# Output Format x Model Identity: Interaction Effects in Single-Round Coding Agent Performance

arXiv: https://arxiv.org/abs/2607.21674 · HTML: https://mlsanity.com/paper/2607.21674/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 23 Jul 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 10 of 10)

- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code](https://mlsanity.com/paper/2403.07974/) (2024)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing](https://mlsanity.com/paper/2305.11738/) (2023)
- [Agentless: Demystifying LLM-based Software Engineering Agents](https://mlsanity.com/paper/2407.01489/) (2024)
- [Efficient Guided Generation for Large Language Models](https://mlsanity.com/paper/2307.09702/) (2023)
- [Where LLM Agents Fail and How They can Learn From Failures](https://mlsanity.com/paper/2509.25370/) (2025)
- [Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering](https://mlsanity.com/paper/2604.08224/) (2026)
- [Inference Economics of Enterprise Coding Agents: A Case Study of Cloud vs. On-Premise LLMs](https://mlsanity.com/paper/2607.13080/) (2026)

## Abstract (arXiv)

Output format is not a neutral implementation detail -- it can reorder model rankings, amplify or suppress individual model differences, and determine whether a coding agent succeeds or fails. We conducted a controlled single-round experiment with 3 models (DeepSeek V4, Doubao 2.0 Pro, Qwen 3.7 Max) x 3 output formats (full file, JSON Patch, unified diff) x 6 tasks x 20 repetitions, totaling 4,013 runs across 4 open-source projects. Only one project (tqdm) yielded non-zero success rates: dotenv, requests, and jsoup yielded zero successes in 2,551 runs. Our central finding is a format x model interaction with no universally optimal format. Doubao achieves 94% success with JSON Patch (Cohen's h = 1.57, p < 0.001), DeepSeek excels at unified diff (66%, h = 0.63), and Qwen shows a small but significant full-file preference (50%, h = 0.29, p < 0.05). Beyond these headline results, we identify a distinct failure mechanism -- format misuse -- where agents correctly diagnose a problem but execute it with excessive scope, most vividly when a one-line fix is applied as a full-file replacement. We propose a model-specific output strategy, a tool-design principle that constrains format semantics to the agent's own localization step, and release all data and templates for reproducibility. All experimental data are available at https://doi.org/10.5281/zenodo.21505157.