How Multi‑Agent MCTS and Information‑Gain Rewards Are Transforming Mobile GUI and Search Agents

This article reviews two recent ICLR 2026 papers—M²‑Miner, a multi‑agent Monte‑Carlo Tree Search framework for low‑cost mobile GUI data mining, and IGPO, an information‑gain‑based reinforcement‑learning method that provides dense rewards for multi‑turn search agents—detailing their designs, experiments, and open‑source releases.

GUI Data MiningInformation GainLLM agents

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How Multi‑Agent MCTS and Information‑Gain Rewards Are Transforming Mobile GUI and Search Agents

Model Perspective

Sep 7, 2024 · Fundamentals

How Choices Reduce Uncertainty: The Hidden Role of Information Entropy

Every daily decision—from picking clothes to selecting a menu item—acts as an entropy‑reducing process, and this article explains how information theory’s concepts of entropy, entropy reduction, and information gain illuminate the nature of choice, free will, and optimal decision‑making.

Information Gainchoice optimizationdecision making

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How Choices Reduce Uncertainty: The Hidden Role of Information Entropy

Model Perspective

Sep 12, 2023 · Fundamentals

What Is Information Entropy? Definition, Calculation, and Decision Tree Applications

This article explains the concept of information entropy, its mathematical definition, how to compute it for simple events like coin flips and dice rolls, and demonstrates its role in decision‑tree learning by calculating information gain for a watermelon dataset.

Information Gaindecision treeinformation entropy

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What Is Information Entropy? Definition, Calculation, and Decision Tree Applications

Model Perspective

Jun 19, 2022 · Artificial Intelligence

How Decision Trees Work: From Entropy to Gini Index Explained

This article introduces decision tree algorithms, explains their role in supervised learning for classification and regression, details the construction process, compares information gain and Gini index for attribute selection, and reviews popular tree methods such as ID3, C4.5, and CART with illustrative examples.

C4.5CARTGini Index

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How Decision Trees Work: From Entropy to Gini Index Explained

Model Perspective

Jun 13, 2022 · Artificial Intelligence

Understanding Decision Trees: From Basic Process to Watermelon Example

This article explains the fundamentals of decision tree learning, describing its recursive construction, the criteria for splitting nodes using information gain based on entropy, and walks through a classic watermelon dataset example to illustrate how attributes are selected and the final tree is built.

ID3 algorithmInformation Gainclassification

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Understanding Decision Trees: From Basic Process to Watermelon Example

Tencent Cloud Developer

Mar 19, 2018 · Artificial Intelligence

Basic Concepts of Decision Trees

Decision trees are tree-structured classifiers that split data using attributes chosen for maximal purity measured by Gini impurity or entropy, with algorithms like ID3 selecting splits by information gain, while overfitting is mitigated through constraints and pruning techniques such as REP, PEP, and CCP.

Gini ImpurityID3Information Gain

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