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README.md

@@ -42,7 +42,7 @@ Average **5.1 evidence segments per question** and **25.9 hours of memory backtr
 - 💻 **Code & reference eval scripts:** <https://github.com/Ziyang412/EgoMemReason>
 - 🌐 **Project page:** <https://egomemreason.github.io/>
 - 🎬 **EgoLife video frames (separate license):** <https://egolife-ai.github.io/>
-- 📄 **Paper:** *coming soon*
+- 📄 **Paper:** <https://arxiv.org/abs/2605.09874>
 
 ## Composition
 
@@ -107,13 +107,13 @@ The reference inference scripts for 12 MLLMs and 5 agentic frameworks (Gemini, G
 ## Citation
 
 ```bibtex
-@article{wang2026egomemreason,
-  title   = {EgoMemReason: A Memory-driven Reasoning Benchmark for
-             Long-Horizon Egocentric Video Understanding},
-  author  = {Wang, Ziyang and Zhang, Yue and Yu, Shoubin and Zhang, Ce and
-             Zhao, Zengqi and Yoon, Jaehong and Lee, Hyunji and
-             Bertasius, Gedas and Bansal, Mohit},
-  year    = {2026},
-  journal = {arXiv preprint}
+@misc{wang2026egomemreasonmemorydrivenreasoningbenchmark,
+      title={EgoMemReason: A Memory-Driven Reasoning Benchmark for Long-Horizon Egocentric Video Understanding},
+      author={Ziyang Wang and Yue Zhang and Shoubin Yu and Ce Zhang and Zengqi Zhao and Jaehong Yoon and Hyunji Lee and Gedas Bertasius and Mohit Bansal},
+      year={2026},
+      eprint={2605.09874},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2605.09874},
 }
 ```