Can Coding Agents Solve Repository-Level Issues with Rendered Code? An Exploratory Study of Visual Representations
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TL;DR - An exploratory study testing whether rendering source code as images (instead of text tokens) can serve as working context for repository-level coding agents on SWE-bench Verified. It matters because visual compression is a proposed way to cut prompt-token cost, and this work maps where that trade-off actually holds in agentic workflows.
- Rendered code reliably lowers prompt-token cost, but savings scale sub-linearly with the nominal visual compression ratio.
- End-to-end repair accuracy is largely preserved, yet rendering does not lift the ceiling set by the underlying model/agent architecture, and becomes unstable under aggressive compression.
- Controlled agent settings separate unguided repository exploration from structured repair stages; visual code helps most when raw source reading is the dominant bottleneck.
- Once localization is structured, remaining cost shifts to patch–test trial-and-error, where visual compression offers little leverage — positioning rendered code as a viable but conditional mechanism.
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Can Coding Agents Solve Repository-Level Issues with Rendered Code? An Exploratory Study of Visual Representations
TL;DR - An exploratory study testing whether rendering source code as images (instead of text tokens) can serve as working context for repository-level coding agents on SWE-bench Verified. It matters because visual compression is a proposed way to cut prompt-token cost, and this work maps where that trade-off actually holds in agentic workflows.
- Rendered code reliably lowers prompt-token cost, but savings scale sub-linearly with the nominal visual compression ratio.
- End-to-end repair accuracy is largely preserved, yet rendering does not lift the ceiling set by the underlying model/agent architecture, and becomes unstable under aggressive compression.
- Controlled agent settings separate unguided repository exploration from structured repair stages; visual code helps most when raw source reading is the dominant bottleneck.
- Once localization is structured, remaining cost shifts to patch–test trial-and-error, where visual compression offers little leverage — positioning rendered code as a viable but conditional mechanism.