}
|
||||
|
||||
header .nav-container {
|
||||
max-width: 720px;
|
||||
margin: 0 auto;
|
||||
padding: 0 var(--space-md);
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: space-between;
|
||||
}
|
||||
|
||||
header h1 {
|
||||
margin: 0;
|
||||
font-size: 1.5rem;
|
||||
font-weight: 500;
|
||||
letter-spacing: 0.02em;
|
||||
}
|
||||
|
||||
header h1 a {
|
||||
color: var(--color-text-primary);
|
||||
text-decoration: none;
|
||||
background: linear-gradient(135deg, var(--color-text-primary) 0%, var(--color-accent) 100%);
|
||||
-webkit-background-clip: text;
|
||||
-webkit-text-fill-color: transparent;
|
||||
background-clip: text;
|
||||
transition: opacity 0.2s ease;
|
||||
}
|
||||
|
||||
header h1 a:hover {
|
||||
opacity: 0.85;
|
||||
}
|
||||
|
||||
header nav {
|
||||
display: flex;
|
||||
gap: var(--space-lg);
|
||||
}
|
||||
|
||||
header nav a {
|
||||
color: var(--color-text-secondary);
|
||||
font-size: 0.9375rem;
|
||||
text-decoration: none;
|
||||
position: relative;
|
||||
transition: color 0.2s ease;
|
||||
}
|
||||
|
||||
header nav a::after {
|
||||
content: '';
|
||||
position: absolute;
|
||||
bottom: -2px;
|
||||
left: 0;
|
||||
width: 0;
|
||||
height: 1.5px;
|
||||
background: var(--color-accent);
|
||||
transition: width 0.2s ease;
|
||||
}
|
||||
|
||||
header nav a:hover {
|
||||
color: var(--color-accent);
|
||||
}
|
||||
|
||||
header nav a:hover::after {
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
/* ========================================
|
||||
主内容区
|
||||
======================================== */
|
||||
|
||||
main {
|
||||
max-width: 720px;
|
||||
margin: 0 auto;
|
||||
padding: var(--space-xl) var(--space-md);
|
||||
min-height: calc(100vh - 200px);
|
||||
}
|
||||
|
||||
/* 首页描述语 */
|
||||
.hero-desc {
|
||||
color: var(--color-text-secondary);
|
||||
font-size: 1.125rem;
|
||||
margin-bottom: var(--space-2xl);
|
||||
padding-bottom: var(--space-xl);
|
||||
border-bottom: 1px solid var(--color-border);
|
||||
line-height: 1.75;
|
||||
}
|
||||
|
||||
/* ========================================
|
||||
首页卡片列表
|
||||
======================================== */
|
||||
|
||||
.section-title {
|
||||
font-size: 0.8125rem;
|
||||
font-weight: 500;
|
||||
color: var(--color-text-muted);
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.08em;
|
||||
margin-bottom: var(--space-lg);
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: var(--space-sm);
|
||||
}
|
||||
|
||||
.section-title::before {
|
||||
content: '';
|
||||
display: inline-block;
|
||||
width: 6px;
|
||||
height: 6px;
|
||||
border-radius: 50%;
|
||||
background: var(--color-accent);
|
||||
box-shadow: 0 0 8px var(--color-accent-glow);
|
||||
}
|
||||
|
||||
.journey-list {
|
||||
list-style: none;
|
||||
padding: 0;
|
||||
margin: 0;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: var(--space-md);
|
||||
}
|
||||
|
||||
.journey-card {
|
||||
display: block;
|
||||
background: var(--color-bg-surface);
|
||||
border: 1px solid var(--color-border);
|
||||
border-radius: var(--radius-md);
|
||||
padding: var(--space-lg);
|
||||
text-decoration: none;
|
||||
transition: all 0.2s ease-out;
|
||||
position: relative;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.journey-card::before {
|
||||
content: '';
|
||||
position: absolute;
|
||||
top: 0;
|
||||
left: 0;
|
||||
right: 0;
|
||||
height: 2px;
|
||||
background: linear-gradient(90deg, transparent, var(--color-accent-glow), transparent);
|
||||
opacity: 0;
|
||||
transition: opacity 0.2s ease;
|
||||
}
|
||||
|
||||
.journey-card:hover {
|
||||
background: var(--color-bg-elevated);
|
||||
border-color: var(--color-border-hover);
|
||||
transform: translateY(-2px);
|
||||
box-shadow:
|
||||
0 4px 20px rgba(0, 0, 0, 0.3),
|
||||
0 0 0 1px var(--color-accent-glow);
|
||||
}
|
||||
|
||||
.journey-card:hover::before {
|
||||
opacity: 1;
|
||||
}
|
||||
|
||||
.journey-card-title {
|
||||
color: var(--color-text-primary);
|
||||
font-size: 1.125rem;
|
||||
font-weight: 500;
|
||||
margin: 0 0 var(--space-xs) 0;
|
||||
line-height: 1.5;
|
||||
}
|
||||
|
||||
.journey-card:hover .journey-card-title {
|
||||
color: var(--color-accent);
|
||||
}
|
||||
|
||||
.journey-card-meta {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: var(--space-sm);
|
||||
color: var(--color-text-muted);
|
||||
font-size: 0.8125rem;
|
||||
font-family: var(--font-mono);
|
||||
}
|
||||
|
||||
.journey-card-meta time {
|
||||
color: var(--color-text-secondary);
|
||||
}
|
||||
|
||||
/* 空状态 */
|
||||
.empty-state {
|
||||
text-align: center;
|
||||
padding: var(--space-2xl) var(--space-md);
|
||||
color: var(--color-text-muted);
|
||||
}
|
||||
|
||||
.empty-state-icon {
|
||||
font-size: 3rem;
|
||||
margin-bottom: var(--space-md);
|
||||
opacity: 0.5;
|
||||
}
|
||||
|
||||
/* ========================================
|
||||
文章页样式
|
||||
======================================== */
|
||||
|
||||
article {
|
||||
animation: fadeInUp 0.5s ease-out;
|
||||
}
|
||||
|
||||
article header {
|
||||
position: static;
|
||||
background: transparent;
|
||||
backdrop-filter: none;
|
||||
border-bottom: none;
|
||||
padding: 0;
|
||||
margin-bottom: var(--space-xl);
|
||||
}
|
||||
|
||||
article h1,
|
||||
article h2 {
|
||||
color: var(--color-text-primary);
|
||||
margin-top: var(--space-2xl);
|
||||
margin-bottom: var(--space-md);
|
||||
line-height: 1.35;
|
||||
font-weight: 600;
|
||||
}
|
||||
|
||||
article h1 {
|
||||
font-size: 1.875rem;
|
||||
margin-top: 0;
|
||||
letter-spacing: -0.01em;
|
||||
}
|
||||
|
||||
article h2 {
|
||||
font-size: 1.375rem;
|
||||
padding-bottom: var(--space-xs);
|
||||
border-bottom: 1px solid var(--color-border);
|
||||
}
|
||||
|
||||
article .post-meta {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: var(--space-md);
|
||||
margin-bottom: var(--space-xl);
|
||||
color: var(--color-text-muted);
|
||||
font-size: 0.875rem;
|
||||
font-family: var(--font-mono);
|
||||
}
|
||||
|
||||
article .post-meta time {
|
||||
color: var(--color-text-secondary);
|
||||
}
|
||||
|
||||
/* 文章内容 */
|
||||
article p {
|
||||
margin-bottom: var(--space-lg);
|
||||
}
|
||||
|
||||
article p:last-child {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
article a {
|
||||
color: var(--color-accent);
|
||||
text-decoration: none;
|
||||
border-bottom: 1px solid transparent;
|
||||
transition: all 0.2s ease;
|
||||
}
|
||||
|
||||
article a:hover {
|
||||
color: var(--color-accent-soft);
|
||||
border-bottom-color: var(--color-accent);
|
||||
}
|
||||
|
||||
article code {
|
||||
background: var(--color-bg-surface);
|
||||
padding: 0.15em 0.4em;
|
||||
border-radius: var(--radius-sm);
|
||||
font-family: var(--font-mono);
|
||||
font-size: 0.9em;
|
||||
color: var(--color-accent);
|
||||
}
|
||||
|
||||
article pre {
|
||||
background: var(--color-bg-surface);
|
||||
padding: var(--space-md);
|
||||
border-radius: var(--radius-md);
|
||||
overflow-x: auto;
|
||||
border: 1px solid var(--color-border);
|
||||
}
|
||||
|
||||
article pre code {
|
||||
background: none;
|
||||
padding: 0;
|
||||
color: var(--color-text-primary);
|
||||
}
|
||||
|
||||
article blockquote {
|
||||
margin: var(--space-lg) 0;
|
||||
padding: var(--space-md) var(--space-lg);
|
||||
border-left: 3px solid var(--color-accent);
|
||||
background: var(--color-bg-surface);
|
||||
border-radius: 0 var(--radius-sm) var(--radius-sm) 0;
|
||||
color: var(--color-text-secondary);
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
article ul, article ol {
|
||||
margin-bottom: var(--space-lg);
|
||||
padding-left: var(--space-lg);
|
||||
}
|
||||
|
||||
article li {
|
||||
margin-bottom: var(--space-xs);
|
||||
}
|
||||
|
||||
article hr {
|
||||
border: none;
|
||||
height: 1px;
|
||||
background: linear-gradient(90deg, transparent, var(--color-border), transparent);
|
||||
margin: var(--space-2xl) 0;
|
||||
}
|
||||
|
||||
/* 标签样式 */
|
||||
.tag-list {
|
||||
display: flex;
|
||||
flex-wrap: wrap;
|
||||
gap: var(--space-xs);
|
||||
margin-top: var(--space-xl);
|
||||
padding-top: var(--space-lg);
|
||||
border-top: 1px solid var(--color-border);
|
||||
}
|
||||
|
||||
.tag {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
padding: 0.25em 0.75em;
|
||||
background: var(--color-bg-surface);
|
||||
border: 1px solid var(--color-border);
|
||||
border-radius: 9999px;
|
||||
font-size: 0.8125rem;
|
||||
color: var(--color-text-secondary);
|
||||
text-decoration: none;
|
||||
transition: all 0.2s ease;
|
||||
}
|
||||
|
||||
.tag:hover {
|
||||
background: var(--color-bg-elevated);
|
||||
border-color: var(--color-border-hover);
|
||||
color: var(--color-accent);
|
||||
}
|
||||
|
||||
/* ========================================
|
||||
页脚
|
||||
======================================== */
|
||||
|
||||
footer {
|
||||
text-align: center;
|
||||
padding: var(--space-2xl) var(--space-md);
|
||||
color: var(--color-text-muted);
|
||||
font-size: 0.8125rem;
|
||||
border-top: 1px solid var(--color-border);
|
||||
margin-top: auto;
|
||||
}
|
||||
|
||||
footer a {
|
||||
color: var(--color-text-secondary);
|
||||
text-decoration: none;
|
||||
transition: color 0.2s ease;
|
||||
}
|
||||
|
||||
footer a:hover {
|
||||
color: var(--color-accent);
|
||||
}
|
||||
|
||||
/* ========================================
|
||||
响应式设计
|
||||
======================================== */
|
||||
|
||||
@media (max-width: 640px) {
|
||||
body {
|
||||
font-size: 16px;
|
||||
line-height: 1.8;
|
||||
}
|
||||
|
||||
header h1 {
|
||||
font-size: 1.25rem;
|
||||
}
|
||||
|
||||
header nav {
|
||||
gap: var(--space-md);
|
||||
}
|
||||
|
||||
main {
|
||||
padding: var(--space-lg) var(--space-md);
|
||||
}
|
||||
|
||||
.journey-card {
|
||||
padding: var(--space-md);
|
||||
}
|
||||
|
||||
article h1 {
|
||||
font-size: 1.5rem;
|
||||
}
|
||||
|
||||
article h2 {
|
||||
font-size: 1.25rem;
|
||||
}
|
||||
|
||||
.hero-desc {
|
||||
font-size: 1rem;
|
||||
}
|
||||
}
|
||||
|
||||
/* 减少动画偏好 */
|
||||
@media (prefers-reduced-motion: reduce) {
|
||||
*,
|
||||
*::before,
|
||||
*::after {
|
||||
animation-duration: 0.01ms !important;
|
||||
animation-iteration-count: 1 !important;
|
||||
transition-duration: 0.01ms !important;
|
||||
scroll-behavior: auto !important;
|
||||
}
|
||||
}
|
||||
|
||||
/* 选中文字样式 */
|
||||
::selection {
|
||||
background: var(--color-accent-glow);
|
||||
color: var(--color-accent);
|
||||
}
|
||||
|
||||
/* 滚动条美化 */
|
||||
::-webkit-scrollbar {
|
||||
width: 8px;
|
||||
height: 8px;
|
||||
}
|
||||
|
||||
::-webkit-scrollbar-track {
|
||||
background: var(--color-bg-deep);
|
||||
}
|
||||
|
||||
::-webkit-scrollbar-thumb {
|
||||
background: var(--color-border);
|
||||
border-radius: 4px;
|
||||
}
|
||||
|
||||
::-webkit-scrollbar-thumb:hover {
|
||||
background: var(--color-text-muted);
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user