# What Can I Edit? Open-Ended Strategy Discovery and the Emotion Editability Landscape

arXiv: https://arxiv.org/abs/2607.23920 · HTML: https://mlsanity.com/paper/2607.23920/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 48 references that are in the map.
- First posted on arXiv 27 Jul 2026.

## Builds on (most cited first, 30 of 37)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [GenArtist: Multimodal LLM as an Agent for Unified Image Generation and Editing](https://mlsanity.com/paper/2407.05600/) (2024)
- [ArtEmis: Affective Language for Visual Art](https://mlsanity.com/paper/2101.07396/) (2021)
- [EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes](https://mlsanity.com/paper/2307.07961/) (2023)
- [Language-Driven Image Style Transfer](https://mlsanity.com/paper/2106.00178/) (2021)
- [EmoGen: Emotional Image Content Generation with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2401.04608/) (2024)
- [Learning Multi-level Deep Representations for Image Emotion Classification](https://mlsanity.com/paper/1611.07145/) (2016)
- [EmoEdit: Evoking Emotions through Image Manipulation](https://mlsanity.com/paper/2405.12661/) (2024)
- [EmotiCrafter: Text-to-Emotional-Image Generation based on Valence-Arousal Model](https://mlsanity.com/paper/2501.05710/) (2025)
- [MIRA: Multimodal Iterative Reasoning Agent for Image Editing](https://mlsanity.com/paper/2511.21087/) (2025)
- [Make Me Happier: Evoking Emotions Through Image Diffusion Models](https://mlsanity.com/paper/2403.08255/) (2024)
- [Affective Image Editing: Shaping Emotional Factors via Text Descriptions](https://mlsanity.com/paper/2505.18699/) (2025)
- [A Unified and Interpretable Emotion Representation and Expression Generation](https://mlsanity.com/paper/2404.01243/) (2024)
- [CoEmoGen: Towards Semantically-Coherent and Scalable Emotional Image Content Generation](https://mlsanity.com/paper/2508.03535/) (2025)
- [Empowering Visual Creativity: A Vision-Language Assistant to Image Editing Recommendations](https://mlsanity.com/paper/2406.00121/) (2024)
- [DreamSteerer: Enhancing Source Image Conditioned Editability using Personalized Diffusion Models](https://mlsanity.com/paper/2410.11208/) (2024)
- [Early Timestep Zero-Shot Candidate Selection for Instruction-Guided Image Editing](https://mlsanity.com/paper/2504.13490/) (2025)
- [EmoFeedback2: Reinforcement of Continuous Emotional Image Generation via LVLM-based Reward and Textual Feedback](https://mlsanity.com/paper/2511.19982/) (2025)
- [GraPE: A Generate-Plan-Edit Framework for Compositional T2I Synthesis](https://mlsanity.com/paper/2412.06089/) (2024)
- [EmoKGEdit: Training-free Affective Injection via Visual Cue Transformation](https://mlsanity.com/paper/2601.12326/) (2026)
- [Beyond Pixels: Visual Metaphor Transfer via Schema-Driven Agentic Reasoning](https://mlsanity.com/paper/2602.01335/) (2026)
- [IMAGAgent: Orchestrating Multi-Turn Image Editing via Constraint-Aware Planning and Reflection](https://mlsanity.com/paper/2603.29602/) (2026)
- [EmoAgent: A Multi-Agent Framework for Diverse Affective Image Manipulation](https://mlsanity.com/paper/2503.11290/) (2025)
- [MUSE: Manipulating Unified Framework for Synthesizing Emotions in Images via Test-Time Optimization](https://mlsanity.com/paper/2511.21051/) (2025)
- [EmoStyle: Emotion-Driven Image Stylization](https://mlsanity.com/paper/2512.05478/) (2025)
- [Emotion-Director: Bridging Affective Shortcut in Emotion-Oriented Image Generation](https://mlsanity.com/paper/2512.19479/) (2025)

## Built on by (most cited first, 2 of 2)

- [Agentic Visual Generation: From Generative Models to Agentic Control](https://mlsanity.com/paper/2609.06758/) (2026)
- [What We Talk About When We Talk About Emotion Editing](https://mlsanity.com/paper/2610.13558/) (2026)

## Abstract (arXiv)

Emotional image editing requires more than applying affective filters or modifying predefined visual factors: an effective edit must identify what a particular image can afford for a target emotion. Existing affective image manipulation methods, including recent agentic variants, largely operate within bounded strategy spaces based on predefined factor taxonomies, knowledge libraries, or conventional editing templates, and therefore often miss image-specific, context-grounded strategies. We introduce EmoScope, a multi-agent framework that reframes the task from "how should I edit?" to "what can I edit?" EmoScope first discovers an image-specific editable space through emotion-conditioned affordance reasoning, then uses a semantic hierarchy of anchors, variables, and context to balance content consistency and emotional expressiveness before executing and verifying the edit. Because its plans are expressed as image-specific affordances rather than retrieved templates, EmoScope also exposes the editing strategy as an interactive surface for user refinement at the plan level. In a large-scale human evaluation covering all eight Mikels emotion categories, with 4,693 valid responses across 1,824 pairwise questions, participants preferred EmoScope over two competitive baselines by 88.1% on average. Attribution analysis further shows that EmoScope selects target-emotion-adaptive strategies rather than applying a uniform template. The same affordance-level plan also supports lightweight user refinement in an interactive pilot. Finally, we show that classifier-based metrics exhibit emotion-conditional blind spots toward non-stereotypical, context-grounded edits, and present a relative content-emotion preference-affinity landscape showing that EmoScope's advantage varies systematically across image-emotion combinations.