EchoAgent: Guideline-Centric Reasoning Agent for Echocardiography Measurement and Interpretation

OG-VLA is a new architecture that integrates Vision Language Action models with 3D-aware policies to enhance robot manipulation tasks. It addresses the challenge of translating natural language instructions and RGBD observations into robot actions. While 3D-aware policies excel in precise tasks, they often struggle with generalization to new scenarios. Conversely, VLAs are adept at generalizing across instructions but can be sensitive to variations in camera and robot poses. OG-VLA aims to improve this generalization by leveraging knowledge from language and vision models.

AgentArmor is a program analysis framework designed to enhance the security of Large Language Model (LLM) agents against prompt injection attacks. By treating agent runtime traces as structured programs, AgentArmor converts these traces into graph-based representations, enabling the enforcement of security policies through a type system. The framework consists of three components: a graph constructor, a property registry, and a security policy enforcer, aiming to mitigate the risks associated with the dynamic behavior of LLM agents.

PRISM-0 is a newly introduced framework for Scene Graph Generation (SGG) that addresses the limitations of traditional supervised methods, which often suffer from training bias and limited predicate diversity. This zero-shot open-vocabulary framework utilizes foundation models in a bottom-up approach to enhance predicate extraction from visual inputs. By filtering detected object pairs and employing a Vision-Language Model (VLM) and a Large Language Model (LLM), PRISM-0 generates a wide range of predicates, validated through a Visual Question Answering (VQA) model, thereby enriching existing d…