EVOCHAMBER: Test-Time Co-evolution of Multi-Agent System at Individual, Team, and Population Scales
Summary
EVOCHAMBER is a training-free, multi-agent test-time evolution framework that enables emergent specialization through collaborative reflection and asymmetric knowledge transfer across individual, team, and population scales, achieving significant improvements on math, code, and reasoning tasks.
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Paper page - EVOCHAMBER: Test-Time Co-evolution of Multi-Agent System at Individual, Team, and Population Scales
Source: https://huggingface.co/papers/2605.11136
Abstract
Multi-agent test-time evolution framework EVOCHAMBER enables emergent specialization through collaborative reflection and asymmetric knowledge transfer across coevolving agents.
We argue thatmulti-agent test-time evolutionis notsingle-agent evolutionreplicated N times. A single-agent learner can only evolve its own context and memory. A multi-agent system additionally evolves who collaborates, how they collaborate, and how knowledge flows across the population. These components have no single-agent counterpart and can produce phenomena such asemergent specialization. Yet prior test-time methods either confine experiences to individual agents, forfeiting cross-agent learning, or broadcast symmetrically to all agents, erasing the specialization that makes collaboration valuable. We present EVOCHAMBER, a training-free framework that instantiates test-time evolution at three levels over acoevolving agent pool. At its core isCODREAM(Collaborative Dreaming), a post-task protocol triggered on team failure or disagreement, in which agents collaboratively reflect, distill insights, and route them asymmetrically from strong to weak agents on the failed niche, preserving specialization while filling knowledge gaps. Team-level operators assembleniche-conditioned teamsand selectcollaboration structuresonline.Population-level lifecycle operatorsfork, merge, prune, and seed agents under performance pressure. On three heterogeneous task streams with Qwen3-8B, EVOCHAMBER reaches 63.9% on competition math, 75.7% on code, and 87.1% on multi-domain reasoning, outperforming the best baseline by 32% relative on math and confirming asymmetric cross-agent transfer as the primary driver in ablation. Starting from several identically initialized agents, four to five stable niche specialists spontaneously emerge, a structural signature of multi-agent evolution that no single-agent learner can express. See our code at: https://github.com/Mercury7353/EvoChamber
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