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• Getting the Source Right, Not Just the Fact: Source-Aware Verification for MCP Agents

  ### Getting the Source Right, Not Just the Fact: Source-Aware Verification for MCP Agents Our latest paper, ProvenanceGuard: Source-Aware Factuality Verification for MCP-Based LLM Agents, targets the gap in current verification methods. The failure mode we care about is called cross-source conflation: a claim that is true somewhere in the evidence, but attributed to the wrong source. Consider a customer support agent that answers, "According to the account record, this plan includes a 30-day refund window." The r…

  6 min read
• Distributed training on OpenShift AI 3.4 with Kubeflow Trainer v2

  ### Briefing #### Introduction Until recently, running distributed training on Kubernetes required custom resource definitions (CRDs) specific to each machine learning (ML) framework. These frameworks had different semantics, failure behaviors, and methods for configuring fundamental aspects such as how many nodes, how they find each other, and what happens when something breaks. Red Hat OpenShift AI 3.4 introduced Kubeflow Trainer v2, a unified training API that replaces framework-specific CRDs with a single `Tr…

  13 min read
• UniEvo-VL: Self-Distillation Training for Multimodal Model Self-Improvement

  ### Briefing Computer Science > Artificial Intelligence #### Abstract Modern multimodal models enable unified systems for generation and understanding, promising to provide and learn from their own feedback. Inspired by this capability, we introduce **UniEvo-VL**, a self-evolving framework for multimodal models to improve through constructive self-correction feedback during test-time compute. Instead of relying on a separate, often larger, teacher, we leverage their self-critiques as privileged information and…

  2 min read