From 431b6e194404f2be7f54805f06b52b887cf3760e Mon Sep 17 00:00:00 2001
From: shadmantabib <90022329+shadmantabib@users.noreply.github.com>
Date: Fri, 23 Jan 2026 03:47:49 +0600
Subject: [PATCH] Revise MoICE reference (Corrected)

---
 content/11.future_trends.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/content/11.future_trends.md b/content/11.future_trends.md
index a352540..6cc6a24 100644
--- a/content/11.future_trends.md
+++ b/content/11.future_trends.md
@@ -69,6 +69,6 @@ Several new architectures exemplify how foundation models advance context-sensit
 
 **LMPriors** (Pre-Trained Language Models as Task-Specific Priors) [@doi:10.48550/arXiv.2210.12530] leverages semantic insights from pre-trained models like GPT-3 to guide tasks such as causal inference, feature selection, and reinforcement learning. This method markedly enhances decision accuracy and efficiency without requiring extensive supervised datasets. However, it necessitates careful prompt engineering to mitigate biases and ethical concerns.
 
-**Mixture of In-Context Experts** (MoICE) [@doi:10.48550/arXiv.2210.12530] introduces a dynamic routing mechanism within attention heads, utilizing multiple Rotary Position Embeddings (RoPE) angles to effectively capture token positions in sequences. MoICE significantly enhances performance on long-context sequences and retrieval-augmented generation tasks by ensuring complete contextual coverage. Efficiency is achieved through selective router training, and interpretability is improved by explicitly visualizing attention distributions, providing detailed insights into the model's reasoning process.
+**Mixture of In-Context Experts** (MoICE) [@doi:10.48550/arXiv.2406.19598] introduces a dynamic routing mechanism within attention heads, utilizing multiple Rotary Position Embeddings (RoPE) angles to effectively capture token positions in sequences. MoICE significantly enhances performance on long-context sequences and retrieval-augmented generation tasks by ensuring complete contextual coverage. Efficiency is achieved through selective router training, and interpretability is improved by explicitly visualizing attention distributions, providing detailed insights into the model's reasoning process.
 
 Collectively, these directions suggest a future in which foundation models evolve from passive representation learners into active, context-sensitive inference engines that unify adaptivity, efficiency, and interpretability within a principled framework.