[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-78198":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":13,"subscribersCount":13,"size":13,"stars1d":13,"stars7d":16,"stars30d":17,"stars90d":13,"forks30d":13,"starsTrendScore":18,"compositeScore":19,"rankGlobal":10,"rankLanguage":10,"license":20,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":21,"hasPages":21,"topics":23,"createdAt":10,"pushedAt":10,"updatedAt":24,"readmeContent":25,"aiSummary":26,"trendingCount":13,"starSnapshotCount":13,"syncStatus":18,"lastSyncTime":27,"discoverSource":28},78198,"ATLAS","ZiyuGuo99\u002FATLAS","ZiyuGuo99","One Discrete Word for Visual Reasoning Overtakes Agentic and Latent Methods","https:\u002F\u002Fatlas-oneword.github.io\u002F",null,"Python",128,0,16,1,3,14,2,42.9,"Apache License 2.0",false,"main",[],"2026-06-12 04:01:23","\u003Cdiv align=\"center\">\n\n\u003Ch1 align=\"center\">\n  \u003Cimg src=\"assets\u002Flogo.png\" height=\"45\" style=\"vertical-align: middle; margin-right: 5px;\"> ATLAS: Agentic or Latent Visual Reasoning?\u003Cbr>\n  One Word is Enough for Both\n\u003C\u002Fh1>\n\n[[🌍 Project Page](https:\u002F\u002Fatlas-oneword.github.io\u002F)] [[📖 Paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2605.15198)] [[🤗 Model & Data](https:\u002F\u002Fatlas-oneword.github.io\u002F)]\n\n\u003C\u002Fdiv>\n\n## News\n\n- **[2026.5.15]** The paper is released on arXiv. 🚀\n- Code, model, and data release are currently under company review. Coming soon in a few days.\n\n## Overview\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"assets\u002Fintro_v4_00.png\" width=\"95%\" alt=\"ATLAS overview\"\u002F>\n\u003C\u002Fp>\n\n## ATLAS\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"assets\u002Fpipe_sep_00.png\" width=\"84%\" alt=\"ATLAS pipeline\"\u002F>\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"assets\u002Flagrpo_00.png\" width=\"84%\" alt=\"LA-GRPO\"\u002F>\n\u003C\u002Fp>\n\n## Visualization\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"assets\u002Fvis1_part1_00.png\" width=\"92%\" alt=\"ATLAS qualitative examples\"\u002F>\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"assets\u002Fvis_attn1_00.png\" width=\"92%\" alt=\"ATLAS attention visualization\"\u002F>\n\u003C\u002Fp>\n\n## Citation\n\n```bibtex\n@article{guo2026atlas,\n  title   = {ATLAS: Agentic or Latent Visual Reasoning? One Word is Enough for Both},\n  author  = {Guo, Ziyu and Liu, Rain and Chen, Xinyan and Heng, Pheng Ann},\n  journal = {arXiv preprint},\n  year    = {2026}\n}\n","ATLAS是一个专注于视觉推理的项目，通过单一离散词来实现代理性和潜在性的视觉推理。其核心功能包括使用先进的算法和模型来处理复杂的图像数据，并能够生成直观的注意力可视化结果，以展示模型如何理解图像内容。技术上，ATLAS结合了最新的研究成果，在保证高效性的同时提高了推理准确性。该项目适用于需要高级图像理解和分析的应用场景，如智能监控、自动驾驶以及医疗影像分析等。","2026-06-11 03:56:36","CREATED_QUERY"]