AdaFuse: Adaptive Ensemble Decoding with Test-Time Scaling for LLMs
Quick summary
arXiv:2601.06022v2 Announce Type: replace-cross Abstract: Large language models (LLMs) exhibit complementary strengths arising from differences in pretraining data, model architectures, and decoding behaviors. Inference-time ensembling provides a practical way to combine these capabilities without retraining. However, existing ensemble approaches suffer from fundamental limitations. Most rely on fixed fusion granularity, which lacks the flexibility required for mid-generation adaptation and fails to adapt to different generation characteristics across tasks. To address these challenges, we pro
Key takeaways
- arXiv:2601.06022v2 Announce Type: replace-cross Abstract: Large language models (LLMs) exhibit complementary strengths arising from differences in pretraining data, model architectures, and decoding behaviors.
- Inference-time ensembling provides a practical way to combine these capabilities without retraining.
- However, existing ensemble approaches suffer from fundamental limitations.
Why it matters
“AdaFuse: Adaptive Ensemble Decoding with Test-Time Scaling for LLMs” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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