EVOCHAMBER enables test-time co-evolution of multi-agent systems across three scales, producing emergent niche specialists and performance gains of up to 32% relative on math tasks with Qwen3-8B.
ScoreFlow: Mastering LLM agent workflows via score-based preference optimization
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.AI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Autogenesis Protocol defines resource and evolution layers for LLM agents, enabling a system that shows performance gains on long-horizon planning benchmarks.
citing papers explorer
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EVOCHAMBER: Test-Time Co-evolution of Multi-Agent System at Individual, Team, and Population Scales
EVOCHAMBER enables test-time co-evolution of multi-agent systems across three scales, producing emergent niche specialists and performance gains of up to 32% relative on math tasks with Qwen3-8B.
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Autogenesis: A Self-Evolving Agent Protocol
Autogenesis Protocol defines resource and evolution layers for LLM agents, enabling a system that shows performance gains on long-horizon planning benchmarks.