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20182026
most citedClass-Incremental Learning: A Survey

283 citations · 545 across the 58 of their papers we have counts for

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38 papers · 1 filter

cs.LG2026

RouteJudge: An Open Platform for Reproducible and Preference-Aware LLM Routing

Guannan Lai, Haoran Hu, Han-Jia Ye

We present RouteJudge, an online pairwise preference evaluation framework for LLM routing systems, with a public platform available at https://routejudge.cn. Different from model-l…

cs.LG2026

TabSwift: An Efficient Tabular Foundation Model with Row-Wise Attention

Si-Yang Liu, Han-Jia Ye

Tabular foundation models, exemplified by TabPFN, perform prediction via in-context learning, inferring test labels directly from labeled training examples. They have demonstrated…

cs.LG2026

From Sampled Outcomes to Capability Distributions: Rethinking Supervision for LLM Routing

Guannan Lai, Haoran Hu, Long Chen +2

Existing LLM routing methods often construct supervision from a single sampled response for each query--model pair. Because LLM generation is stochastic, however, such an observati…

cs.LG2026

SAME: Stabilized Mixture-of-Experts for Multimodal Continual Instruction Tuning

Zhen-Hao Xie, Jun-Tao Tang, Yu-Cheng Shi +3

Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, but real-world deployment requires them to continually expand their capabilities, ma…

cs.LG2025

One-Embedding-Fits-All: Efficient Zero-Shot Time Series Forecasting by a Model Zoo

Hao-Nan Shi, Ting-Ji Huang, Lu Han +2

The proliferation of Time Series Foundation Models (TSFMs) has significantly advanced zero-shot forecasting, enabling predictions for unseen time series without task-specific fine-…

cs.LG2025

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts

Lan Li, Da-Wei Zhou, Han-Jia Ye +1

Domain-Incremental Learning (DIL) focuses on continual learning in non-stationary environments, requiring models to adjust to evolving domains while preserving historical knowledge…