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世界中で発行される人工知能関連の論文をまとめるページです。
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Merge-of-Thought Distillation

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Symmetry-Guided Multi-Agent Inverse Reinforcement Learning

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Expert-Guided Explainable Few-Shot Learning for Medical Image Diagnosis

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MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining

Demo: Healthcare Agent Orchestrator (HAO) for Patient Summarization in Molecular Tumor Boards

Focusing by Contrastive Attention: Enhancing VLMs' Visual Reasoning

Beyond the Pre-Service Horizon: Infusing In-Service Behavior for Improved Financial Risk Forecasting

On Synthesis of Timed Regular Expressions

TinyDef-DETR: A DETR-based Framework for Defect Detection in Transmission Lines from UAV Imagery

LiDAR-BIND-T: Improved and Temporally Consistent Sensor Modality Translation and Fusion for Robotic Applications

From Vision to Validation: A Theory- and Data-Driven Construction of a GCC-Specific AI Adoption Index

A Comprehensive Guide to Differential Privacy: From Theory to User Expectations

The Architecture of AI Transformation: Four Strategic Patterns and an Emerging Frontier

FLM-Audio: Natural Monologues Improves Native Full-Duplex Chatbots via Dual Training

Deep Learning-Based Rock Particulate Classification Using Attention-Enhanced ConvNeXt

The Information Dynamics of Generative Diffusion

Data-Augmented Few-Shot Neural Stencil Emulation for System Identification of Computer Models

Group Expectation Policy Optimization for Heterogeneous Reinforcement Learning

Pretrained Conformers for Audio Fingerprinting and Retrieval

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To Theoretically Understand Transformer-Based In-Context Learning for Optimizing CSMA

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HiD-VAE: Interpretable Generative Recommendation via Hierarchical and Disentangled Semantic IDs

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New Kid in the Classroom: Exploring Student Perceptions of AI Coding Assistants

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Uncertainty-aware Diffusion and Reinforcement Learning for Joint Plane Localization and Anomaly Diagnosis in 3D Ultrasound

Uncertainty Estimation by Human Perception versus Neural Models

Persistent Homology of Topic Networks for the Prediction of Reader Curiosity

Task Matters: Knowledge Requirements Shape LLM Responses to Context-Memory Conflict

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MM-Prompt: Cross-Modal Prompt Tuning for Continual Visual Question Answering

An Ontology-Driven Graph RAG for Legal Norms: A Structural, Temporal, and Deterministic Approach

Combating Falsification of Speech Videos with Live Optical Signatures (Extended Version)

Early Exit and Multi Stage Knowledge Distillation in VLMs for Video Summarization

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SWI: Speaking with Intent in Large Language Models

Byzantine-Robust Federated Learning Using Generative Adversarial Networks

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Rethinking Disentanglement under Dependent Factors of Variation

Discovering physical laws with parallel symbolic enumeration

Semantic Augmentation in Images using Language

Algorithmic Collusion by Large Language Models

A minimal coalition logic

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TreeGPT: Pure TreeFFN Encoder-Decoder Architecture for Structured Reasoning Without Attention Mechanisms

Robix: A Unified Model for Robot Interaction, Reasoning and Planning

KROMA: Ontology Matching with Knowledge Retrieval and Large Language Models

Scaling LLM Planning: NL2FLOW for Parametric Problem Generation and Rigorous Evaluation

Optimizing Length Compression in Large Reasoning Models

LLMs for sensory-motor control: Combining in-context and iterative learning

Effort-aware Fairness: Incorporating a Philosophy-informed, Human-centered Notion of Effort into Algorithmic Fairness Metrics

Simulating Human-like Daily Activities with Desire-driven Autonomy

Enhancing Few-Shot Transfer Learning with Optimized Multi-Task Prompt Tuning through Modular Prompt Composition

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SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning

Feasibility-Guided Fair Adaptive Offline Reinforcement Learning for Medicaid Care Management

Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulations

Explaining Concept Drift through the Evolution of Group Counterfactuals

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Graph Alignment via Dual-Pass Spectral Encoding and Latent Space Communication

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A modified RIME algorithm with covariance learning and diversity enhancement for numerical optimization

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Prompt Pirates Need a Map: Stealing Seeds helps Stealing Prompts

Resource-Efficient Glioma Segmentation on Sub-Saharan MRI

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LLMs Don't Know Their Own Decision Boundaries: The Unreliability of Self-Generated Counterfactual Explanations

MetaLLMix : An XAI Aided LLM-Meta-learning Based Approach for Hyper-parameters Optimization

Merge-of-Thought Distillation

Created by

Haebom

作者

Zhanming Shen, Zeyu Qin, Zenan Huang, Hao Chen, Jiaqi Hu, Yihong Zhuang, Guoshan Lu, Gang Chen, Junbo Zhao

概要

本論文では、複数の教師モデルを活用して長距離思考プロセス（CoT）モデルの推論能力を効率的に蒸留する新しい方法であるMerge-of-Thought Distillation（MoT）を提案します。既存の単一の教師モデルに依存する蒸留方式の限界を克服するために、MoTは複数の教師モデルの地図を統合して学生モデルを学習します。各教師モデルごとに生徒モデルを fine-tuning し、その結果を重み空間でマージする過程を繰り返し行います。競争数学のベンチマークでは、少量の高品質CoTサンプルのみを使用してQwen3-14B学生モデルにMoTを適用した結果、DEEPSEEK-R1、QWEN3-30B-A3B、QWEN3-32B、OPENAI-O1などの強力なモデルを凌駕する性能を示しました。 MoTは、単一の教師の蒸留と単純なマルチ教師の統合方式よりも優れた性能を示し、過適合を軽減し、分布の移動と同等のレベルの教師モデルにも堅牢であることを示しています。さらに、カタストロフィックフォージングを減らし、数学領域を超えて一般的な推論能力を向上させ、より良い教師モデルを培養する効果も見られます。これらの結果は,MoTが,様々な教師モデルから効率的に長距離CoT能力を小型学生モデルに蒸留する簡単で拡張可能な方法であることを示した。

Takeaways、Limitations

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Takeaways:

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複数の教師モデルを活用して長距離事故過程(CoT)モデルの推論能力を効率的に蒸留する新しい方法の提示

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少量の高品質データでも優れた性能を実現

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従来のシングル教師蒸留とマルチ教師統合方式より優れた性能と堅牢性を確保

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Catastrophic forgettingの低減と一般推論能力の向上

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より良い先生モデルの培養可能性を提示

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Limitations：

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本論文で提示された実験結果は主に競争数学のベンチマークに限定されている。他のドメインへの一般化の可能性に関するさらなる研究が必要

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さまざまな教師モデルの選択とマージ戦略の最適化研究が必要

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MoTの計算コストとメモリ効率の追加分析が必要

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