AI BriefingCoding Agent 9-Task Benchmark Released
Key point
Retrieval selection boosted coding agent performance across 9 software tasks.
Details
Released an open-source benchmark called paper-lantern-challenges.
- Measures coding agent performance on 9 everyday software tasks.
- Compares the use of retrieval-augmented technique selection.
- Stated that all prompts, agent code paths, and prediction files are stored and are reproducible.
The experiment was conducted using Claude Opus 4.6 as the planner and Gemini Flash 3 as the task model, with the same input data and the same evaluation scripts. The per-task results showed improvements ranging from +0.010 to +0.320.
The included tasks are as follows.
- Test generation: mutation score
- Text-to-SQL: execution accuracy
- PDF extraction
- Contract extraction
- PR review
- Text classification
- Few-shot prompt selection
- LLM routing
- Summarization evaluation
Each task uses standard datasets and quantitative metrics, and the evaluation method is documented via a per-directory evaluate.py and README.md. The authors suggested that this benchmark can verify which technique coding agents should choose and when, in real-world work.
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