FlexMoE produces nested pruned subnetworks for MoE LLMs across budgets via channel importance ranking and discrete action learning, plus one mid-budget recovery fine-tune, retaining 99.8% performance at 50% expert parameter pruning.
Evoagent: Towards automatic multi-agent generation via evolutionary algorithms
4 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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Pith papers citing it
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external citations · external index
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Swarm Skills is a portable multi-agent coordination specification with roles, workflows, bounds, and a self-evolution algorithm that distills trajectories using Effectiveness, Utilization, and Freshness scores for zero-adapter portability.
VSRAQ is a MoE-specific quantization objective that combines value and structure alignment to preserve expert-selection behavior and reduce quality loss without inference overhead.
Moderate pruning of MoE models preserves in-domain biomedical utility and reliability but both degrade rapidly in cross-domain settings and at extreme pruning ratios.
citing papers explorer
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FlexMoE: One-for-All Nested Intra-Expert Pruning for MoE Language Models
FlexMoE produces nested pruned subnetworks for MoE LLMs across budgets via channel importance ranking and discrete action learning, plus one mid-budget recovery fine-tune, retaining 99.8% performance at 50% expert parameter pruning.
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Swarm Skills: A Portable, Self-Evolving Multi-Agent System Specification for Coordination Engineering
Swarm Skills is a portable multi-agent coordination specification with roles, workflows, bounds, and a self-evolution algorithm that distills trajectories using Effectiveness, Utilization, and Freshness scores for zero-adapter portability.
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Value-and-Structure Alignment for Routing-Consistent Quantization of Mixture-of-Experts Models
VSRAQ is a MoE-specific quantization objective that combines value and structure alignment to preserve expert-selection behavior and reduce quality loss without inference overhead.
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On the Utility and Factual Reliability of Pruned Mixture-of-Experts Models in the Biomedical Domain
Moderate pruning of MoE models preserves in-domain biomedical utility and reliability but both degrade rapidly in cross-domain settings and at extreme pruning ratios.