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arxiv: 2605.15181 · v1 · submitted 2026-05-14 · 💻 cs.CV

Recognition: 2 theorem links

· Lean Theorem

From Plans to Pixels: Learning to Plan and Orchestrate for Open-Ended Image Editing

Anirudh Sundara Rajan , Krishna Kumar Singh , Yong Jae Lee
Authors on Pith no claims yet

Pith reviewed 2026-05-15 03:17 UTC · model grok-4.3

classification 💻 cs.CV
keywords image editingmulti-step planningorchestrationvision-language modelsoutcome rewardslong-horizon tasksexperiential learning
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The pith

Coupling a planner with a reward-driven orchestrator enables reliable multi-step image editing from abstract instructions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a framework that decomposes abstract editing instructions into atomic steps via a planner and then executes those steps through an orchestrator that selects tools and regions. A vision-language judge supplies outcome-based rewards that measure instruction adherence and visual quality, allowing the orchestrator to be trained directly on editing results. Successful trajectories are fed back to refine the planner, creating a closed loop between planning and execution. This matters because existing models and handcrafted pipelines often produce incoherent results on open-ended tasks such as making an advertisement vegetarian-friendly. The approach therefore aims to move beyond imitation learning or fixed pipelines toward outcome-driven improvement.

Core claim

The central claim is that an experiential loop in which a planner produces structured atomic decompositions and an orchestrator selects and applies tools and regions, guided by rewards from a vision-language judge, yields more coherent and reliable edits than single-step models or rule-based multi-step baselines. The orchestrator is trained to maximize the judge's rewards on instruction adherence and visual quality, while high-reward trajectories are used to update the planner. This tight coupling of planning with reward-driven execution is what distinguishes the method from prior agent-based approaches that rely on handcrafted pipelines or teacher imitation.

What carries the argument

The experiential framework of a planner that generates atomic decompositions coupled with an orchestrator trained to maximize vision-language judge rewards for tool and region selection.

If this is right

  • The method produces more coherent results on long-horizon, abstract instructions than single-step or rule-based approaches.
  • Learning occurs directly from editing outcomes rather than from imitation of expert trajectories.
  • Successful execution trajectories can be reused to iteratively improve the planner.
  • The orchestrator learns to choose appropriate tools and spatial regions conditioned on the current state and step.
  • The overall system handles open-ended tasks that require multiple coordinated changes without handcrafted decomposition rules.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same reward-driven loop could be tested on sequential tasks outside image editing, such as code editing or scene graph manipulation, if a suitable judge is available.
  • If the judge generalizes across domains, the framework might reduce reliance on large amounts of human demonstration data for agent training.
  • Extending the planner to output probabilistic decompositions rather than fixed sequences could improve robustness when early steps have multiple valid paths.

Load-bearing premise

A vision-language judge can reliably score both instruction adherence and visual quality across diverse editing tasks without systematic errors or biases.

What would settle it

Human ratings of the method's outputs showing lower coherence or lower instruction adherence than single-step baselines on the same set of abstract multi-step tasks.

Figures

Figures reproduced from arXiv: 2605.15181 by Anirudh Sundara Rajan, Krishna Kumar Singh, Yong Jae Lee.

Figure 1
Figure 1. Figure 1: Given a high-level instruction such as “Adapt for a rural audience,” single-step editors (e.g., Flux Kontext [19] and Qwen-Image-Edit [47]) strug￾gle to jointly adapt visual themes, textual content, and audience-specific con￾text while preserving the original advertisement layout and identity. In contrast, our framework decomposes the task into structured subtasks and orchestrates multiple tools using outc… view at source ↗
Figure 2
Figure 2. Figure 2: Checklist-guided planner (Stage 1). Given an input advertisement and a high-level instruction (left, e.g., “Target business travelers with added corporate benefits”), the planner generates a structured, ordered sequence of subtasks (center) that explicitly address checklist items (right). In this exam￾ple, the generated plan includes adapting the room aesthetics for a professional audience, adding work-rel… view at source ↗
Figure 3
Figure 3. Figure 3: Reward-driven orchestration (Stage 2). Given an input image and a high-level instruction (e.g., “Target business travelers with added corporate benefits”), the orchestrator selects appropriate tools and spatial regions to ex￾ecute a specific subtask (e.g., replacing the red pillows with neutral-toned pro￾fessional decor pillows). The edited result is then evaluated by a VLM judge, which assesses instructio… view at source ↗
Figure 4
Figure 4. Figure 4: User study (random￾ized A/B testing). Results show a consistent human pref￾erence for our approach. User Studies To corroborate the MLLM judge results, we conduct a user study using ran￾domized A/B testing. Participants are shown paired results in random order and asked to select their preferred edit or indicate a tie, while accounting for instruction follow￾ing, identity preservation, and visual quality. … view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative results on diverse long-horizon advertisement edit￾ing tasks. These examples show two challenging instructions—adapting for Business travelers, and for American Independence Day. Single-step editors (Flux Kontext [19] and Qwen-Image-Edit [47]) often perform partial stylistic changes or introduce minimal or shallow modifications in text, layout, or branding. In con￾trast, our method consistently… view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative results on diverse long-horizon advertisement edit￾ing tasks. These examples show three challenging instructions—adapting for Lunar New Year, western audience, and for fitness-conscious audience. Our method consistently produces edits that are faithful to the instruction and glob￾ally coherent, jointly updating visual themes, textual content, layout elements, and brand messaging. Note that we b… view at source ↗
Figure 7
Figure 7. Figure 7: Planner–orchestrator subtasks and their outputs. Each row be￾gins with the input advertisement (first column) and a high-level instruction. The subsequent columns show the sequence of subtask plans and edits produced by our system. These results illustrate how our checklist-guided planning decom￾poses an abstract instruction into concrete atomic edits and how our orchestrator selects tool-regions to execut… view at source ↗
Figure 8
Figure 8. Figure 8: Examples from the MagicBrush benchmark. Each example shows the [PITH_FULL_IMAGE:figures/full_fig_p025_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Region discovery using the SAM-2 + Qwen3-VL pipeline. SAM-2 first [PITH_FULL_IMAGE:figures/full_fig_p030_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: DeepSeek-OCR identifies textual and layout elements in the image by [PITH_FULL_IMAGE:figures/full_fig_p031_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Layer-based region discovery using Qwen-Layered. [PITH_FULL_IMAGE:figures/full_fig_p032_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Instruction-guided region discovery using Qwen-BBox. [PITH_FULL_IMAGE:figures/full_fig_p032_12.png] view at source ↗
read the original abstract

Modern image editing models produce realistic results but struggle with abstract, multi step instructions (e.g., ``make this advertisement more vegetarian-friendly''). Prior agent based methods decompose such tasks but rely on handcrafted pipelines or teacher imitation, limiting flexibility and decoupling learning from actual editing outcomes. We propose an experiential framework for long-horizon image editing, where a planner generates structured atomic decompositions and an orchestrator selects tools and regions to execute each step. A vision language judge provides outcome-based rewards for instruction adherence and visual quality. The orchestrator is trained to maximize these rewards, and successful trajectories are used to refine the planner. By tightly coupling planning with reward driven execution, our approach yields more coherent and reliable edits than single-step or rule-based multistep baselines.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes an experiential framework for open-ended image editing consisting of a planner that decomposes abstract multi-step instructions into atomic steps and an orchestrator that selects tools and regions for execution. A vision-language model judge supplies outcome-based scalar rewards for instruction adherence and visual quality; these rewards train the orchestrator via reinforcement, after which successful trajectories refine the planner. The central claim is that this tight coupling of planning and reward-driven execution produces more coherent and reliable results than single-step models or rule-based multi-step baselines.

Significance. If the empirical claims are substantiated, the work would be significant for agent-based image editing: it moves beyond handcrafted pipelines and imitation learning by grounding both planning and orchestration directly in editing outcomes via a learned reward signal. This could enable more flexible handling of abstract instructions while providing a reproducible training loop that prior methods lack.

major comments (2)
  1. [Experiments] The abstract and method description assert that the approach 'yields more coherent and reliable edits than single-step or rule-based multistep baselines,' yet the manuscript contains no experimental results, quantitative metrics, ablation studies, or implementation details. This absence is load-bearing because the superiority claim cannot be evaluated without evidence that the reward-driven training actually improves coherence.
  2. [Method] Section 3.3 (VLM judge): The central training procedure relies on the vision-language judge supplying accurate, consistent rewards for both instruction adherence and visual quality, but no validation is provided (e.g., human correlation, inter-rater agreement, or error analysis on edge cases). If judge scores systematically misalign with human preference, the learned policy optimizes a noisy objective, directly undermining the coherence advantage.
minor comments (2)
  1. [Method] Clarify the exact interface between planner output and orchestrator input, including the format of atomic decompositions.
  2. [Introduction] Add missing references to recent VLM-as-judge literature and prior agent-based editing systems for proper positioning.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive feedback. We agree that the current manuscript lacks the necessary experimental evidence and judge validation to support the central claims, and we will revise accordingly to include these elements.

read point-by-point responses

  1. Referee: [Experiments] The abstract and method description assert that the approach 'yields more coherent and reliable edits than single-step or rule-based multistep baselines,' yet the manuscript contains no experimental results, quantitative metrics, ablation studies, or implementation details. This absence is load-bearing because the superiority claim cannot be evaluated without evidence that the reward-driven training actually improves coherence.

    Authors: We acknowledge that the submitted manuscript does not contain an Experiments section with quantitative results, metrics, ablations, or implementation details. This omission prevents direct evaluation of the superiority claims. In the revised version we will add a full Experiments section reporting comparisons to single-step models and rule-based multi-step baselines on instruction adherence, visual quality, and human preference metrics, together with ablations on the planner-orchestrator interaction and reward-driven training, plus all necessary implementation details. revision: yes

  2. Referee: [Method] Section 3.3 (VLM judge): The central training procedure relies on the vision-language judge supplying accurate, consistent rewards for both instruction adherence and visual quality, but no validation is provided (e.g., human correlation, inter-rater agreement, or error analysis on edge cases). If judge scores systematically misalign with human preference, the learned policy optimizes a noisy objective, directly undermining the coherence advantage.

    Authors: We agree that the absence of validation for the VLM judge is a critical gap. In the revised manuscript we will add a dedicated validation subsection that reports correlation between VLM judge scores and human ratings on a held-out set of edited images, inter-rater agreement statistics, and an error analysis covering edge cases such as ambiguous instructions and subtle visual modifications. This will demonstrate the reliability of the reward signal. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the experiential training framework

full rationale

The paper presents a training loop in which an external vision-language judge supplies outcome-based scalar rewards that are used to optimize the orchestrator and to filter trajectories for planner refinement. This structure treats the judge as an independent source of supervision rather than defining any quantity (such as reward or success) in terms of the model's own outputs. No equations or procedures are shown that reduce the claimed performance gain to a fitted parameter or a self-referential definition; the superiority over baselines is asserted as an empirical result of the reward-driven process. No self-citations function as load-bearing uniqueness theorems, and no ansatz or known empirical pattern is smuggled in via citation. The derivation chain therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides insufficient detail to enumerate specific free parameters, axioms, or invented entities; the approach appears to build on existing vision-language models and planning techniques without introducing new postulated entities.

pith-pipeline@v0.9.0 · 5431 in / 1166 out tokens · 59674 ms · 2026-05-15T03:17:29.103673+00:00 · methodology

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Reference graph

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