Partial fusion interpolates between neural network ensembles and weight aggregation by only fusing the most similar neurons identified via partial optimal transport, enabling flexible cost-performance tradeoffs.
International Conference on Learning Representations , year =
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Residual Paving decomposes selective refusal editing into an early-layer router for intervention decisions and later-layer residual experts for edits, with oracle routing showing that learned route selectivity is the primary bottleneck across six backbones.
PopuLoRA shows that co-evolving populations of LoRA adapters through cross-evaluated self-play can outperform compute-matched single-agent baselines on multiple code and math reasoning benchmarks.
citing papers explorer
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Partial Fusion of Neural Networks: Efficient Tradeoffs Between Ensembles and Weight Aggregation
Partial fusion interpolates between neural network ensembles and weight aggregation by only fusing the most similar neurons identified via partial optimal transport, enabling flexible cost-performance tradeoffs.
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Residual Paving: Diagnosing the Routing Bottleneck in Selective Refusal Editing
Residual Paving decomposes selective refusal editing into an early-layer router for intervention decisions and later-layer residual experts for edits, with oracle routing showing that learned route selectivity is the primary bottleneck across six backbones.
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PopuLoRA: Co-Evolving LLM Populations for Reasoning Self-Play
PopuLoRA shows that co-evolving populations of LoRA adapters through cross-evaluated self-play can outperform compute-matched single-agent baselines on multiple code and math reasoning benchmarks.