We use real-time data at scale, AI and machine learning and the power of the cloud to solve challenging industry problems.
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Evaluating multi-step LLM systems requires moving beyond end-to-end metrics. Our researchers introduced RAFFLES, an iterative reasoning-based fault attribution framework that uses a central Judge and specialized Evaluators to pinpoint step-level faults in complex AI workflows.
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Read the full paper to see how RAFFLES diagnoses step-level errors across multi-agent and mathematical reasoning benchmarks: capitalone.science/publicati…
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While numerous Direct Preference Optimization (DPO) extensions have been introduced, discerning which components drive downstream gains remains tough. We collaborated with researchers from @Columbia to build RainbowPO, a unified framework that categorizes and combines seven key DPO improvements.
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Dive into the full paper for details on the unified objective and benchmark results: capitalone.science/publicati…
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We strongly support the call for collective action on cyber defense. Together, we can make the digital infrastructure we all depend on more secure.
An open letter for a global surge in cyber defense, signed by over 100 organizations including Anthropic, AWS, Google, Microsoft, OpenAI, and Oracle.
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Instead of relying on third-party API wrappers, we build custom, proprietary AI models trained on our modern data ecosystem. Our Chief Scientist Prem Natarajan recently joined @IEEESpectrum to discuss how our research team advances model customization for complex financial environments.
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Defending LLMs requires a proactive offensive strategy. That’s why Capital One researchers published an end-to-end overview of LLM red teaming, which involves proactively attacking models to identify vulnerabilities. Their work maps out attack methods, software packages, and evaluation metrics for practical applications.
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Read the full survey paper to explore the red teaming frameworks and methodologies: capitalone.science/publicati…
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As part of our commitment to AI building AI fluency at scale, we recently brought together over 10,000 of our engineers for a month of hands-on training to master agentic coding workflows with partners like @Google and @AnthropicAI.
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Read our full blog on building an AI-ready workforce: capitalone.com/tech/ai/advan…
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Complex RAG queries often force a choice between missing context and bloated prompts. Capital One researchers introduced FB-RAG, a training-free framework using lightweight LLMs to sample future outputs and guide the final generator—cutting latency up to 48%.
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Check out the full paper to see the architecture and benchmark results on LongBench and ∞Bench: capitalone.science/publicati…
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At Capital One, we’re building custom AI stack layers and task-specialized models to run AI reliably at scale. By pairing open-source models with proprietary data and breaking down workflows, we get better latency, compute efficiency, and deterministic logic.
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Read more from Milind Naphade, SVP, AI Foundations, on our science-first approach to Enterprise AI via Forbes: forbes.com/sites/capitalone/…
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Capital One researchers developed APT (Adversarially Pre-trained Transformer) for zero-shot tabular prediction. APT uses adversarial synthetic data agents and a mixture block architecture to handle arbitrary class counts with sub-second runtimes.
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Read the full paper to explore our benchmark results and architecture on our science site:
capitalone.science/publicati…
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AI security improves when organizations share research, tools, and real-world experience.
We’re joining other industry leaders, including @NVIDIA, in the Open Secure AI Alliance to help organizations identify and address software vulnerabilities and strengthen critical systems.
Learn more: blogs.nvidia.com/blog/open-s…
AI security advances when the industry builds in the open, together.
We're introducing the Open Secure AI Alliance with industry leaders to develop new techniques and tools to safeguard software and agents.
By sharing models, tooling and research in the open, we can broaden the community of defenders.
Learn more about the founding members’ contributions: nvda.ws/4pD8Fc5
ALT Logos of various companies including Adobe, Cisco, and Microsoft.
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Earlier this month, we wrapped up our July tour stops at #ICML2026 and #ACL2026NLP! Our teams shared peer-reviewed research on LLM reasoning and agentic safety with a focus on how we build responsible and scalable foundation model architectures.
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Explore our research and learn more about upcoming AI World Tour stops capitalone.com/tech/ai-world…
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