The Synthetic Mirror -- Synthetic Data at the Age of Agentic AI
Synthetic data, which is artificially generated and intelligently mimicking or supplementing the real-world data, is increasingly used. The proliferation of AI agents and the adoption of synthetic data create a synthetic mirror that conceptualizes a representation and potential distortion of reality, thus generating trust and accountability deficits. This paper explores the implications for privacy and policymaking stemming from synthetic data generation, and the urgent need for new policy instruments and legal framework adaptation to ensure appropriate levels of trust and accountability for AI agents relying on synthetic data. Rather than creating entirely new policy or legal regimes, the most practical approach involves targeted amendments to existing frameworks, recognizing synthetic data as a distinct regulatory category with unique characteristics.
Code (0)
등록된 구현이 없습니다.
Tasks
Synthetic Data GenerationSimilar Papers 제목 키워드 기반
LiteResearcher: A Scalable Agentic RL Training Framework for Deep Research Agent
Reinforcement Learning (RL) has emerged as a powerful training paradigm for LLM-based agents. However, scaling agentic RL for deep research remains constrained by two coupled challenges: hand-crafted synthetic data fails…
Reinforcement LearningPhysMirror: Physics-Aware Mirror Object Generation
Synthesizing physically accurate mirror reflections remains a fundamental challenge for modern text-to-image diffusion models, which are increasingly critical for generating synthetic training data for embodied AI and ro…
Mirror-3DGS: Incorporating Mirror Reflections into 3D Gaussian Splatting
3D Gaussian Splatting (3DGS) has significantly advanced 3D scene reconstruction and novel view synthesis. However, like Neural Radiance Fields (NeRF), 3DGS struggles with accurately modeling physical reflections, particu…
3DGS3D Scene ReconstructionNeRFNovel View SynthesisReflect3r: Single-View 3D Stereo Reconstruction Aided by Mirror Reflections
Mirror reflections are common in everyday environments and can provide stereo information within a single capture, as the real and reflected virtual views are visible simultaneously. We exploit this property by treating …
3D ReconstructionPose EstimationPoint CloudsVideoThinker: Building Agentic VideoLLMs with LLM-Guided Tool Reasoning
Long-form video understanding remains a fundamental challenge for current Video Large Language Models. Most existing models rely on static reasoning over uniformly sampled frames, which weakens temporal localization and …