# Capital One at EMNLP 2026 in Budapest DevFeed: [Capital One at EMNLP 2026 in Budapest]() Original publisher: [Read original article]() Author: Capital One Tech Published: 2026-10-23T13:23:19Z Content type: news Language: en Sources: [Capital One Tech]() Topics: [AI Research](), [Large Language Model](), [Retrieval Augmented Generation (RAG)](), [LLM security](), [ai safety](), [Multi Agent Systems]() Tags: [2026](), [academic](), [adaptive](), [adversarial](), [agentic](), [agentic-systems](), [ai](), [ai-ml](), [ai-research](), [applications](), [artificial-intelligence](), [attack](), [authoring](), [behavior](), [california](), [challenges](), [collaboration](), [complex](), [conference](), [consumer](), [data-science](), [detection](), [naturallanguageprocessing](), [science]() ## AI overview The excerpt describes 16 Capital One and academic partner papers accepted at EMNLP 2026, spanning model safety, retrieval, intent resolution, evaluation and agent workflows. Examples include FinRT, which distills adaptive red-teaming into reusable adversarial generators for safety checks under fixed generation budgets; MEMGUARD, which inspects and isolates untrusted inputs before they contaminate long-term memory; and CAGE, which organizes retrieved passages as directed heterogeneous graphs to preserve coherence across chunks. SPARC-RAG combines sequential refinement with parallel exploration and context management, while AIR probes ambiguous text-to-SQL requests and asks targeted clarifying questions, even when only the schema is available. For GUI agents, HiViG adds progress summaries and visually grounded action critique; the excerpt reports relative task-success improvements of 5.8% for open-weight models and 9.0% for Gemini-3 Flash versus the strongest critics evaluated. Other highlighted work includes benchmarks for agent workflows, prompt regression, code-switching and judge bias. These are summaries of selected papers from the supplied text. ## Source excerpt Capital One researchers and academic partners present 16 accepted papers advancing responsible AI, RAG and LLM evaluation.Advancing natural language processing through multi-sector research Natural language processing (NLP) forms the backbone of modern agentic systems, document intelligence and enterprise artificial intelligence applications. Capital One researchers, in collaboration with our academic partners, are presenting 16 accepted papers at the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP 2026), taking place in Budapest, Hungary, October 24-29. These research papers highlight ongoing investments in foundational AI/ML science, robust red-teaming, graph-encoded retrieval-augmented generation (RAG), intent resolution and trustworthy evaluation techniques. Multi-sector collaboration accelerates the transition of foundational NLP innovations into secure, reliable enterprise systems. By co-authoring several of these papers with university partners including the University of Illinois Urbana-Champaign, University of Southern California and UNC Chapel Hill, Capital One contributes to open science while addressing complex technical challenges at scale. Highlighted EMNLP 2026 research papers Papers on robustness, red-teaming and model safety: FinRT: Distilling adaptive red-teaming strategies into reusable adversarial generators in consumer finance Rikhiya Ghosh, Himanshu Kumar, Sriram Venkatapathy, Sahil Wadhwa, Alexandre Day, Andy Luo, Pranab Mohanty Large language models remain susceptible to adversarial prompts that induce policy-violating behavior in regulated financial contexts. This work introduces FinRT, a framework that distills adaptive red-teaming strategies into reusable adversarial generators, significantly improving attack detection and safety verification under fixed generation budgets. MEMGUARD: Preventing memory contamination in long-term memory-augmented large language models Hyeonjeong Ha, William Campbell, Yue Wu (academ