COUNTLOOP: Iterative Agent Guided High Instance Image Generation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Diffusion models have achieved remarkable progress in photorealistic synthesis, yet they remain unreliable for generating scenes with a precise number of object instances, especially in complex, high-density settings. We introduce CountLoop, a training-free framework that equips diffusion models with accurate instance control via iterative structured feedback. It alternates between image synthesis and multimodal agent evaluation: an LLM-guided layout planner and critic provide explicit feedback on object counts, spatial arrangements, and attribute consistency, which is used to refine scene layouts and guide subsequent generations. Instance-driven attention masking and compositional techniques further prevent semantic leakage, enabling clear separation of individual objects even in occluded scenes. Evaluations on COCO-Count, T2I-CompBench, and two newly introduced high-instance benchmarks demonstrate that CountLoop surpasses existing benchmarks by achieving a counting accuracy of as much as 98% while consistently acing spatial arrangement and visual quality over existing layout and gradient‑guided baselines with a score of 0.97.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it