Agents
Closing the loop: agentic evaluation for image editing foundation models
Why evaluating image editing models is both critical and challenging Instruction-based image editing is becoming a core capability of multimodal foundation models. Users can increasingly edit images s
Why evaluating image editing models is both critical and challenging Instruction-based image editing is becoming a core capability of multimodal foundation models. Users can increasingly edit images simply by describing what they want: “remove the person in the background,” “make the car red,” or “move the chair next to the table.”
Related
- FAGER: Factually Grounded Evaluation and Refinement of Text-to-Image Models
- Efficient Agent Evaluation via Diversity-Guided User Simulation
- Capture the Flags: Family-Based Evaluation of Agentic LLMs via Semantics-Preserving Transformations
Source: Lambda Labs | 2026-09-17