pith:56JFTSWG
Towards Iterative End-to-End Software Development: A Feature-Driven Multi-Agent Framework
EvoDev's Feature Map models feature dependencies and propagates context to let LLM agents outperform linear baselines by 56.8 percent on Android tasks.
arxiv:2511.02399 v3 · 2025-11-04 · cs.SE · cs.AI
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Claims
We evaluate EvoDev on challenging Android development tasks and show that it outperforms the best-performing baseline, Claude Code, by a substantial margin of 56.8%, while improving single-agent performance by 16.0%-76.6% across different base LLMs.
That the reported performance gains on Android tasks stem primarily from the Feature Map dependency modeling and context propagation rather than from unstated details in agent prompting, evaluation metrics, or task selection.
EvoDev introduces an iterative feature-driven framework with a DAG-based Feature Map for context propagation that improves LLM agent performance on end-to-end software development tasks by 56.8% over the best baseline.
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| First computed | 2026-06-08T01:03:50.708128Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Canonical record JSON
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