Merging breaks MoE routing via softmax sensitivity; HARC uses Hessian curvature for closed-form router calibration that improves merged model performance without retraining.
Fusechat: Knowl- edge fusion of chat models
5 Pith papers cite this work. Polarity classification is still indexing.
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The Master Key Hypothesis states that capabilities are low-dimensional directions transferable across models through linear subspace alignment, with UNLOCK demonstrating gains such as 12.1% accuracy improvement on MATH when transferring CoT from 14B to 7B models.
HeteroFusion fuses heterogeneous LLMs via topology-based alignment and conflict-aware denoising, outperforming merging and ensemble baselines in cross-family and multi-source settings.
A Dirichlet-prior Bayesian estimator for model success probability replaces Pass@k, delivering faster-converging and more stable rankings with credible intervals on math benchmarks.
InfiGFusion introduces graph-on-logits distillation with an O(n log n) Gromov-Wasserstein approximation to fuse LLMs by modeling token co-activations, reporting gains over baselines on 11 benchmarks.
citing papers explorer
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When Model Merging Breaks Routing: Training-Free Calibration for MoE
Merging breaks MoE routing via softmax sensitivity; HARC uses Hessian curvature for closed-form router calibration that improves merged model performance without retraining.
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The Master Key Hypothesis: Unlocking Cross-Model Capability Transfer via Linear Subspace Alignment
The Master Key Hypothesis states that capabilities are low-dimensional directions transferable across models through linear subspace alignment, with UNLOCK demonstrating gains such as 12.1% accuracy improvement on MATH when transferring CoT from 14B to 7B models.
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Can Heterogeneous Language Models Be Fused?
HeteroFusion fuses heterogeneous LLMs via topology-based alignment and conflict-aware denoising, outperforming merging and ensemble baselines in cross-family and multi-source settings.
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Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation
A Dirichlet-prior Bayesian estimator for model success probability replaces Pass@k, delivering faster-converging and more stable rankings with credible intervals on math benchmarks.
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InfiGFusion: Graph-on-Logits Distillation via Efficient Gromov-Wasserstein for Model Fusion
InfiGFusion introduces graph-on-logits distillation with an O(n log n) Gromov-Wasserstein approximation to fuse LLMs by modeling token co-activations, reporting gains over baselines on 11 benchmarks.