Mixture-Of-Experts

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Learning to Trade Like an Expert: Cognitive Fine-Tuning for Stable Financial Reasoning in Language Models
Apr 18, 2026Yuchen Pan, Soung Chang LiewFrontier Models Multiple-Choice Questions
Application of a Mixture of Experts-based Foundation Model to the GlueX DIRC Detector
Apr 17, 2026Cristiano Fanelli, James Giroux, Cole Granger +1Wireless Foundation Models Mixture-of-Experts Models
AstroVLM: Expert Multi-agent Collaborative Reasoning for Astronomical Imaging Quality Diagnosis
Apr 17, 2026Yaohui Han, Tianshuo Wang, Zixi Zhao +6Vision-Language Multi-Agent Reasoning
FineSteer: A Unified Framework for Fine-Grained Inference-Time Steering in Large Language Models
Apr 16, 2026Zixuan Weng, Jinghuai Zhang, Kunlin Cai +3Inference-Time Control Mixture-of-Experts Models
Pangu-ACE: Adaptive Cascaded Experts for Educational Response Generation on EduBench
Apr 16, 2026Dinghao Li, Wenlong Zhou, Zhimin Chen +5Tutors Multi-Hop Question Answering Benchmarks
ELMoE-3D: Leveraging Intrinsic Elasticity of MoE for Hybrid-Bonding-Enabled Self-Speculative Decoding in On-Premises Serving
Apr 16, 2026Yuseon Choi, Jingu Lee, Jungjun Oh +5Mixture-of-Experts Models Speculative Decoding
Geometric Metrics for MoE Specialization: From Fisher Information to Early Failure Detection
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Symbiotic-MoE: Unlocking the Synergy between Generation and Understanding
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Unveiling Language Routing Isolation in Multilingual MoE Models for Interpretable Subnetwork Adaptation
Apr 4, 2026Kening Zheng, Wei-Chieh Huang, Jiahao Huo +9Mixture-of-Experts Models Multilingual Language Models
How Annotation Trains Annotators: Competence Development in Social Influence Recognition
Apr 3, 2026Maciej Markiewicz, Beata Bajcar, Wiktoria Mieleszczenko-Kowszewicz +8Human Annotators Human Annotations
Council Mode: A Heterogeneous Multi-Agent Consensus Framework for Reducing LLM Hallucination and Bias
Apr 3, 2026Shuai Wu, Xue Li, Yanna Feng +3Large Language Model Hallucination Mixture-of-Experts Models
StanceMoE: Mixture-of-Experts Architecture for Stance Detection
Apr 1, 2026Abdullah Al Shafi, Md. Milon Islam, Sk. Imran Hossain +1Tweet-Level Stance Detection Sentence-Level Classification
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ExFusion: Efficient Transformer Training via Multi-Experts Fusion
Mar 30, 2026Jiacheng Ruan, Daize Dong, Xiaoye Qu +5Mixture-of-Experts Models Transformer Architectures
Modality-Guided Mixture of Structured Experts with Entropy-Triggered Routing for Multimodal Recommendation
Feb 24, 2026Ji Dai, Quan Fang, DeSheng CaiMultimodal Recommendation Recommendation
A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs
Feb 23, 2026Zijie Liu, Jie Peng, Jinhao Duan +7Load Balancing Mixture-Of-Expert Inference
OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale
Feb 5, 2026Jingze Shi, Zhangyang Peng, Yizhang Zhu +3Mixture-Of-Expert Inference Mixture-of-Experts Models
L2R: Low-Rank and Lipschitz-Controlled Routing for Mixture-of-Experts
Jan 29, 2026Minghao Yang, Ren Togo, Guang Li +2Mixture-Of-Expert Language Models Adaptive Routing
TRACE: Trajectory Recovery for Continuous Mechanism Evolution in Causal Representation Learning
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ButterflyMoE: Compression-Scalable Ternary Experts via Structured Butterfly Orbits
Jan 20, 2026Aryan KarmoreExperts Expert Demonstrations
Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models
Jan 12, 2026Xin Cheng, Rui Tian, Wangding Zeng +18Sparse Expert Activation Mixture-of-Experts Models
DR-LoRA: Dynamic Rank LoRA for Fine-Tuning Mixture-of-Experts Models
Jan 8, 2026Guanzhi Deng, Bo Li, Ronghao Chen +5Gradient-Free Rank Allocator Mixture-of-Experts Models
HiMoE-VLA: Hierarchical Mixture-of-Experts for Generalist Vision-Language-Action Policies
Dec 5, 2025Zhiying Du, Bei Liu, Yaobo Liang +7Mixture-of-Experts Models Iterative Co-Training
Mixtures of SubExperts for Large Language Continual Learning
Nov 9, 2025Haeyong Kang, Hee Suk Yoon, Dahua Feng +1Mixture-Of-Expert Language Models Efficient Continual Learning
PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference
Nov 6, 2025Yushu Zhao, Zheng Wang, Minjia ZhangMixture-Of-Expert Inference Mixture-of-Experts Models
CXRAgent: Director-Orchestrated Multi-Stage Reasoning for Chest X-Ray Interpretation
Oct 24, 2025Jinhui Lou, Yan Yang, Zhou Yu +4Chest X-Ray Xai
Variational Mixture of Graph Neural Experts for Alzheimer's Disease Recognition across Frequency Bands in EEG Brain Networks
Oct 13, 2025Jun-En Ding, Anna Zilverstand, Shihao Yang +2Alzheimer Human Brain Networks
MM-Nav: Multi-View VLA Model for Robust Visual Navigation via Multi-Expert Learning
Oct 3, 2025Tianyu Xu, Jiawei Chen, Jiazhao Zhang +5Vision-Language Navigation Object Goal Navigation
SHMoAReg: Spark Deformable Image Registration via Spatial Heterogeneous Mixture of Experts and Attention Heads
Sep 24, 2025Yuxi Zheng, Jianhui Feng, Tianran Li +2Image Registration Popular Deformable Convolutions
Expert Preference-based Evaluation of Automated Related Work Generation
Aug 11, 2025Furkan Şahinuç, Subhabrata Dutta, Iryna GurevychHuman Grounded Evaluation Scientific Discovery
Macro Graph of Experts for Billion-Scale Multi-Task Recommendation
Jun 12, 2025Hongyu Yao, Zijin Hong, Hao Chen +6Multimodal Recommendation Large-Scale Recommendation Systems
"Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions
Jun 11, 2025Kellie Yu Hui Sim, Roy Ka-Wei Lee, Kenny Tsu Wei ChooMental Health Support Peers
Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets
May 27, 2025Alexander Jaus, Zdravko Marinov, Constantin Seibold +4Medical Imaging Datasets Pseudo-Labeling
MEGO: Learning Mixture-of-Experts for General-Purpose Binary Optimization
May 29, 2024Shengcai Liu, Zhiyuan Wang, Yew-Soon Ong +2Black-Box Optimization Combinatorial Optimization
"You are an expert annotator": Automatic Best-Worst-Scaling Annotations for Emotion Intensity Modeling
Mar 26, 2024Christopher Bagdon, Prathamesh Karmalker, Harsha Gurulingappa +1Human Annotators Annotations
Retrieval of Scientific and Technological Resources for Experts and Scholars
Apr 13, 2022Suyu Ouyang, Yingxia Shao, Ang LiScientific Discovery Experts
Regularized Estimation and Feature Selection in Mixtures of Generalized Linear Experts
Jul 14, 2019Thin Nguyen-Van, Faicel Chamroukhi, Ha Hoang Van +1Mixture-of-Experts Models Lasso
DeepResearch Bench II: Diagnosing Deep Research Agents via Rubrics from Expert Reports
Date pendingRuizhe Li, Mingxuan Du, Benfeng Xu +3Deep Research Agentic Benchmarks
Dynamic Expert Quantization for Scalable Mixture-of-Experts Inference
Date pendingKexin Chu, Dawei Xiang, Zixu Shen +3Mixture-Of-Expert Inference Mixed-Precision Quantization
SMELT: Scaling Laws for Compute-Matched MoE Looped Transformers
Date pendingShaowen Wang, Ge Zhang, Kairong Luo +6Transformer Layers Transformer Architectures
Cache-Aware Joint Router Adaptation for Memory-Efficient MoE Inference
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Multi-Modal Time Series Prediction via Mixture of Modulated Experts
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CMoE: Contrastive Mixture of Experts for Motion Control and Terrain Adaptation of Humanoid Robots
Date pendingShihao Ma, Hongjin Chen, Zijun Xu +6Long-Tail Humanoid Whole-Body Control Terrain
FluxMoE: Decoupling Expert Residency for High-Performance MoE Serving
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Apr 18, 2026 · cs.LGJ/K move · Enter open · S save

Learning to Trade Like an Expert: Cognitive Fine-Tuning for Stable Financial Reasoning in Language Models

Yuchen Pan, Soung Chang Liew

Department of Information Engineering, The Chinese University of Hong Kong

Recent deployments of large language models (LLMs) as autonomous trading agents raise questions about whether financial decision-making competence generalizes beyond specific market patterns and how it should be trained and evaluated in noisy markets lacking ground truth. We propose a structured framework for training and evaluating such models. Central to our approach is a curated, multiple-choice question (MCQ) dataset derived from classic textbooks and historical markets, verified by an AI committee, enriched with structured reasoning traces, and augmented to reduce shortcut learning. To evaluate whether performance on isolated MCQs generalizes to real-world trading, we introduce a two-stage protocol combining test-set evaluation with an MCQ-based chronological trading simulation. Extensive evaluations across market regimes provide statistically robust evidence that open models trained with our framework exhibit competitive, risk-aware behavior over time, outperform open-source baselines, and approach frontier-model performance at smaller scale. We release the dataset and evaluation framework to support further research.

Frontier Models Multiple-Choice Questions Experts

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