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pith:2025:56JFTSWGPGAHVUGF44HXLKF23G
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Towards Iterative End-to-End Software Development: A Feature-Driven Multi-Agent Framework

Chen Xu, Chong Wang, Junwei Liu, Kaseng Wong, Tong Bai, Weitong Chen, Xin Peng, Yiling Lou

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

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

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
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Schema pith-number/v1.0

Canonical hash

ef9259cac679807ad0c5e70f75a8bad9a6e51da699e04f26aaceab94bab4db3e

Aliases

arxiv: 2511.02399 · arxiv_version: 2511.02399v3 · doi: 10.48550/arxiv.2511.02399 · pith_short_12: 56JFTSWGPGAH · pith_short_16: 56JFTSWGPGAHVUGF · pith_short_8: 56JFTSWG
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/56JFTSWGPGAHVUGF44HXLKF23G \
  | jq -c '.canonical_record' \
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Canonical record JSON
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