ai framework
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Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence
The researchers introduce a new framework called 'Agentic Self-Improvement' to address challenges in professional workflows related to modern black-box Image-to-Video (I2V) models. The framework optimizes the video…
GUIDE: Governed Unified Intelligence for Document-to-Artifact Generation in Enterprise Settings
The paper introduces GUIDE, a framework designed to automate the creation of enterprise guideline documents. The system uses six specialized agents to parse, extract, check consistency, evaluate, escalate human…
Open Evaluation Agent: Efficient and Promptable Evaluation of Visual Generative Models
Researchers propose a new framework called Evaluation Agent to quickly and efficiently evaluate visual generative models. The agent uses human-like strategies for multi-round evaluations, providing detailed…
A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination
A research paper introduces a new framework called A-SR for symbolic regression. A-SR is designed to discover closed-form equations from data and uses a self-evolving agentic system that coordinates formula discovery…
Falsifiable Commitment Planning for Self-Correcting Web Agents
Researchers propose a new framework called FCPAgent for web agents to ensure they stay on track during long-term planning. The framework represents each plan step as a Falsifiable Commitment Unit (FCU) that includes…
OpenForgeRL: Train Harness-native Agents in Any Environment
OpenForgeRL is an open-source framework designed to train AI agents using complex inference harnesses like Claude Code, Codex, and OpenClaw, in various environments. The framework allows end-to-end training of these…
AI-accelerated End-to-End Framework for Rapid Professional Upskilling
The paper presents an end-to-end AI-accelerated framework designed to improve professional upskilling programs by focusing on five stages: knowledge acquisition, content development, content review and verification…
AI-accelerated End-to-End Framework for Rapid Professional Upskilling
The paper presents an end-to-end AI-accelerated framework designed to improve professional upskilling programs' efficiency by focusing on five stages: knowledge acquisition, content development, content review and…
MM-ToolSandBox: A Unified Framework for Evaluating Visual Tool-Calling Agents
Researchers have introduced MM-ToolSandBox, a tool for evaluating visual agents that can perform tasks by calling tools based on visual inputs. The framework includes over 500 tools across 16 application domains and…
StructAgent: Harness Long-horizon Digital Agents with Unified Causal Structure
In this paper, researchers introduce StructAgent, a framework designed to improve long-horizon digital agents' performance in tasks with evolving contexts and complex workflows. The framework introduces a unified state…
A toy framework for single and multi-agent human-AI curiosity ecosystems
This research paper introduces a simplified framework for understanding curiosity within single and multiple agent systems. The framework considers how an agent decides to ask questions based on factors like immediate…
From Application-Layer Simulation to Native Meta-Architecture: Structural Tension as an Endogenous Driver for Heterogeneous AI Evolution
This research proposes a theoretical framework for integrating application-layer cognitive processes into a native meta-architecture for large language models (LLMs). The framework introduces three mechanisms…
From Application-Layer Simulation to Native Meta-Architecture: Structural Tension as an Endogenous Driver for Heterogeneous AI Evolution
The paper proposes a theoretical framework to integrate higher-order cognitive architectures into a native meta-architecture for large language models (LLMs). This is achieved by introducing three mechanisms…
Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy
Researchers introduce OmniAct, a framework designed for creating persistent embodied agents in unstructured environments. The framework is built on a hierarchical asynchronous architecture that separates planning…
Themis: An explainable AI-enabled framework for Reinforcement Learning with Human Feedback
Researchers present Themis, an AI framework that combines explainability and human feedback to train safe Reinforcement Learning (RL) systems. The framework is designed for over 200 environments, easy configuration for…
