RESEARCH · RESEARCH · #2913
Adobe Research and UC San Diego introduce EditAgent, an AI that chooses specialized editing tools and verifies edits
EditAgent, from Adobe Research and UC San Diego, decomposes image-editing requests into steps, pits a general-purpose editor against specialized tools for each step, and uses a standard language model fed verification records (close-ups, change maps, and measured statistics) to pick the best variant. On a 600-edit benchmark it outperformed prior agent judges and improved several open editors, but needs roughly ten times the compute of a single editor; the verified outputs also produced useful training data.
KEY POINTS
- EditAgent, from Adobe Research and UC San Diego, decomposes image-editing requests into steps, pits a general-purpose editor against specialized tools for each step, and uses a standard language model fed verification records (close-ups, change maps, and measured statistics) to pick the best variant.
- On a 600-edit benchmark it outperformed prior agent judges and improved several open editors, but needs roughly ten times the compute of a single editor; the verified outputs also produced useful training data.
- This shows a practical agent design—tool selection plus numeric verification—that improves edit quality and can seed better training data, with direct implications for production apps like Photoshop.
WHY IT MATTERS
This shows a practical agent design—tool selection plus numeric verification—that improves edit quality and can seed better training data, with direct implications for production apps like Photoshop.