Add Branch-and-Browse arXiv (#363)

Author: TCtower

source/_data/SymbioticLab.bib

@@ -2186,6 +2186,23 @@ @InProceedings{rdx:hotnets25
   }
 }
 
+@Article{branchandbrowse:arxiv25,
+  author        = {Shiqi He and Yue Cui and Xinyu Ma and Yaliang Li and Bolin Ding and Mosharaf Chowdhury},
+  title         = {{Branch-and-Browse}: Efficient and Controllable Web Exploration with Tree-Structured Reasoning and Action Memory},
+  year          = {2025},
+  month         = {Oct},
+  volume        = {abs/2510.19838},
+  archiveprefix = {arXiv},
+  eprint        = {2510.19838},
+  url           = {https://arxiv.org/abs/2510.19838},
+  publist_confkey = {arXiv:2510.19838},
+  publist_link = {paper || https://arxiv.org/abs/2510.19838},
+  publist_topic = {Systems + AI},
+  publist_abstract = {
+Autonomous web agents powered by large language models (LLMs) show strong potential for performing goal-oriented tasks such as information retrieval, report generation, and online transactions. These agents mark a key step toward practical embodied reasoning in open web environments. However, existing approaches remain limited in reasoning depth and efficiency: vanilla linear methods fail at multi-step reasoning and lack effective backtracking, while other search strategies are coarse-grained and computationally costly. We introduce Branch-and-Browse, a fine-grained web agent framework that unifies structured reasoning-acting, contextual memory, and efficient execution. It (i) employs explicit subtask management with tree-structured exploration for controllable multi-branch reasoning, (ii) bootstraps exploration through efficient web state replay with background reasoning, and (iii) leverages a page action memory to share explored actions within and across sessions. On the WebArena benchmark, Branch-and-Browse achieves a task success rate of 35.8% and reduces execution time by up to 40.4% relative to state-of-the-art methods. These results demonstrate that Branch-and-Browse is a reliable and efficient framework for LLM-based web agents.
+  }
+} 
+
 @Article{sphinx:arxiv25,
   author          = {Yuchen Xia and Souvik Kundu and Mosharaf Chowdhury and Nishil Talati},
   title           = {Sphinx: Efficiently Serving Novel View Synthesis using Regression-Guided Selective Refinement},