Today at 1:00pm, join Dan Garrette and Miloš Stanojević at the #EMNLP2026# Google booth (D5) for a live demo of @GoogleDeepMind SL2T, bringing ASL-to-English translation to Gboard and Live Transcribe on Pixel 11. Read the blog:

Don't miss our interactive demo analyzing 16,000+ NLP paper limitations. Join Piyawat Lertvittayakumjorn at the #EMNLP2026# Google booth (#D5#) today at 10:30am to see how research trends have shifted and look up how your own paper was coded. Read the paper:

Don't miss your chance to explore research career pathways and student programs at Google during @emnlpmeeting. Stop by the #EMNLP2026# Google booth (#D5#) today at 4:15pm for a live Recruiting Q&A with Sunipa Dev and Nima Kalantari to ask your questions and connect with our team.

How well do LLMs understand Southeast Asian languages? Today at 10:30am, join Shubham Mittal at the #EMNLP2026# Google booth (#D5#) for a live demo of DEEPSEA, a benchmark to evaluate LLMs across 12 Southeast Asian languages. Read the paper:

How can positive alignment anchor autonomous AI agents to core values rather than brittle rubrics? Join @GoogleDeepMind researcher Verena Rieser today at 9:00am for the @emnlpmeeting Industry Keynote, and visit the #EMNLP2026# Google booth (D5) afterward to connect with our team.

Red Hat researchers, in collaboration with Purdue, developed VEX-Bench—a benchmark for assessing large language models (LLMs) in software supply chain vulnerability triage. Traditional tools often generate up to 70% false alerts. Discover the findings: #EMNLP2026#

🎉 Excited to share that our paper "VEX-Bench: Benchmarking LLM Agents for Assessing Exploitability of Software Supply Chain Vulnerabilities” has been accepted to EMNLP 2026! This work is a collaboration between @PurdueCS and multiple teams from @RedHat (AI Engineering, Ecosystem Engineering, and Product Security). Software supply chains are an increasingly important security challenge. While tools can identify vulnerable dependencies, determining whether a vulnerability is actually exploitable in a downstream application often requires substantial cross-repository reasoning and security expertise. In this work, we introduce VEX-Bench, a benchmark for evaluating LLM agents on this problem: - 75 real-world vulnerability cases mined from GitHub - Expert-labeled across Python, Java, and Go - Evaluation of 9 models across 3 agent harnesses - Going beyond binary “exploitable / not exploitable” decisions to evaluate the reasoning behind exploitability Our results show that frontier LLM agents can perform well on binary vulnerability assessment, but fine-grained exploitability reasoning remains significantly more challenging. This points to an important direction for AI-assisted software security: it’s not enough for an agent to identify that something is vulnerable. We need to understand why it is exploitable, whether it actually matters in context, and how confidently we can trust the assessment. 🙌 Huge thanks to all the co-authors and collaborators who made this possible! @tian_yi_zhang, Jiahao Shi, Yifeng Di, Ilan Pinto, Edward Tsien, Hongjiao Zhang, Ronit Dey, Ilona Shishov, Gal Netanel, Zvi Grinberg, Vladimir Belousov, Bat-Zion Rotman 📄 Paper: #EMNLP2026# #AI# #LLM# #LLMAgents# #Cybersecurity# #SoftwareSupplyChain# #OpenSource# #PurdueCS# #RedHat#