Add Mordal code link and BnB ACL'26

source/_data/SymbioticLab.bib

@@ -2324,6 +2324,7 @@ @InProceedings{mordal:iclr26
   publist_confkey = {ICLR'26},
   publist_link    = {paper || mordal-iclr26.pdf},
   publist_topic   = {Systems + AI},
+  publist_link    = {code || https://github.com/SymbioticLab/Mordal},
   publist_abstract = {
   Incorporating multiple modalities into large language models (LLMs) is a powerful way to enhance their understanding of non-textual data, enabling them to perform multimodal tasks.
   Vision language models (VLMs) form the fastest growing category of multimodal models because of their many practical use cases, including in healthcare, robotics, and accessibility.
@@ -2446,3 +2447,16 @@ @Article{openg2g:arxiv26
 In order to understand the impact of large datacenters on the grid and to facilitate the design of effective coordination strategies, we build OpenG2G, a simulation platform for AI datacenter-grid runtime coordination. We show that OpenG2G is capable of answering a wide range of coordination questions by allowing users to implement and compare various control paradigms (including classic, optimization, and learning-based controllers), and quantify how AI model and deployment choices affect datacenter flexibility and coordination outcomes. This versatility is enabled by OpenG2G's modular and extensible architecture: a datacenter backend driven by real measurements of production-grade AI services, a grid backend built on high-fidelity grid simulators, and a generic controller interface that closes the loop between them. We describe the design of OpenG2G and demonstrate its usefulness through realistic grid scenarios and AI workloads.
   }
 }
+
+@Article{branchandbrowse:acl26,
+  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          = {2026},
+  month         = {July},
+  publist_confkey = {ACL'26},
+  publist_link = {paper || branchandbrowse-acl26.pdf},
+  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.
+  }
+} 

source/open-source/index.md

@@ -59,6 +59,9 @@ sections:
 
   - title: "Selected Software Artifacts"
     items:
+      - name: "Mordal"
+        github_url: "https://github.com/SymbioticLab/Mordal"
+        description: "Automated pretrained model selection for VLMs."
       - name: "TetriServe"
         github_url: "https://github.com/DiT-Serving/TetriServe"
         description: "Efficient serving system for mixed DiT workloads."

source/publications/index.md

@@ -482,6 +482,14 @@ venues:
         name: ACM Conference on AI and Agentic Systems Demo Track
         date: 2026-05-26
         url: https://caisconf.org
+  ACL:
+    category: Conferences
+    occurrences:
+      - key: ACL'26
+        name: The 64th Annual Meeting of the Association for Computational Linguistics
+        date: 2026-07-02
+        url: https://2026.aclweb.org/
+        acceptance: 19%
 {% endpublist %}
 
 ---