EMAS: Stabilizing Multi-Agent System Evolution through Evidence-Guided Revision
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Merged summary
TL;DR - EMAS is a framework that lets a multi-agent LLM system keep evolving after deployment, converting execution traces into structured diagnoses that drive validated revisions of MAS topology and prompts without touching model weights. It matters because it turns per-sample experience into reusable system updates, improving accuracy and cutting token cost instead of freezing a design after an initial optimization stage.
- Addresses a gap in automated MAS design: prompts/topologies are usually optimized once and deployed unchanged, so downstream experience is discarded and accuracy-first designs can be token-expensive.
- Revision pipeline is evidence-gated: traces become structured diagnoses naming a revision operation and target; a candidate revision is proposed only when the same diagnosis recurs across samples, and applied only if paired validation against the current MAS passes an acceptance criterion.
- Evaluated on four benchmarks and two LLM backbones; EMAS gets the highest task-weighted overall accuracy for both backbones and is best or tied in six of eight model–benchmark settings.
- Within two evolution epochs, relative task-weighted accuracy gains of 6.30% (Kimi-K2-6) and 20.10% (Qwen3.6-27B); on MBPP with Qwen3.6-27B accuracy rises 55.09% → 89.12% with 62.2% fewer tokens per task.
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EMAS: Stabilizing Multi-Agent System Evolution through Evidence-Guided Revision
TL;DR - EMAS is a framework that lets a multi-agent LLM system keep evolving after deployment, converting execution traces into structured diagnoses that drive validated revisions of MAS topology and prompts without touching model weights. It matters because it turns per-sample experience into reusable system updates, improving accuracy and cutting token cost instead of freezing a design after an initial optimization stage.
- Addresses a gap in automated MAS design: prompts/topologies are usually optimized once and deployed unchanged, so downstream experience is discarded and accuracy-first designs can be token-expensive.
- Revision pipeline is evidence-gated: traces become structured diagnoses naming a revision operation and target; a candidate revision is proposed only when the same diagnosis recurs across samples, and applied only if paired validation against the current MAS passes an acceptance criterion.
- Evaluated on four benchmarks and two LLM backbones; EMAS gets the highest task-weighted overall accuracy for both backbones and is best or tied in six of eight model–benchmark settings.
- Within two evolution epochs, relative task-weighted accuracy gains of 6.30% (Kimi-K2-6) and 20.10% (Qwen3.6-27B); on MBPP with Qwen3.6-27B accuracy rises 55.09% → 89.12% with 62.2% fewer tokens per task.