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ensemble pruning

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Sep 30, 2026 · Artificial Intelligence

ME-Decoding: Reframing LLM Decoding as Ensemble Pruning with Semantic Redundancy Control

Renmin University researchers propose ME-Decoding, a method that selects candidate tokens by jointly optimizing probability confidence and semantic diversity via Mahalanobis distance, outperforming probability-only truncation on reasoning and open-ended generation across multiple models and temperatures with only 3% latency overhead.

EMNLP 2026LLM decodingMahalanobis distance

0 likes · 11 min read

ME-Decoding: Reframing LLM Decoding as Ensemble Pruning with Semantic Redundancy Control

Machine Heart

May 22, 2026 · Artificial Intelligence

Breaking the Echo Chamber: MP‑MoE Introduces Ensemble‑Pruning for Diverse Experts

The paper presents MP‑MoE, a new Mixture‑of‑Experts architecture that replaces top‑k routing with Mahalanobis‑based ensemble pruning, explicitly encouraging expert diversity via a co‑occurrence matrix, and uses an efficient greedy algorithm with incremental Cholesky updates, achieving higher performance with minimal training overhead and no inference cost.

Expert DiversityICML 2026MP-MoE

0 likes · 8 min read

Breaking the Echo Chamber: MP‑MoE Introduces Ensemble‑Pruning for Diverse Experts